diff --git a/index.html b/index.html
index 38cda1a87..9f1e0edd5 100644
--- a/index.html
+++ b/index.html
@@ -184,18 +184,18 @@ <h2 data-anchor-id="sec-packages">Explore</h2>
 <style>
 .grid-sizer, .grid-item { 
   width: 100%;
-  margin: 5px auto;
+  margin: 1px auto;
 }
 @media only screen and (min-width: 600px) {
   .grid-sizer, .grid-item { 
-    width: 47%;
-    margin: 5px auto;
+    width: calc(50% - 5px);
+    margin: 1px auto;
   }
 }
 @media only screen and (min-width: 768px) {
   .grid-sizer, .grid-item { 
-    width: 30%;
-    margin: 5px auto;
+    width: calc(33% - 5px);
+    margin: 1px auto;
   }
 }
 </style>
@@ -452,7 +452,8 @@ <h5 class="card-subtitle mb-2 text-muted">Analysis and Visualization of Archaeol
   percentPosition: true,
   masonry: {
     // use outer width of grid-sizer for columnWidth
-    columnWidth: '.grid-sizer'
+    columnWidth: '.grid-sizer',
+    gutter: 1
   }
 });
 
@@ -474,7 +475,6 @@ <h5 class="card-subtitle mb-2 text-muted">Analysis and Visualization of Archaeol
 </section>
 
 </main> <!-- /main -->
-<div style="display: inline; text-align: center;"><a href="https://www.u-bordeaux-montainge.fr"><img src="https://www.tesselle.org/images/UBM.jpg" class="logo-institution"></a><a href="https://www.cnrs.fr"><img src="https://www.tesselle.org/images/CNRS.png" class="logo-institution"></a></div>
 <script id="quarto-html-after-body" type="application/javascript">
 window.document.addEventListener("DOMContentLoaded", function (event) {
   const toggleBodyColorMode = (bsSheetEl) => {
@@ -707,13 +707,7 @@ <h5 class="card-subtitle mb-2 text-muted">Analysis and Visualization of Archaeol
     <div class="nav-footer-left">
       &nbsp;
     </div>   
-    <div class="nav-footer-center">
-      <ul class="footer-items list-unstyled">
-    <li class="nav-item">
- The tesselle project is supported by the <a href="https://mshs.univ-toulouse.fr/" target="_blank"><abbr title="Centre de Recherche et d'Études pour l'Art Préhistorique">CREAP</abbr></a>, <a href="https://www.archeosciences-bordeaux.fr/" target="_blank">Archéosciences Bordeaux</a> and the <a href="https://www.huma-num.fr/" target="_blank"><abbr title="Très Grande Infrastructure de Recherche">TGIR</abbr> Huma-Num</a>.
-  </li>  
-</ul>
-    </div>
+    <div class="nav-footer-center"><a href="https://www.u-bordeaux-montainge.fr"><img src="https://www.tesselle.org/images/UBM.jpg" class="logo-institution"></a><a href="https://www.cnrs.fr"><img src="https://www.tesselle.org/images/CNRS.png" class="logo-institution"></a> <br>The tesselle project is supported by <a href="https://www.archeosciences-bordeaux.fr/" target="_blank">Archéosciences Bordeaux</a>, the <a href="https://mshs.univ-toulouse.fr/" target="_blank"><abbr title="Centre de Recherche et d'Études pour l'Art Préhistorique">CREAP</abbr></a> and the <a href="https://www.huma-num.fr/" target="_blank"><abbr title="Très Grande Infrastructure de Recherche">TGIR</abbr> Huma-Num</a>.</div>
     <div class="nav-footer-right">
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diff --git a/packages.json b/packages.json
index 1ef116476..2bdfa0a82 100644
--- a/packages.json
+++ b/packages.json
@@ -1 +1 @@
-[{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Tools for Cleaning Rectangular Data","text":"dependency-free collection simple functions cleaning rectangular data. package allows detect, count replace values discard rows/columns using predicate function. addition, provides tools check conditions return informative error messages.","code":"To cite arkhe in publications use:    Frerebeau N (2023). _arkhe: Tools for Cleaning Rectangular Data_.   Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.3526659 <https://doi.org/10.5281/zenodo.3526659>,   R package version 1.3.0, <https://packages.tesselle.org/arkhe/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{arkhe: Tools for Cleaning Rectangular Data}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.3.0},     url = {https://packages.tesselle.org/arkhe/},     doi = {10.5281/zenodo.3526659},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Tools for Cleaning Rectangular Data","text":"can install released version arkhe CRAN : development version GitHub :","code":"install.packages(\"arkhe\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/arkhe\")","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Tools for Cleaning Rectangular Data","text":"","code":"## Load the package library(arkhe)  ## Create a matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    5    8   NA    3    5 #> [2,]    9    7    8    4    2 #> [3,]    4   10    7   NA    2 #> [4,]   NA    5    5    4    3 #> [5,]    7    9    9    8    1  ## Count missing values in rows count(X, f = is.na, margin = 1) #> [1] 1 0 1 1 0 ## Count non-missing values in columns count(X, f = is.na, margin = 2, negate = TRUE) #> V1 V2 V3 V4 V5  #>  4  5  4  4  5  ## Find row with NA detect(X, f = is.na, margin = 1) #> [1]  TRUE FALSE  TRUE  TRUE FALSE ## Find column without any NA detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE) #>    V1    V2    V3    V4    V5  #> FALSE  TRUE FALSE FALSE  TRUE  ## Remove row with any NA discard(X, f = is.na, margin = 1, all = FALSE) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    9    7    8    4    2 #> [2,]    7    9    9    8    1 ## Remove column with any NA discard(X, f = is.na, margin = 2, all = FALSE) #>      [,1] [,2] #> [1,]    8    5 #> [2,]    7    2 #> [3,]   10    2 #> [4,]    5    3 #> [5,]    9    1  ## Replace NA with zeros replace_NA(X, value = 0) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    5    8    0    3    5 #> [2,]    9    7    8    4    2 #> [3,]    4   10    7    0    2 #> [4,]    0    5    5    4    3 #> [5,]    7    9    9    8    1","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Tools for Cleaning Rectangular Data","text":"Please note arkhe project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":[],"package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Row Names to an Explicit Column \u2014 append","title":"Convert Row Names to an Explicit Column \u2014 append","text":"Convert Row Names Explicit Column","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Row Names to an Explicit Column \u2014 append","text":"","code":"append_rownames(x, ...)  # S4 method for data.frame append_rownames(x, after = 0, remove = TRUE, var = \"rownames\")","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Row Names to an Explicit Column \u2014 append","text":"x data.frame. ... Currently used. length-one numeric vector specifying subscript, row names appended. remove logical scalar: row names removed? var character string giving name column use row names.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Row Names to an Explicit Column \u2014 append","text":"data.frame.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert Row Names to an Explicit Column \u2014 append","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert Row Names to an Explicit Column \u2014 append","text":"","code":"X <- data.frame(   x = 1:5,   y = 6:10,   z = LETTERS[1:5] )  ## Assign column to row names (Y <- assign_rownames(X, 3)) #>   x  y #> A 1  6 #> B 2  7 #> C 3  8 #> D 4  9 #> E 5 10  ## Append row names to data.frame (Z <- append_rownames(Y)) #>   rownames x  y #> 1        A 1  6 #> 2        B 2  7 #> 3        C 3  8 #> 4        D 4  9 #> 5        E 5 10","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/arkhe-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in arkhe \u2014 arkhe-deprecated","title":"Deprecated Functions in arkhe \u2014 arkhe-deprecated","text":"functions still work removed (defunct) next version.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/arkhe-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions in arkhe \u2014 arkhe-deprecated","text":"","code":"wide_to_long(from, ...)  to_long(from, ...)  # S4 method for matrix wide_to_long(from, factor = FALSE, reverse = FALSE)  # S4 method for matrix to_long(from, factor = FALSE, reverse = FALSE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/arkhe-package.html","id":null,"dir":"Reference","previous_headings":"","what":"arkhe: Tools for Cleaning Rectangular Data \u2014 arkhe-package","title":"arkhe: Tools for Cleaning Rectangular Data \u2014 arkhe-package","text":"dependency-free collection simple functions cleaning rectangular data. package allows detect, count replace values discard rows/columns using predicate function. addition, provides tools check conditions return informative error messages.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/arkhe-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"arkhe: Tools for Cleaning Rectangular Data \u2014 arkhe-package","text":"arkhe uses following options() configure behavior: arkhe.verbose: logical scalar. R report extra information progress? Defaults TRUE.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/arkhe-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"arkhe: Tools for Cleaning Rectangular Data \u2014 arkhe-package","text":"Full list authors contributors (alphabetic order): Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"Assign Specific Row/Column Column/Row Names","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"","code":"assign_colnames(x, ...)  assign_rownames(x, ...)  # S4 method for data.frame assign_rownames(x, column, remove = TRUE)  # S4 method for data.frame assign_colnames(x, row, remove = TRUE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"x data.frame. ... Currently used. column length-one numeric vector specifying column number become row names. remove logical scalar: specified row/column removed making column/row names? row length-one numeric vector specifying row number become column names.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"data.frame.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"","code":"X <- data.frame(   x = 1:5,   y = 6:10,   z = LETTERS[1:5] )  ## Assign column to row names (Y <- assign_rownames(X, 3)) #>   x  y #> A 1  6 #> B 2  7 #> C 3  8 #> D 4  9 #> E 5 10  ## Append row names to data.frame (Z <- append_rownames(Y)) #>   rownames x  y #> 1        A 1  6 #> 2        B 2  7 #> 3        C 3  8 #> 4        D 4  9 #> 5        E 5 10","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Estimation \u2014 bootstrap","title":"Bootstrap Estimation \u2014 bootstrap","text":"Samples randomly elements object replacement.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Estimation \u2014 bootstrap","text":"","code":"bootstrap(object, ...)  # S4 method for numeric bootstrap(object, do, n, ..., f = NULL)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Estimation \u2014 bootstrap","text":"object numeric vector. ... Extra arguments passed . function takes object argument returns single numeric value. n non-negative integer giving number bootstrap replications. f function takes single numeric vector (result ) argument.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Estimation \u2014 bootstrap","text":"f NULL (default), bootstrap() returns named numeric vector following elements: original observed value applied object. mean bootstrap estimate mean . bias bootstrap estimate bias . error bootstrap estimate standard error . f function, bootstrap() returns result f applied n values .","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Estimation \u2014 bootstrap","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Estimation \u2014 bootstrap","text":"","code":"x <- rnorm(20)  ## Bootstrap bootstrap(x, do = mean, n = 100) #>    original        mean        bias       error  #> -0.23001183 -0.26088732 -0.03087549  0.19544745   ## Estimate the 25th and 95th percentiles quant <- function(x) { quantile(x, probs = c(0.25, 0.75)) } bootstrap(x, n = 100, do = mean, f = quant) #>        25%        75%  #> -0.3575141 -0.1015618   ## Get the n bootstrap values bootstrap(x, n = 100, do = mean, f = function(x) { x }) #>   [1] -0.354575156 -0.264014359 -0.024722116  0.168383889 -0.308479383 #>   [6]  0.042123894 -0.631067882 -0.103311499 -0.217030853 -0.180843364 #>  [11] -0.377664859 -0.255918944  0.260098734 -0.039807122 -0.273346343 #>  [16] -0.097273971 -0.421726282 -0.339250807 -0.385834467 -0.082725113 #>  [21] -0.278828961 -0.314183617  0.111879988  0.259573743 -0.182563677 #>  [26] -0.222178437 -0.168244579 -0.319917168 -0.652910481 -0.626622912 #>  [31] -0.149500332 -0.114149030 -0.211087995 -0.035279463 -0.481243393 #>  [36] -0.228694443 -0.296699910 -0.347472668 -0.595869659 -0.123912074 #>  [41] -0.314146171 -0.242591941 -0.092909644 -0.450002215 -0.291444799 #>  [46] -0.246667862 -0.240226038 -0.349990699 -0.345395193 -0.116084354 #>  [51] -0.235571530 -0.210884887 -0.610726433  0.018464397 -0.018265597 #>  [56] -0.245874130 -0.156736947 -0.039486500  0.043348922 -0.495482369 #>  [61] -0.313652243 -0.244748120  0.063470300 -0.117959964 -0.052234695 #>  [66] -0.254617069 -0.280670352 -0.054931345 -0.484812617  0.035113663 #>  [71] -0.061796337 -0.016094557 -0.272161888 -0.528599567 -0.003423236 #>  [76] -0.593896436 -0.285145538 -0.399949034 -0.072200926 -0.110624087 #>  [81] -0.200153483  0.007328726 -0.349747032 -0.371607923 -0.233177528 #>  [86]  0.534766177 -0.052641721 -0.171065756 -0.090688354  0.105562894 #>  [91]  0.043238954 -0.562153775 -0.058901428 -0.349603978  0.004644821 #>  [96] -0.106330578 -0.328705521 -0.495705109 -0.295970657 -0.839429360  ## Jackknife jackknife(x, do = mean) # Sample mean #>   original       mean       bias      error  #> -0.2300118 -0.2300118  0.0000000  0.2154961   ## Get the leave-one-out values instead of summary jackknife(x, do = mean, f = function(x) { x }) #>  [1] -0.2260393 -0.1903113 -0.1957656 -0.2753305 -0.2263948 -0.2667864 #>  [7] -0.2142052 -0.2851663 -0.2555698 -0.1419977 -0.1139680 -0.2245960 #> [13] -0.1886281 -0.2639238 -0.2216209 -0.2753058 -0.1883000 -0.3161935 #> [19] -0.2863064 -0.2438270","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-attribute.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Object Attributes \u2014 check-attribute","title":"Check Object Attributes \u2014 check-attribute","text":"Check Object Attributes","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-attribute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Object Attributes \u2014 check-attribute","text":"","code":"assert_empty(x)  assert_filled(x)  assert_length(x, expected, empty = FALSE)  assert_lengths(x, expected)  assert_dimensions(x, expected)  assert_names(x, expected)  assert_dimnames(x, expected)  assert_rownames(x, expected)  assert_colnames(x, expected)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-attribute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Object Attributes \u2014 check-attribute","text":"x object checked. expected appropriate expected value. empty logical scalar: empty objects ignored?","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-attribute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Object Attributes \u2014 check-attribute","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-attribute.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Object Attributes \u2014 check-attribute","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-data.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Data \u2014 check-data","title":"Check Data \u2014 check-data","text":"assert_missing() assert_infinite() check object contains missing (NA, NaN) infinite (Inf) value. assert_unique() checks object contains duplicated elements.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Data \u2014 check-data","text":"","code":"assert_missing(x)  assert_infinite(x)  assert_unique(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Data \u2014 check-data","text":"x object checked.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Data \u2014 check-data","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Data \u2014 check-data","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Matrix \u2014 check-matrix","title":"Check Matrix \u2014 check-matrix","text":"Check Matrix","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Matrix \u2014 check-matrix","text":"","code":"assert_symmetric(x)  assert_square(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Matrix \u2014 check-matrix","text":"x matrix checked.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Matrix \u2014 check-matrix","text":"Throw error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-matrix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Matrix \u2014 check-matrix","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-comparison.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Numeric Relations \u2014 check-numeric-comparison","title":"Check Numeric Relations \u2014 check-numeric-comparison","text":"Check Numeric Relations","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-comparison.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Numeric Relations \u2014 check-numeric-comparison","text":"","code":"assert_lower(x, y, ...)  assert_greater(x, y, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-comparison.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Numeric Relations \u2014 check-numeric-comparison","text":"x, y numeric object checked. ... Extra parameters passed internal methods.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-comparison.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Numeric Relations \u2014 check-numeric-comparison","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-comparison.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Numeric Relations \u2014 check-numeric-comparison","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-trend.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Numeric Trend \u2014 check-numeric-trend","title":"Check Numeric Trend \u2014 check-numeric-trend","text":"Check Numeric Trend","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-trend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Numeric Trend \u2014 check-numeric-trend","text":"","code":"assert_constant(x, ...)  assert_decreasing(x, ...)  assert_increasing(x, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-trend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Numeric Trend \u2014 check-numeric-trend","text":"x numeric object checked. ... Extra parameters passed internal methods.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-trend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Numeric Trend \u2014 check-numeric-trend","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-trend.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Numeric Trend \u2014 check-numeric-trend","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Numeric Values \u2014 check-numeric","title":"Check Numeric Values \u2014 check-numeric","text":"Check Numeric Values","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Numeric Values \u2014 check-numeric","text":"","code":"assert_count(x, ...)  assert_whole(x, ...)  assert_positive(x, ...)  assert_negative(x, ...)  assert_odd(x, ...)  assert_even(x, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Numeric Values \u2014 check-numeric","text":"x numeric object checked. ... Extra parameters passed internal methods.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Numeric Values \u2014 check-numeric","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Numeric Values \u2014 check-numeric","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-type.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Data Types \u2014 check-type","title":"Check Data Types \u2014 check-type","text":"Check Data Types","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Data Types \u2014 check-type","text":"","code":"assert_type(x, expected)  assert_scalar(x, expected)  assert_function(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Data Types \u2014 check-type","text":"x object checked. expected character string specifying expected type. must one \"list\", \"atomic\", \"vector\", \"numeric\", \"integer\", \"double\", \"character\" \"logical\".","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-type.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Data Types \u2014 check-type","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-type.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Data Types \u2014 check-type","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class Diagnostic \u2014 check_class","title":"Class Diagnostic \u2014 check_class","text":"Class Diagnostic","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Class Diagnostic \u2014 check_class","text":"","code":"check_class(object, conditions)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class Diagnostic \u2014 check_class","text":"object object error messages related. conditions list condition messages.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Class Diagnostic \u2014 check_class","text":"Throw error conditions non-zero length, invisibly returns TRUE .","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check_class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Class Diagnostic \u2014 check_class","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Empty Rows/Columns \u2014 compact","title":"Remove Empty Rows/Columns \u2014 compact","text":"Removes empty rows/columns array-like object using predicate function.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Empty Rows/Columns \u2014 compact","text":"","code":"compact(x, ...)  compact_cols(x, ...)  compact_rows(x, ...)  # S4 method for ANY compact(x, margin = 1)  # S4 method for ANY compact_cols(x)  # S4 method for ANY compact_rows(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Empty Rows/Columns \u2014 compact","text":"x R object (matrix data.frame). ... Currently used. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns).","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Remove Empty Rows/Columns \u2014 compact","text":"row/column empty contains NA, zeros (type numeric) zero length character strings (type character).","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Remove Empty Rows/Columns \u2014 compact","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remove Empty Rows/Columns \u2014 compact","text":"","code":"## Create a data.frame X <- data.frame(A = 0, B = 1:5, C = 6, D = \"\", F = letters[1:5]) X #>   A B C D F #> 1 0 1 6   a #> 2 0 2 6   b #> 3 0 3 6   c #> 4 0 4 6   d #> 5 0 5 6   e  ## Remove empty columns compact(X, margin = 2) #>   B C F #> 1 1 6 a #> 2 2 6 b #> 3 3 6 c #> 4 4 6 d #> 5 5 6 e","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/conditions.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditions \u2014 conditions","title":"Conditions \u2014 conditions","text":"throw_error() stops execution current expression executes error action. throw_warning() generates warning message. catch_conditions() catch_message() handles unusual conditions.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/conditions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditions \u2014 conditions","text":"","code":"throw_error(.subclass, message, call = NULL, ...)  throw_warning(.subclass, message, call = NULL, ...)  catch_message(expr)  catch_conditions(expr)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/conditions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditions \u2014 conditions","text":".subclass character string specifying class message returned. message character string specifying message returned. call call. ... Extra arguments. expr expression evaluated.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/conditions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Conditions \u2014 conditions","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"Computes Wald interval proportion desired level significance.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"","code":"confidence_binomial(object, ...)  # S4 method for numeric confidence_binomial(   object,   n,   level = 0.95,   method = \"wald\",   corrected = FALSE )","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"object numeric vector giving number success. ... Currently used. n length-one numeric vector giving number trials. level length-one numeric vector giving confidence level. Must single number \\(0\\) \\(1\\). method character string specifying method used. unambiguous substring can used. corrected logical scalar: continuity correction used? used method \"wald\".","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"length-two numeric vector giving lower upper confidence limits.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"","code":"## Confidence interval for a mean x <- seq(from = -4, to = 4, by = 0.01) y <- dnorm(x)  confidence_mean(y, type = \"student\") #>     lower     upper  #> 0.1151118 0.1345606  confidence_mean(y, type = \"normal\") #>     lower     upper  #> 0.1151265 0.1345459   ## Confidence interval for a propotion confidence_binomial(118, n = 236) #>     lower     upper  #> 0.4362086 0.5637914   x <- c(35, 74, 22, 69) confidence_multinomial(x) #>           lower     upper #> [1,] 0.12234021 0.2276598 #> [2,] 0.30308797 0.4369120 #> [3,] 0.06663649 0.1533635 #> [4,] 0.27911853 0.4108815","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval for a Mean \u2014 confidence_mean","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"Computes confidence interval mean desired level significance.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"","code":"confidence_mean(object, ...)  # S4 method for numeric confidence_mean(object, level = 0.95, type = c(\"student\", \"normal\"))","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"object numeric vector. ... Currently used. level length-one numeric vector giving confidence level. Must single number \\(0\\) \\(1\\). type character string giving type confidence interval returned. must one \"student\" (default) \"normal\". unambiguous substring can given.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"length-two numeric vector giving lower upper confidence limits.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"","code":"## Confidence interval for a mean x <- seq(from = -4, to = 4, by = 0.01) y <- dnorm(x)  confidence_mean(y, type = \"student\") #>     lower     upper  #> 0.1151118 0.1345606  confidence_mean(y, type = \"normal\") #>     lower     upper  #> 0.1151265 0.1345459   ## Confidence interval for a propotion confidence_binomial(118, n = 236) #>     lower     upper  #> 0.4362086 0.5637914   x <- c(35, 74, 22, 69) confidence_multinomial(x) #>           lower     upper #> [1,] 0.12234021 0.2276598 #> [2,] 0.30308797 0.4369120 #> [3,] 0.06663649 0.1533635 #> [4,] 0.27911853 0.4108815","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"Computes Wald interval proportion desired level significance.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"","code":"confidence_multinomial(object, ...)  # S4 method for numeric confidence_multinomial(   object,   level = 0.95,   method = \"wald\",   corrected = FALSE )","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"object numeric vector positive integers giving number occurrences class. ... Currently used. level length-one numeric vector giving confidence level. Must single number \\(0\\) \\(1\\). method character string specifying method used. unambiguous substring can used. corrected logical scalar: continuity correction used? used method \"wald\".","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"two column numeric  matrix giving lower upper confidence limits.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"","code":"## Confidence interval for a mean x <- seq(from = -4, to = 4, by = 0.01) y <- dnorm(x)  confidence_mean(y, type = \"student\") #>     lower     upper  #> 0.1151118 0.1345606  confidence_mean(y, type = \"normal\") #>     lower     upper  #> 0.1151265 0.1345459   ## Confidence interval for a propotion confidence_binomial(118, n = 236) #>     lower     upper  #> 0.4362086 0.5637914   x <- c(35, 74, 22, 69) confidence_multinomial(x) #>           lower     upper #> [1,] 0.12234021 0.2276598 #> [2,] 0.30308797 0.4369120 #> [3,] 0.06663649 0.1533635 #> [4,] 0.27911853 0.4108815","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Values Using a Predicate \u2014 count","title":"Count Values Using a Predicate \u2014 count","text":"Counts values rows/columns using predicate function.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Values Using a Predicate \u2014 count","text":"","code":"count(x, ...)  # S4 method for data.frame count(x, f, margin = 1, negate = FALSE, na.rm = FALSE, ...)  # S4 method for matrix count(x, f, margin = 1, negate = FALSE, na.rm = FALSE, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Values Using a Predicate \u2014 count","text":"x R object (matrix data.frame). ... arguments passed f. f predicate function. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). negate logical scalar: negation f used instead f? na.rm logical scalar: NA values stripped computation proceeds?","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Values Using a Predicate \u2014 count","text":"numeric vector.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Count Values Using a Predicate \u2014 count","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Values Using a Predicate \u2014 count","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    3    2    6    6    7 #> [2,]    9    4    4   10    6 #> [3,]   10   10   10   NA   NA #> [4,]    1    8    4    8    7 #> [5,]    4    2   NA    9    5  ## Count missing values in rows count(X, f = is.na, margin = 1) #> [1] 0 0 2 0 1 ## Count non-missing values in columns count(X, f = is.na, margin = 2, negate = TRUE) #> V1 V2 V3 V4 V5  #>  5  5  4  4  4","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":null,"dir":"Reference","previous_headings":"","what":"Find Rows/Columns Using a Predicate \u2014 detect","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"Finds rows/columns array-like object using predicate function.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"","code":"detect(x, ...)  # S4 method for ANY detect(x, f, margin = 1, negate = FALSE, all = FALSE, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"x R object (matrix data.frame). ... arguments passed f. f predicate function. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). negate logical scalar: negation f used instead f? logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"logical vector.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    6   10    1    4    3 #> [2,]    1    7    1    9    3 #> [3,]    1    1    3    9    1 #> [4,]    7    8    5    1    5 #> [5,]   NA   NA    4   NA    9  ## Find row with NA detect(X, f = is.na, margin = 1) #> [1] FALSE FALSE FALSE FALSE  TRUE ## Find column without any NA detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE) #>    V1    V2    V3    V4    V5  #> FALSE FALSE  TRUE FALSE  TRUE","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/discard.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Rows/Columns Using a Predicate \u2014 discard","title":"Remove Rows/Columns Using a Predicate \u2014 discard","text":"Removes rows/columns array-like object using predicate function.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/discard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Rows/Columns Using a Predicate \u2014 discard","text":"","code":"discard(x, ...)  discard_cols(x, ...)  discard_rows(x, ...)  # S4 method for ANY discard(   x,   f,   margin = 1,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )  # S4 method for ANY discard_rows(   x,   f,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )  # S4 method for ANY discard_cols(   x,   f,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/discard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Rows/Columns Using a Predicate \u2014 discard","text":"x R object (matrix data.frame). ... arguments passed f. f predicate function. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). negate logical scalar: negation f used instead f? logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. verbose logical scalar: R report extra information progress?","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/discard.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Remove Rows/Columns Using a Predicate \u2014 discard","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/discard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remove Rows/Columns Using a Predicate \u2014 discard","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    3    6   NA    9    3 #> [2,]    2    5    5    8   NA #> [3,]    7    3    4    4    9 #> [4,]    1    4    5   NA    2 #> [5,]    8    5    1   10    5  ## Remove row with any NA discard(X, f = is.na, margin = 1, all = FALSE) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    7    3    4    4    9 #> [2,]    8    5    1   10    5 ## Remove column with any NA discard(X, f = is.na, margin = 2, all = FALSE) #>      [,1] [,2] #> [1,]    3    6 #> [2,]    2    5 #> [3,]    7    3 #> [4,]    1    4 #> [5,]    8    5","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for Working With Empty String \u2014 empty","title":"Tools for Working With Empty String \u2014 empty","text":"remove_empty() remove rows/columns contain empty strings. replace_empty() replaces empty strings.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for Working With Empty String \u2014 empty","text":"","code":"remove_empty(x, ...)  replace_empty(x, ...)  # S4 method for ANY remove_empty(x, margin = 1, all = FALSE, na.rm = FALSE)  # S4 method for matrix replace_empty(x, value)  # S4 method for data.frame replace_empty(x, value)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for Working With Empty String \u2014 empty","text":"x R object (matrix data.frame). ... Currently used. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. na.rm logical scalar: NA values stripped computation proceeds? value possible replacement value.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/empty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tools for Working With Empty String \u2014 empty","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/empty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for Working With Empty String \u2014 empty","text":"","code":"## Create a data matrix X <- matrix(sample(LETTERS, 25, TRUE), nrow = 5, ncol = 5)  ## Add empty string k <- sample(1:25, 3, FALSE) X[k] <- \"\" X #>      [,1] [,2] [,3] [,4] [,5] #> [1,] \"G\"  \"W\"  \"\"   \"\"   \"U\"  #> [2,] \"Y\"  \"X\"  \"U\"  \"V\"  \"N\"  #> [3,] \"X\"  \"X\"  \"I\"  \"E\"  \"D\"  #> [4,] \"T\"  \"Y\"  \"V\"  \"O\"  \"U\"  #> [5,] \"B\"  \"\"   \"A\"  \"Z\"  \"D\"   ## Remove rows with empty strings remove_empty(X, margin = 1) #>      [,1] [,2] [,3] [,4] [,5] #> [1,] \"Y\"  \"X\"  \"U\"  \"V\"  \"N\"  #> [2,] \"X\"  \"X\"  \"I\"  \"E\"  \"D\"  #> [3,] \"T\"  \"Y\"  \"V\"  \"O\"  \"U\"   ## Replace empty strings replace_empty(X, value = \"XXX\") #>      [,1] [,2]  [,3]  [,4]  [,5] #> [1,] \"G\"  \"W\"   \"XXX\" \"XXX\" \"U\"  #> [2,] \"Y\"  \"X\"   \"U\"   \"V\"   \"N\"  #> [3,] \"X\"  \"X\"   \"I\"   \"E\"   \"D\"  #> [4,] \"T\"  \"Y\"   \"V\"   \"O\"   \"U\"  #> [5,] \"B\"  \"XXX\" \"A\"   \"Z\"   \"D\"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/infinite.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for Working With Infinite Values \u2014 infinite","title":"Tools for Working With Infinite Values \u2014 infinite","text":"remove_Inf() remove rows/columns contain infinite values. replace_Inf replaces infinite values values.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/infinite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for Working With Infinite Values \u2014 infinite","text":"","code":"remove_Inf(x, ...)  replace_Inf(x, ...)  # S4 method for ANY remove_Inf(x, margin = 1, all = FALSE)  # S4 method for matrix replace_Inf(x, value = 0)  # S4 method for data.frame replace_Inf(x, value = 0)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/infinite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for Working With Infinite Values \u2014 infinite","text":"x R object (matrix data.frame). ... Currently used. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. value possible replacement value.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/infinite.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tools for Working With Infinite Values \u2014 infinite","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/infinite.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for Working With Infinite Values \u2014 infinite","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add Inf k <- sample(1:25, 3, FALSE) X[k] <- Inf X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    1    3  Inf    4  Inf #> [2,]    8    5    7    9    1 #> [3,]    6    2    5    6    8 #> [4,]    7    7    5  Inf    5 #> [5,]    7    8    4    1    3  ## Remove rows with Inf remove_Inf(X, margin = 1) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    8    5    7    9    1 #> [2,]    6    2    5    6    8 #> [3,]    7    8    4    1    3  ## Replace Inf with zeros replace_Inf(X, value = 0) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    1    3    0    4    0 #> [2,]    8    5    7    9    1 #> [3,]    6    2    5    6    8 #> [4,]    7    7    5    0    5 #> [5,]    7    8    4    1    3","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":null,"dir":"Reference","previous_headings":"","what":"Bayesian Credible Interval \u2014 interval_credible","title":"Bayesian Credible Interval \u2014 interval_credible","text":"Computes shortest credible interval within unobserved parameter value falls particular probability.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bayesian Credible Interval \u2014 interval_credible","text":"","code":"interval_credible(x, ...)  # S4 method for numeric interval_credible(x, level = 0.95)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bayesian Credible Interval \u2014 interval_credible","text":"x numeric vector. ... Currently used. level length-one numeric vector giving confidence level.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bayesian Credible Interval \u2014 interval_credible","text":"three-columns numeric  matrix giving lower upper boundaries credible interval associated probability.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bayesian Credible Interval \u2014 interval_credible","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bayesian Credible Interval \u2014 interval_credible","text":"","code":"## HDR of the Old Faithful eruption times interval_hdr(faithful$eruptions) #>         start      end    p #> [1,] 1.307160 2.837942 0.33 #> [2,] 3.150567 5.295819 0.62","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":null,"dir":"Reference","previous_headings":"","what":"Highest Density Regions \u2014 interval_hdr","title":"Highest Density Regions \u2014 interval_hdr","text":"Highest Density Regions","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Highest Density Regions \u2014 interval_hdr","text":"","code":"interval_hdr(x, y, ...)  # S4 method for numeric,numeric interval_hdr(x, y, level = 0.954)  # S4 method for numeric,missing interval_hdr(x, level = 0.954, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Highest Density Regions \u2014 interval_hdr","text":"x numeric vector giving coordinates points density estimated. y numeric vector giving estimated density values. y missing x numeric vector, density estimates computed x. ... arguments passed stats::density(). level length-one numeric vector giving confidence level.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Highest Density Regions \u2014 interval_hdr","text":"three-columns numeric  matrix giving lower upper boundaries HPD interval associated probabilities.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Highest Density Regions \u2014 interval_hdr","text":"Hyndman, R. J. (1996). Computing graphing highest density regions. American Statistician, 50: 120-126. doi:10.2307/2684423 .","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Highest Density Regions \u2014 interval_hdr","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Highest Density Regions \u2014 interval_hdr","text":"","code":"## HDR of the Old Faithful eruption times interval_hdr(faithful$eruptions) #>         start      end    p #> [1,] 1.307160 2.837942 0.33 #> [2,] 3.150567 5.295819 0.62","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":null,"dir":"Reference","previous_headings":"","what":"Jackknife Estimation \u2014 jackknife","title":"Jackknife Estimation \u2014 jackknife","text":"Jackknife Estimation","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Jackknife Estimation \u2014 jackknife","text":"","code":"jackknife(object, ...)  # S4 method for numeric jackknife(object, do, ..., f = NULL)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Jackknife Estimation \u2014 jackknife","text":"object numeric vector. ... Extra arguments passed . function takes object argument returns single numeric value. f function takes single numeric vector (leave-one-values ) argument.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Jackknife Estimation \u2014 jackknife","text":"f NULL (default), jackknife() returns named numeric vector following elements: original observed value applied object. mean jackknife estimate mean . bias jackknife estimate bias . error jackknife estimate standard error . f function, jackknife() returns result f applied leave-one-values .","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Jackknife Estimation \u2014 jackknife","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Jackknife Estimation \u2014 jackknife","text":"","code":"x <- rnorm(20)  ## Bootstrap bootstrap(x, do = mean, n = 100) #>    original        mean        bias       error  #>  0.14565989  0.13104524 -0.01461464  0.23848682   ## Estimate the 25th and 95th percentiles quant <- function(x) { quantile(x, probs = c(0.25, 0.75)) } bootstrap(x, n = 100, do = mean, f = quant) #>          25%          75%  #> -0.002251734  0.279260509   ## Get the n bootstrap values bootstrap(x, n = 100, do = mean, f = function(x) { x }) #>   [1]  0.43039038  0.20569337 -0.12346231  0.07890700  0.08198185  0.40706136 #>   [7] -0.10356961  0.09922693  0.05686612  0.26156990  0.28905648  0.02424760 #>  [13]  0.08009081  0.30724032  0.66974867  0.44542265  0.02041284 -0.08116079 #>  [19]  0.01480548  0.11409536  0.60173106  0.09687759 -0.12886483 -0.20829643 #>  [25]  0.37881994  0.31910581 -0.05900876  0.07250347  0.33729817 -0.16638088 #>  [31]  0.32932694  0.10187384 -0.19635637  0.67554312  0.12949750  0.05268718 #>  [37] -0.07856544  0.23526846  0.53351332  0.14306191  0.15852229 -0.12736808 #>  [43]  0.07355868  0.03760085 -0.15446830  0.13021126  0.36332861  0.12115010 #>  [49] -0.14216211  0.18096342  0.15610047  0.80843649 -0.07059272  0.52369545 #>  [55]  0.12995564 -0.11061855  0.15039954  0.02361363  0.10957063 -0.13808787 #>  [61]  0.09491213  0.06801922  0.25202480 -0.08957378  0.27567313  0.57898542 #>  [67]  0.10575104  0.39636880  0.21606101  0.18260413 -0.28894382  0.23905615 #>  [73] -0.25685970  0.09339155  0.48478190  0.14519122  0.34178327 -0.11283620 #>  [79]  0.16934627 -0.06703038  0.46523253  0.25913102  0.26237423  0.09554590 #>  [85]  0.28927284  0.29229987  0.19989578  0.01875762  0.07249745  0.04099358 #>  [91]  0.09222536  0.20267609  0.06144836  0.24978342 -0.20200934  0.37718515 #>  [97]  0.34187158  0.37971883 -0.13657618  0.31474599  ## Jackknife jackknife(x, do = mean) # Sample mean #>  original      mean      bias     error  #> 0.1456599 0.1456599 0.0000000 0.2394428   ## Get the leave-one-out values instead of summary jackknife(x, do = mean, f = function(x) { x }) #>  [1] 0.19152082 0.19342042 0.10769904 0.14875192 0.25618400 0.10601127 #>  [7] 0.11938772 0.20343002 0.10146239 0.08518697 0.10419447 0.15732785 #> [13] 0.12970218 0.08063737 0.11548916 0.08222062 0.18823202 0.27572430 #> [19] 0.11352506 0.15309013","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/keep.html","id":null,"dir":"Reference","previous_headings":"","what":"Keep Rows/Columns Using a Predicate \u2014 keep","title":"Keep Rows/Columns Using a Predicate \u2014 keep","text":"Keeps rows/columns array-like object using predicate function.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/keep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keep Rows/Columns Using a Predicate \u2014 keep","text":"","code":"keep(x, ...)  keep_cols(x, ...)  keep_rows(x, ...)  # S4 method for ANY keep(   x,   f,   margin = 1,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )  # S4 method for ANY keep_rows(   x,   f,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )  # S4 method for ANY keep_cols(   x,   f,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/keep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keep Rows/Columns Using a Predicate \u2014 keep","text":"x R object (matrix data.frame). ... arguments passed f. f predicate function. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). negate logical scalar: negation f used instead f? logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. verbose logical scalar: R report extra information progress?","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/keep.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Keep Rows/Columns Using a Predicate \u2014 keep","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/keep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keep Rows/Columns Using a Predicate \u2014 keep","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    2    5   10    6    2 #> [2,]   NA    6    2    2   NA #> [3,]    6    3    4    8    4 #> [4,]    3    5    3    6    2 #> [5,]    3   NA   10    9   10  ## Keep row without any NA keep(X, f = is.na, margin = 1, negate = TRUE, all = TRUE) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    2    5   10    6    2 #> [2,]    6    3    4    8    4 #> [3,]    3    5    3    6    2 ## Keep row without any NA keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE) #>      [,1] [,2] #> [1,]   10    6 #> [2,]    2    2 #> [3,]    4    8 #> [4,]    3    6 #> [5,]   10    9","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_gcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Greatest Common Divisor \u2014 math_gcd","title":"Greatest Common Divisor \u2014 math_gcd","text":"Computes greatest common divisor (GCD) two integer using Euclidean algorithm.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_gcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Greatest Common Divisor \u2014 math_gcd","text":"","code":"math_gcd(x, y)  # S4 method for numeric,numeric math_gcd(x, y)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_gcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Greatest Common Divisor \u2014 math_gcd","text":"x, y numeric vector.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_gcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Greatest Common Divisor \u2014 math_gcd","text":"numeric vector.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_gcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Greatest Common Divisor \u2014 math_gcd","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_lcm.html","id":null,"dir":"Reference","previous_headings":"","what":"Least Common Multiple \u2014 math_lcm","title":"Least Common Multiple \u2014 math_lcm","text":"Computes lowest common multiple denominators set fractions.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_lcm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Least Common Multiple \u2014 math_lcm","text":"","code":"math_lcm(x, y)  # S4 method for numeric,numeric math_lcm(x, y)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_lcm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Least Common Multiple \u2014 math_lcm","text":"x, y numeric vector.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_lcm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Least Common Multiple \u2014 math_lcm","text":"numeric vector.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_lcm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Least Common Multiple \u2014 math_lcm","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/missing.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for Working With Missing Values \u2014 missing","title":"Tools for Working With Missing Values \u2014 missing","text":"remove_NA() remove rows/columns contain missing values. replace_NA replaces missing values values.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/missing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for Working With Missing Values \u2014 missing","text":"","code":"remove_NA(x, ...)  replace_NA(x, ...)  # S4 method for ANY remove_NA(x, margin = 1, all = FALSE)  # S4 method for matrix replace_NA(x, value = 0)  # S4 method for data.frame replace_NA(x, value = 0)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/missing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for Working With Missing Values \u2014 missing","text":"x R object (matrix data.frame). ... Currently used. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. value possible replacement value.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/missing.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tools for Working With Missing Values \u2014 missing","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/missing.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for Working With Missing Values \u2014 missing","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]   NA    7    7    3    1 #> [2,]    7   NA    9    4    3 #> [3,]    9    6    7    3    5 #> [4,]    4    8    3    9    4 #> [5,]    1   NA   10    7    2  ## Remove rows with NA remove_NA(X, margin = 1) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    9    6    7    3    5 #> [2,]    4    8    3    9    4  ## Replace NA with zeros replace_NA(X, value = 0) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    0    7    7    3    1 #> [2,]    7    0    9    4    3 #> [3,]    9    6    7    3    5 #> [4,]    4    8    3    9    4 #> [5,]    1    0   10    7    2","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the Availability of a Package \u2014 needs","title":"Check the Availability of a Package \u2014 needs","text":"Check Availability Package","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the Availability of a Package \u2014 needs","text":"","code":"needs(x, ask = TRUE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the Availability of a Package \u2014 needs","text":"x character vector naming packages check. ask logical scalar: user asked select packages downloaded installed?","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the Availability of a Package \u2014 needs","text":"Invisibly returns NULL.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check the Availability of a Package \u2014 needs","text":"needs() designed use inside functions package check availability suggested package. required packages available R running interactively, user asked install packages.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check the Availability of a Package \u2014 needs","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix Predicates \u2014 predicate-matrix","title":"Matrix Predicates \u2014 predicate-matrix","text":"is_square() checks matrix square. is_symmetric() checks matrix symmetric.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix Predicates \u2014 predicate-matrix","text":"","code":"is_square(x)  is_symmetric(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix Predicates \u2014 predicate-matrix","text":"x matrix tested.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrix Predicates \u2014 predicate-matrix","text":"logical scalar.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Numeric Predicates \u2014 predicate-numeric","title":"Numeric Predicates \u2014 predicate-numeric","text":"Check numeric objects: is_zero() checks object contains zeros. is_odd() is_even() check number odd even, respectively. is_positive() is_negative check object contains (strictly) positive negative numbers. is_whole() checks object contains whole numbers.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Numeric Predicates \u2014 predicate-numeric","text":"","code":"is_zero(x, na.rm = FALSE)  is_odd(x, na.rm = FALSE)  is_even(x, na.rm = FALSE)  is_positive(x, strict = FALSE, na.rm = FALSE)  is_negative(x, strict = FALSE, na.rm = FALSE)  is_whole(x, na.rm = FALSE, tolerance = .Machine$double.eps^0.5)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Numeric Predicates \u2014 predicate-numeric","text":"x numeric object tested. na.rm logical scalar: missing values (including NaN) omitted? strict logical scalar: strict inequality used? tolerance numeric scalar giving tolerance check within.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Numeric Predicates \u2014 predicate-numeric","text":"logical vector.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-scalar.html","id":null,"dir":"Reference","previous_headings":"","what":"Scalar Type Predicates \u2014 predicate-scalar","title":"Scalar Type Predicates \u2014 predicate-scalar","text":"Scalar Type Predicates","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-scalar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scalar Type Predicates \u2014 predicate-scalar","text":"","code":"is_scalar_list(x)  is_scalar_atomic(x)  is_scalar_vector(x)  is_scalar_numeric(x)  is_scalar_integer(x)  is_scalar_double(x)  is_scalar_character(x)  is_scalar_logical(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-scalar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scalar Type Predicates \u2014 predicate-scalar","text":"x object tested.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-scalar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scalar Type Predicates \u2014 predicate-scalar","text":"logical scalar.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-trend.html","id":null,"dir":"Reference","previous_headings":"","what":"Numeric Trend Predicates \u2014 predicate-trend","title":"Numeric Trend Predicates \u2014 predicate-trend","text":"Check numeric objects: is_constant() checks equality among elements vector. is_increasing() is_decreasing() check sequence numbers monotonically increasing decreasing, respectively.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-trend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Numeric Trend Predicates \u2014 predicate-trend","text":"","code":"is_constant(x, tolerance = .Machine$double.eps^0.5, na.rm = FALSE)  is_increasing(x, na.rm = FALSE)  is_decreasing(x, na.rm = FALSE)  is_greater(x, y, strict = FALSE, na.rm = FALSE)  is_lower(x, y, strict = FALSE, na.rm = FALSE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-trend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Numeric Trend Predicates \u2014 predicate-trend","text":"x, y numeric object tested. tolerance numeric scalar giving tolerance check within. na.rm logical scalar: missing values (including NaN) omitted? strict logical scalar: strict inequality used?","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-trend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Numeric Trend Predicates \u2014 predicate-trend","text":"logical scalar.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-type.html","id":null,"dir":"Reference","previous_headings":"","what":"Type Predicates \u2014 predicate-type","title":"Type Predicates \u2014 predicate-type","text":"Type Predicates","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Type Predicates \u2014 predicate-type","text":"","code":"is_list(x)  is_atomic(x)  is_vector(x)  is_numeric(x)  is_integer(x)  is_double(x)  is_character(x)  is_logical(x)  is_error(x)  is_warning(x)  is_message(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Type Predicates \u2014 predicate-type","text":"x object tested.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-type.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Type Predicates \u2014 predicate-type","text":"logical scalar.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-utils.html","id":null,"dir":"Reference","previous_headings":"","what":"Utility Predicates \u2014 predicate-utils","title":"Utility Predicates \u2014 predicate-utils","text":"is_empty() checks object empty (zero-length dimensions). has_length() checks long object. has_names() checks object named. has_duplicates() checks object duplicated elements. has_missing() has_infinite() check object contains missing infinite values.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-utils.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility Predicates \u2014 predicate-utils","text":"","code":"has_length(x, n = NULL)  has_names(x, names = NULL)  has_duplicates(x)  has_missing(x)  has_infinite(x)  is_empty(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-utils.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility Predicates \u2014 predicate-utils","text":"x vector tested. n length-one numeric vector specifying length test x . NULL, returns TRUE x length greater zero, FALSE otherwise. names character vector specifying names test x . NULL, returns TRUE x names, FALSE otherwise.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-utils.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility Predicates \u2014 predicate-utils","text":"logical scalar.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/remove_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Constant Columns \u2014 remove_constant","title":"Remove Constant Columns \u2014 remove_constant","text":"Remove Constant Columns","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/remove_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Constant Columns \u2014 remove_constant","text":"","code":"remove_constant(x, ...)  # S4 method for ANY remove_constant(x, na.rm = FALSE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/remove_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Constant Columns \u2014 remove_constant","text":"x R object (matrix data.frame). ... Currently used. na.rm logical scalar: NA values stripped computation proceeds?","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/remove_constant.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Remove Constant Columns \u2014 remove_constant","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/remove_constant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remove Constant Columns \u2014 remove_constant","text":"","code":"## Create a data.frame X <- data.frame(A = 1, B = 1:3) X #>   A B #> 1 1 1 #> 2 1 2 #> 3 1 3  remove_constant(X) #>   B #> 1 1 #> 2 2 #> 3 3  ## Add NA X[1, 1] <- NA remove_constant(X) #>    A B #> 1 NA 1 #> 2  1 2 #> 3  1 3 remove_constant(X, na.rm = TRUE) #>   B #> 1 1 #> 2 2 #> 3 3","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/validate.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a Condition \u2014 validate","title":"Validate a Condition \u2014 validate","text":"Validate Condition","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/validate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a Condition \u2014 validate","text":"","code":"validate(expr)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/validate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a Condition \u2014 validate","text":"expr object evaluated.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/validate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate a Condition \u2014 validate","text":"Returns NULL success, otherwise returns error string.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/validate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Validate a Condition \u2014 validate","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/with_seed.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate an Expression with a Temporarily Seed \u2014 with_seed","title":"Evaluate an Expression with a Temporarily Seed \u2014 with_seed","text":"Evaluate Expression Temporarily Seed","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/with_seed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate an Expression with a Temporarily Seed \u2014 with_seed","text":"","code":"with_seed(expr, seed, ..., envir = parent.frame(), rounding = TRUE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/with_seed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate an Expression with a Temporarily Seed \u2014 with_seed","text":"expr expression evaluated. seed single value passed set.seed(). ... arguments passed set.seed(). envir environment expr evaluated. rounding logical scalar: default discrete uniform generation method R versions prior 3.6.0 used? Usefull unit testing.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/with_seed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate an Expression with a Temporarily Seed \u2014 with_seed","text":"results expr evaluated.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/zero.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for Working With Zeros \u2014 zero","title":"Tools for Working With Zeros \u2014 zero","text":"remove_zero() remove rows/columns contain zeros. replace_zero replaces zeros.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/zero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for Working With Zeros \u2014 zero","text":"","code":"remove_zero(x, ...)  replace_zero(x, ...)  # S4 method for ANY remove_zero(x, margin = 1, all = FALSE, na.rm = FALSE)  # S4 method for matrix replace_zero(x, value)  # S4 method for data.frame replace_zero(x, value)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/zero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for Working With Zeros \u2014 zero","text":"x R object (matrix data.frame). ... Currently used. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. na.rm logical scalar: NA values stripped computation proceeds? value possible replacement value.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/zero.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tools for Working With Zeros \u2014 zero","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/zero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for Working With Zeros \u2014 zero","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add zero k <- sample(1:25, 3, FALSE) X[k] <- 0 X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    7   10    3    5    5 #> [2,]    3    9    0    7    7 #> [3,]    7    4    9    1    7 #> [4,]    7    7    8    2    2 #> [5,]   10    2    0   10    0  ## Remove rows with zero remove_zero(X, margin = 1) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    7   10    3    5    5 #> [2,]    7    4    9    1    7 #> [3,]    7    7    8    2    2  ## Replace zero replace_zero(X, value = 1) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    7   10    3    5    5 #> [2,]    3    9    1    7    7 #> [3,]    7    4    9    1    7 #> [4,]    7    7    8    2    2 #> [5,]   10    2    1   10    1","package":"arkhe"},{"path":"https://packages.tesselle.org/kairos/articles/event.html","id":"definition","dir":"Articles","previous_headings":"","what":"Definition","title":"Event Date Model","text":"Event accumulation dates density estimates occupation duration archaeological site (L. Bellanger, Husi, Tomassone 2006; L. Bellanger, Tomassone, Husi 2008; Lise Bellanger Husi 2012). event date estimation terminus post-quem archaeological assemblage. accumulation date represents \u201cchronological profile\u201d assemblage. According Lise Bellanger Husi (2012), accumulation date can interpreted \u201cbest [\u2026] formation process reflecting duration succession events scale archaeological time, worst, imprecise dating due contamination context residual intrusive material.\u201d words, accumulation dates estimate occurrence archaeological events rhythms long term.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/event.html","id":"event-date","dir":"Articles","previous_headings":"Definition","what":"Event Date","title":"Event Date Model","text":"Event dates estimated fitting Gaussian multiple linear regression model factors resulting correspondence analysis - somewhat similar idea introduced Poblome Groenen (2003). model results known dates selection reliable contexts allows predict event dates remaining assemblages. First, correspondence analysis (CA) carried summarize information count matrix \\(X\\). correspondence analysis \\(X\\) provides coordinates \\(m\\) rows along \\(q\\) factorial components, denoted \\(f_{ik} ~\\forall \\\\left[ 1,m \\right], k \\\\left[ 1,q \\right]\\). , assuming \\(n\\) assemblages reliably dated another source, Gaussian multiple linear regression model fitted factorial components \\(n\\) dated assemblages: \\[ t^E_i = \\beta_{0} + \\sum_{k = 1}^{q} \\beta_{k} f_{ik} + \\epsilon_i ~\\forall \\[1,n] \\] \\(t^E_i\\) known date point estimate \\(\\)th assemblage, \\(\\beta_k\\) regression coefficients \\(\\epsilon_i\\) normally, identically independently distributed random variables, \\(\\epsilon_i \\sim \\mathcal{N}(0,\\sigma^2)\\). \\(n\\) equations stacked together written matrix notation \\[ t^E = F \\beta + \\epsilon \\] \\(\\epsilon \\sim \\mathcal{N}_{n}(0,\\sigma^2 I_{n})\\), \\(\\beta = \\left[ \\beta_0 \\cdots \\beta_q \\right]' \\\\mathbb{R}^{q+1}\\) \\[ F = \\begin{bmatrix} 1 & f_{11} & \\cdots & f_{1q} \\\\ 1 & f_{21} & \\cdots & f_{2q} \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ 1 & f_{n1} & \\cdots & f_{nq} \\end{bmatrix} \\] Assuming \\(F'F\\) nonsingular, ordinary least squares estimator unknown parameter vector \\(\\beta\\) : \\[ \\widehat{\\beta} = \\left( F'F \\right)^{-1} F' t^E \\] Finally, given vector CA coordinates \\(f_i\\), predicted event date assemblage \\(t^E_i\\) : \\[ \\widehat{t^E_i} = f_i \\hat{\\beta} \\] endpoints \\(100(1 \u2212 \\alpha)\\)% associated prediction confidence interval given : \\[ \\widehat{t^E_i} \\pm t_{\\alpha/2,n-q-1} \\sqrt{\\widehat{V}} \\] \\(\\widehat{V_i}\\) estimator variance prediction error:  \\[ \\widehat{V_i} = \\widehat{\\sigma}^2 \\left( f_i^T \\left( F'F \\right)^{-1} f_i + 1 \\right) \\] \\(\\widehat{\\sigma} = \\frac{\\sum_{=1}^{n} \\left( t_i - \\widehat{t^E_i} \\right)^2}{n - q - 1}\\). probability density event date \\(t^E_i\\) can described normal distribution: \\[ t^E_i \\sim \\mathcal{N}(\\widehat{t^E_i},\\widehat{V_i}) \\]","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/event.html","id":"accumulation-date","dir":"Articles","previous_headings":"Definition","what":"Accumulation Date","title":"Event Date Model","text":"row (assemblages) columns (types) CA coordinates linked together -called transition formulae, event dates type \\(t^E_j\\) can predicted following procedure . , accumulation date \\(t^A_i\\) defined weighted mean event date ceramic types found given assemblage. weights conditional frequencies respective types assemblage (akin MCD). accumulation date estimated : \\[ \\widehat{t^A_i} = \\sum_{j = 1}^{p} \\widehat{t^E_j} \\times \\frac{x_{ij}}{x_{\\cdot}} \\] probability density accumulation date \\(t^A_i\\) can described Gaussian mixture: \\[ t^A_i \\sim \\frac{x_{ij}}{x_{\\cdot}} \\mathcal{N}(\\widehat{t^E_j},\\widehat{V_j}^2) \\] Interestingly, integral accumulation date offers estimates cumulative occurrence archaeological events, close enough definition tempo plot introduced Dye (2016).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/event.html","id":"limitation","dir":"Articles","previous_headings":"","what":"Limitation","title":"Event Date Model","text":"Event accumulation dates estimation relies conditions assumptions matrix seriation problem. Dunnell (1970) summarizes conditions assumptions follows. homogeneity conditions state groups included seriation must: comparable duration. Belong cultural tradition. Come local area. mathematical assumptions state distribution historical temporal class: continuous time. Exhibits form unimodal curve. Theses assumptions create distributional model ordering accomplished arranging matrix class distributions approximate required pattern. resulting order inferred chronological. Predicted dates interpreted care: dates highly dependent range known dates fit regression.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/event.html","id":"usage","dir":"Articles","previous_headings":"","what":"Usage","title":"Event Date Model","text":"package provides implementation chronological modeling method developed Lise Bellanger Husi (2012). method slightly modified allows construction different probability density curves archaeological assemblage dates (event, activity tempo).    Resampling methods can used check stability resulting model. jackknife() used, one type/fabric removed time statistics recalculated. way, one can assess whether certain type/fabric substantial influence date estimate. bootstrap() used, large number new bootstrap assemblages created, sample size, resampling original assemblage replacement. , examination bootstrap statistics makes possible pinpoint assemblages require investigation.","code":"## Bellanger et al. did not publish the data supporting their demonstration:  ## no replication of their results is possible.  ## Here is an example using the Zuni dataset from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) ## The names of the vector entries must match the names of the assemblages zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, dates = zuni_dates, rank = 10)  ## Model summary ## (results are expressed in rata die) summary(model) #>  #> Call: #> stats::lm(formula = date ~ ., data = contexts) #>  #> Residuals: #>   LZ0852   LZ0610   LZ0578   LZ0569   LZ0563   LZ0329   LZ0322   LZ0279  #>  -479.32   351.48  1283.51   163.57 -1626.71  -290.90   950.04 -1427.33  #>   LZ0227   LZ0067   LZ0066  LZ0005Q     CS16    CS144   LZ1209  #>  -280.59   -50.24   266.02    45.83  -105.47  1016.24   183.86  #> attr(,\"class\") #> [1] \"RataDie\" #> attr(,\"class\")attr(,\"package\") #> [1] \"aion\" #>  #> Coefficients: #>             Estimate Std. Error t value Pr(>|t|)     #> (Intercept) 425015.4     1064.9 399.095 2.36e-10 *** #> F1          -57813.4      602.6 -95.938 7.08e-08 *** #> F2            9387.7      615.2  15.260 0.000108 *** #> F3           -2047.2      789.1  -2.594 0.060411 .   #> F4            3928.9     2127.1   1.847 0.138452     #> F5           -1146.2     1442.3  -0.795 0.471276     #> F6             995.1      485.4   2.050 0.109663     #> F7            1667.0     1906.7   0.874 0.431304     #> F8            4126.0     1264.9   3.262 0.031027 *   #> F9           -1889.1     1079.6  -1.750 0.155039     #> F10           -144.8      967.9  -0.150 0.888300     #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Residual standard error: 1494 on 4 degrees of freedom #> Multiple R-squared:  0.9997, Adjusted R-squared:  0.999  #> F-statistic:  1433 on 10 and 4 DF,  p-value: 1.168e-06  ## Extract model coefficients ## (convert results to Gregorian years) coef(model, calendar = CE()) #>  (Intercept)           F1           F2           F3           F4           F5  #> 1163.6512430 -158.2887134   25.7005062   -5.6059619   10.7559523   -3.1402928  #>           F6           F7           F8           F9          F10  #>    2.7236281    4.5617156   11.2951729   -5.1728421   -0.3984238  ## Extract residual standard deviation ## (convert results to Gregorian years) sigma(model, calendar = CE()) #> [1] 4.088072  ## Extract model residuals ## (convert results to Gregorian years) resid(model, calendar = CE()) #>  [1] -1.3132066  0.9602214  3.5123344  0.4453877 -4.4554781 -0.7975492 #>  [7]  2.6001038 -3.9104984 -0.7693659 -0.1399926  0.7260925  0.1228117 #> [13] -0.2908912  2.7814906  0.5009809  ## Extract model fitted values ## (convert results to Gregorian years) fitted(model, calendar = CE()) #>  [1] 1217.3105 1073.0370 1176.4849 1096.5531 1210.4540 1076.7948 1106.3999 #>  [8] 1122.9078 1104.7666  863.1376 1110.2712  858.8744 1328.2882 1259.2185 #> [15] 1250.4963 ## Estimate event dates ## (results are expressed in rata die) eve <- predict_event(model, margin = 1, level = 0.95) head(eve) #>            date    lower    upper    error #> LZ1105 426454.0 420445.5 432462.4 2164.087 #> LZ1103 417145.8 414689.9 419601.7  884.558 #> LZ1100 421968.1 416394.9 427541.4 2007.317 #> LZ1099 400974.7 396642.1 405307.4 1560.503 #> LZ1097 397176.5 393404.6 400948.3 1358.530 #> LZ1096 306371.0 300801.2 311940.7 2006.057 ## Activity plot plot(model, type = \"activity\", event = TRUE, select = 1:6) plot(model, type = \"activity\", event = TRUE, select = \"LZ1105\") ## Tempo plot plot(model, type = \"tempo\", select = \"LZ1105\") ## Check model variability ## (results are expressed in rata die) ## Warning: this may take a few seconds  ## Jackknife fabrics jack <- jackknife(model) head(jack) #>             date     lower     upper    error       bias #> LZ1105 155634807 155628798 155640815 2164.087 2638541997 #> LZ1103 152396320 152393864 152398776  884.558 2583645955 #> LZ1100 154171872 154166299 154177445 2007.317 2613748366 #> LZ1099 146238877 146234544 146243210 1560.503 2479244339 #> LZ1097 145631868 145628096 145635640 1358.530 2468989755 #> LZ1096 112226452 112220882 112232021 2006.057 1902641373  ## Bootstrap of assemblages boot <- bootstrap(model, n = 30) head(boot) #>             min     mean      max       Q5      Q95 #> LZ1105 412500.5 428092.6 441503.3 417020.9 438980.7 #> LZ1103 396969.3 417512.2 435152.2 404258.3 430499.0 #> LZ1100 368850.1 419240.0 437436.0 396933.0 436149.3 #> LZ1099 397156.4 400615.9 406365.3 397296.1 403830.9 #> LZ1097 357449.2 395404.5 439303.3 364671.7 437187.9 #> LZ1096 264824.9 307706.2 381153.9 264824.9 355533.8","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/seriation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Seriation","text":"matrix seriation problem archaeology based three conditions two assumptions, Dunnell (1970) summarizes follows. homogeneity conditions state groups included seriation must: comparable duration, Belong cultural tradition, Come local area. mathematical assumptions state distribution historical temporal class: continuous time, Exhibits form unimodal curve. Theses assumptions create distributional model ordering accomplished arranging matrix class distributions approximate required pattern. resulting order inferred chronological.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/seriation.html","id":"reciprocal-ranking","dir":"Articles","previous_headings":"","what":"Reciprocal ranking","title":"Seriation","text":"Reciprocal ranking iteratively rearrange rows /columns according weighted rank data matrix convergence (Ihm 2005). given incidence matrix \\(C\\): rows \\(C\\) rearranged increasing order : \\[ x_{} = \\sum_{j = 1}^{p} j \\frac{c_{ij}}{c_{\\cdot}} \\] columns \\(C\\) rearranged similar way: \\[ y_{j} = \\sum_{= 1}^{m} \\frac{c_{ij}}{c_{\\cdot j}} \\] two steps repeated convergence. Note procedure enter infinite loop.  positive difference column mean percentage (french \u201c\u00e9cart positif au pourcentage moyen\u201d, EPPM) represents deviation situation statistical independence (Desachy 2004). independence can interpreted absence relationships types chronological order assemblages, EPPM useful graphical tool explore significance relationship rows columns related seriation (Desachy 2004).","code":"## Build an incidence matrix with random data set.seed(12345) bin <- sample(c(TRUE, FALSE), 400, TRUE, c(0.6, 0.4)) incidence1 <- matrix(bin, nrow = 20)  ## Get seriation order on rows and columns ## If no convergence is reached before the maximum number of iterations (100),  ## it stops with a warning. (indices <- seriate_rank(incidence1, margin = c(1, 2), stop = 100)) #> <RankPermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 6 15 12 14 2 5 10 8 13 20 16 18 19 7 9 1 3 11 17 4... #> - Column order: 9 4 2 8 3 1 6 5 16 20 15 14 12 17 13 10 19 7 11 18...  ## Permute matrix rows and columns incidence2 <- permute(incidence1, indices)  ## Plot matrix tabula::plot_heatmap(incidence1, col = c(\"white\", \"black\")) tabula::plot_heatmap(incidence2, col = c(\"white\", \"black\")) ## Replicates Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Plot frequencies and EPPM values tabula::seriograph(compiegne) ## Get seriation order for columns on EPPM using the reciprocal ranking method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #> <RankPermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Permute columns compiegne_permuted <- permute(compiegne, indices)  ## Plot frequencies and EPPM values tabula::seriograph(compiegne_permuted)","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/seriation.html","id":"seriation","dir":"Articles","previous_headings":"Correspondence analysis","what":"Seriation","title":"Seriation","text":"Correspondence Analysis (CA) effective method seriation archaeological assemblages. order rows columns given coordinates along one dimension CA space, assumed account temporal variation. direction temporal change within correspondence analysis space arbitrary: additional information needed determine actual order time.","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni) ## Get row permutations from CA coordinates (zun_indices <- seriate_average(zuni, margin = c(1, 2))) #> <AveragePermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 372 387 350 367 110 417 364 407 357 356 160 373 34... #> - Column order: 18 14 17 16 13 15 9 8 12 11 6 7 5 10 4 2 3 1...  ## Plot CA results dimensio::biplot(zun_indices) ## Permute data matrix zuni_permuted <- permute(zuni, zun_indices)  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni_permuted)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/seriation.html","id":"refining","dir":"Articles","previous_headings":"Correspondence analysis","what":"Refining","title":"Seriation","text":"Peeples Schachner (2012) propose procedure identify samples subject sampling error samples underlying structural relationships might influencing ordering along CA space. relies partial bootstrap approach CA-based seriation sample replicated n times. maximum dimension length convex hull around sample point cloud allows remove samples given cutoff value. According Peeples Schachner (2012), \u201c[] point removal procedure [results ] reduced dataset position individuals within CA highly stable produces ordering consistent assumptions frequency seriation.\u201d","code":"## Partial bootstrap CA ## Warning: this may take a few seconds! zuni_boot <- dimensio::bootstrap(zun_indices, n = 30)  ## Bootstrap CA results for the rows ## (add convex hull) zuni_boot |>    dimensio::viz_rows(col = \"lightgrey\", pch = 16) |>    dimensio::viz_hull(col = adjustcolor(\"#004488\", alpha = 0.5))  ## Bootstrap CA results for the columns zuni_boot |>    dimensio::viz_columns(pch = 16) ## Replicates Peeples and Schachner 2012 results ## Samples with convex hull maximum dimension length greater than the cutoff ## value will be marked for removal. ## Define cutoff as one standard deviation above the mean fun <- function(x) { mean(x) + sd(x) } (zuni_refine <- seriate_refine(zun_indices, cutoff = fun, margin = 1)) #> <RefinePermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 372 350 387 367 110 417 364 357 407 160 356 373 34... #> - Column order: 17 18 14 16 13 15 9 8 12 11 6 10 7 5 4 2 3 1... #> Partial bootstrap refinement: #> - Cutoff value: 1.75 #> - Rows to keep: 354 of 420 (84%)  ## Plot CA results for the rows dimensio::viz_rows(zuni_refine, highlight = \"observation\", pch = c(16, 15)) ## Histogram of convex hull maximum dimension length hist(zuni_refine[[\"length\"]], xlab = \"Maximum length\", main = \"\") abline(v = zuni_refine[[\"cutoff\"]], col = \"red\") ## Permute data matrix zuni_permuted2 <- permute(zuni, zuni_refine)  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni_permuted2)","package":"kairos"},{"path":[],"package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"convenient reproducible toolkit relative absolute dating analysis chronological patterns. package includes functions chronological modeling dating archaeological assemblages count data. provides methods matrix seriation. also allows compute time point estimates density estimates occupation duration archaeological site. kairos provides methods : Matrix seriation: seriate_rank() seriate_average() Mean ceramic date estimation (South 1977): mcd() Event accumulation date estimation (Bellanger Husi 2012): event() Aoristic analysis (Ratcliffe 2000): aoristic() Chronological apportioning (Roberts et al.\u00a02012): apportion() tabula companion package kairos provides functions visualization analysis archaeological count data.","code":"To cite kairos in publications use:    Frerebeau N (2023). _kairos: Analysis of Chronological Patterns from   Archaeological Count Data_. Universit\u00e9 Bordeaux Montaigne, Pessac,   France. doi:10.5281/zenodo.5653896   <https://doi.org/10.5281/zenodo.5653896>, R package version 2.0.1,   <https://packages.tesselle.org/kairos/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{kairos: Analysis of Chronological Patterns from Archaeological Count Data}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 2.0.1},     url = {https://packages.tesselle.org/kairos/},     doi = {10.5281/zenodo.5653896},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"can install released version kairos CRAN : development version GitHub :","code":"install.packages(\"kairos\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/kairos\")","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"kairos v2.0 uses aion internal date representation. Look vignette(\"aion\") start. assumes keep data tidy: variable (type/taxa) must saved column observation (sample/case) must saved row.","code":"## Load packages library(kairos) ## Data from Husi 2022 data(\"loire\", package = \"folio\") keep <- c(\"Anjou\", \"Bl\u00e9sois\", \"Orl\u00e9anais\", \"Haut-Poitou\", \"Touraine\") loire <- subset(loire, area %in% keep)  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (weights) by group ao <- aoristic(loire_range, step = 50, weight = TRUE, groups = loire$area)  ## Plot plot(ao, col = \"grey\") ## Rate of change by group ro <- roc(ao, n = 30) plot(ro)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"Please note kairos project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":[],"package":"kairos"},{"path":[],"package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/AoristicSum-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Aoristic Sum \u2014 AoristicSum-class","title":"Aoristic Sum \u2014 AoristicSum-class","text":"S4 class represent aoristic analysis results.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/AoristicSum-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Aoristic Sum \u2014 AoristicSum-class","text":"breaks RataDie vector giving date break time-blocks. weights numeric vector. groups character vector store group names (). p numeric array giving aorisitic probabilities.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/AoristicSum-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Aoristic Sum \u2014 AoristicSum-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/AoristicSum-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Aoristic Sum \u2014 AoristicSum-class","text":"code snippets , x AoristicSum object. .data.frame(x) Coerces data.frame.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/AoristicSum-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Aoristic Sum \u2014 AoristicSum-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/CountApportion-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Apportioning \u2014 CountApportion-class","title":"Count Apportioning \u2014 CountApportion-class","text":"S4 class represent artifact apportioning results. Gives apportioning artifact types (columns) per site (rows) per period (dim. 3).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/CountApportion-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Count Apportioning \u2014 CountApportion-class","text":".Data \\(m \\times p \\times k\\) array giving proportion artifact type (\\(p\\)) given period (\\(k\\)). p \\(m \\times p \\times k\\) array giving probability apportioning artifact type (\\(p\\)) given period (\\(k\\)). method character string specifying distribution used apportioning (type popularity curve). length-one numeric vector giving beginning period interest (years AD). length-one numeric vector giving end period interest (years AD). step length-one numeric vector giving step size, .e. width time step apportioning (years AD).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/CountApportion-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count Apportioning \u2014 CountApportion-class","text":"class inherits base array.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/CountApportion-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Count Apportioning \u2014 CountApportion-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/EventDate-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Date Model \u2014 EventDate-class","title":"Date Model \u2014 EventDate-class","text":"S4 class store event accumulation times archaeological assemblages.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/EventDate-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Date Model \u2014 EventDate-class","text":"dates length-\\(m\\) numeric vector dates expressed rata die. model multiple linear model: Gaussian multiple linear regression model fitted event date estimation prediction. keep integer vector giving subscripts CA components keep.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/EventDate-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Date Model \u2014 EventDate-class","text":"Dates internally stored rata die. class inherits dimensio::CA.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/EventDate-class.html","id":"extract","dir":"Reference","previous_headings":"","what":"Extract","title":"Date Model \u2014 EventDate-class","text":"code snippets , x EventDate object. time(x) Extract dates assemblages. coef(x) Extract model coefficients. fitted(x) Extract model fitted values. residuals(x) Extract model residuals. sigma(x) Extract residual standard deviation. terms(x) Extract model terms.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/EventDate-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Date Model \u2014 EventDate-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/IncrementTest-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Frequency Increment Test \u2014 IncrementTest-class","title":"Frequency Increment Test \u2014 IncrementTest-class","text":"S4 class represent Frequency Increment Test results.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/IncrementTest-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Frequency Increment Test \u2014 IncrementTest-class","text":"statistic numeric vector giving values t-statistic. parameter integer giving degrees freedom t-statistic. p_value numeric vector giving p-value test.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/IncrementTest-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Frequency Increment Test \u2014 IncrementTest-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/IncrementTest-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Frequency Increment Test \u2014 IncrementTest-class","text":"code snippets , x IncrementTest object. .data.frame(x) Coerces data.frame.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/IncrementTest-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Frequency Increment Test \u2014 IncrementTest-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/MeanDate-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Date \u2014 MeanDate-class","title":"Mean Date \u2014 MeanDate-class","text":"S4 class store weighted mean date (e.g. Mean Ceramic Date) archaeological assemblages.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/MeanDate-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Mean Date \u2014 MeanDate-class","text":"dates length-\\(p\\) numeric vector giving dates (ceramic) types expressed rata die. replications numeric matrix giving replications.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/MeanDate-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mean Date \u2014 MeanDate-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/MeanDate-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Mean Date \u2014 MeanDate-class","text":"code snippets , x MeanDate object. .data.frame(x) Coerces data.frame.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/MeanDate-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean Date \u2014 MeanDate-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/PermutationOrder-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutation Order \u2014 PermutationOrder-class","title":"Permutation Order \u2014 PermutationOrder-class","text":"S4 classes represent permutation order.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/PermutationOrder-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Permutation Order \u2014 PermutationOrder-class","text":"rows_order integer vector giving rows permutation. columns_order integer vector giving columns permutation.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/PermutationOrder-class.html","id":"subset","dir":"Reference","previous_headings":"","what":"Subset","title":"Permutation Order \u2014 PermutationOrder-class","text":"code snippets , x PermutationOrder object. x[[]] Extract information slot selected subscript . length-one character vector.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/PermutationOrder-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Permutation Order \u2014 PermutationOrder-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RateOfChange-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate of Change \u2014 RateOfChange-class","title":"Rate of Change \u2014 RateOfChange-class","text":"S4 class represent rates change aoristic analysis.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RateOfChange-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Rate of Change \u2014 RateOfChange-class","text":"replicates non-negative integer giving number replications. groups character vector store group names ().","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RateOfChange-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rate of Change \u2014 RateOfChange-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RateOfChange-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Rate of Change \u2014 RateOfChange-class","text":"code snippets , x AoristicSum object. .data.frame(x) Coerces data.frame.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RateOfChange-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rate of Change \u2014 RateOfChange-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RefinePermutationOrder-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial Bootstrap CA \u2014 RefinePermutationOrder-class","title":"Partial Bootstrap CA \u2014 RefinePermutationOrder-class","text":"S4 class store partial bootstrap correspondence analysis results.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RefinePermutationOrder-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Partial Bootstrap CA \u2014 RefinePermutationOrder-class","text":"length numeric vector giving convex hull maximum dimension length. cutoff length-one numeric vector giving cutoff value samples selection. keep integer vector giving subscript variables kept.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RefinePermutationOrder-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Partial Bootstrap CA \u2014 RefinePermutationOrder-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":null,"dir":"Reference","previous_headings":"","what":"Aoristic Analysis \u2014 aoristic","title":"Aoristic Analysis \u2014 aoristic","text":"Computes aoristic sum.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aoristic Analysis \u2014 aoristic","text":"","code":"aoristic(x, y, ...)  # S4 method for numeric,numeric aoristic(   x,   y,   step = 1,   start = min(x),   end = max(y),   calendar = CE(),   weight = TRUE,   groups = NULL )  # S4 method for ANY,missing aoristic(   x,   step = 1,   start = NULL,   end = NULL,   calendar = CE(),   weight = TRUE,   groups = NULL )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aoristic Analysis \u2014 aoristic","text":"x, y numeric vector giving lower upper boundaries time intervals, respectively. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. step length-one integer vector giving step size, .e. width time step time series (defaults \\(1\\), .e. annual level). start length-one numeric vector giving beginning time window. end length-one numeric vector giving end time window. calendar TimeScale object specifying calendar x y (see calendar()). Defaults Gregorian Common Era. weight logical scalar: aoristic sum weighted length periods (default). FALSE aoristic sum number elements within time block. groups factor vector sense .factor(groups) defines grouping. x list (data.frame), groups can length-one vector giving index grouping component (column) x.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aoristic Analysis \u2014 aoristic","text":"AoristicSum object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aoristic Analysis \u2014 aoristic","text":"Aoristic analysis used determine probability contemporaneity archaeological sites assemblages. aoristic analysis distributes probability event uniformly temporal fraction period considered. aoristic sum distribution total number events assumed within period. Muller Hinz (2018) pointed overlapping temporal intervals related period categorization dating accuracy likely bias analysis. proposed weighting method overcome problem. method implemented (moment), see aoristAAR package.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Aoristic Analysis \u2014 aoristic","text":"Crema, E. R. (2012). Modelling Temporal Uncertainty Archaeological Analysis. Journal Archaeological Method Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3 . Johnson, . (2004). Aoristic Analysis: Seeds New Approach Mapping Archaeological Distributions Time. Ausserer, K. F., B\u00f6rner, W., Goriany, M. & Karlhuber-V\u00f6ckl, L. (ed.), Enter Past - E-Way Four Dimensions Cultural Heritage, Oxford: Archaeopress, p. 448-52. BAR International Series 1227. doi:10.15496/publikation-2085 M\u00fcller-Schee\u00dfel, N. & Hinz, M. (2018). Aoristic Research R: Correcting Temporal Categorizations Archaeology. Presented Human History Digital Future (CAA 2018), Tubingen, March 21. https://www.youtube.com/watch?v=bUBukex30QI. Palmisano, ., Bevan, . & Shennan, S. (2017). Comparing Archaeological Proxies Long-Term Population Patterns: Example Central Italy. Journal Archaeological Science, 87: 59-72. doi:10.1016/j.jas.2017.10.001 . Ratcliffe, J. H. (2000). Aoristic Analysis: Spatial Interpretation Unspecific Temporal Events. International Journal Geographical Information Science, 14(7): 669-79. doi:10.1080/136588100424963 . Ratcliffe, J. H. (2002). Aoristic Signatures Spatio-Temporal Analysis High Volume Crime Patterns. Journal Quantitative Criminology, 18(1): 23-43. doi:10.1023/:1013240828824 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Aoristic Analysis \u2014 aoristic","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aoristic Analysis \u2014 aoristic","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Chronological Apportioning \u2014 apportion","title":"Chronological Apportioning \u2014 apportion","text":"Chronological Apportioning","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chronological Apportioning \u2014 apportion","text":"","code":"apportion(object, ...)  # S4 method for data.frame apportion(   object,   s0,   s1,   t0,   t1,   from = min(s0),   to = max(s1),   step = 25,   method = c(\"uniform\", \"truncated\"),   z = 2,   progress = getOption(\"kairos.progress\") )  # S4 method for matrix apportion(   object,   s0,   s1,   t0,   t1,   from = min(s0),   to = max(s1),   step = 25,   method = c(\"uniform\", \"truncated\"),   z = 2,   progress = getOption(\"kairos.progress\") )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chronological Apportioning \u2014 apportion","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. s0 length-\\(m\\) numeric vector giving site beginning dates expressed CE years (BCE years must given negative numbers). s1 length-\\(m\\) numeric vector giving site end dates expressed CE years (BCE years must given negative numbers). t0 length-\\(p\\) numeric vector giving type beginning dates expressed CE years (BCE years must given negative numbers). t1 length-\\(p\\) numeric vector giving type end dates expressed CE years (BCE years must given negative numbers). length-one numeric vector giving beginning period interest (years CE). length-one numeric vector giving end period interest (years CE). step length-one integer vector giving step size, .e. width time step apportioning (years CE; defaults \\(25\\)). method character string specifying distribution used (type popularity curve). must one \"uniform\" (uniform distribution) \"truncated\" (truncated standard normal distribution). unambiguous substring can given. z integer value giving lower upper truncation points (defaults \\(2\\)). used method \"truncated\". progress logical scalar: progress bar displayed?","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Chronological Apportioning \u2014 apportion","text":"CountApportion object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chronological Apportioning \u2014 apportion","text":"Roberts, J. M., Mills, B. J., Clark, J. J., Haas, W. R., Huntley, D. L. & Trowbridge, M. . (2012). Method Chronological Apportioning Ceramic Assemblages. Journal Archaeological Science, 39(5): 1513-20. doi:10.1016/j.jas.2011.12.022 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Chronological Apportioning \u2014 apportion","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/data.frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Data Frame \u2014 data.frame","title":"Coerce to a Data Frame \u2014 data.frame","text":"Coerce Data Frame","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/data.frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Data Frame \u2014 data.frame","text":"","code":"# S4 method for MeanDate as.data.frame(x, ..., calendar = getOption(\"kairos.calendar\"))  # S4 method for AoristicSum as.data.frame(x, ..., calendar = getOption(\"kairos.calendar\"))  # S4 method for IncrementTest as.data.frame(x, row.names = NULL, optional = FALSE, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/data.frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Data Frame \u2014 data.frame","text":"x object. ... parameters passed data.frame(). calendar TimeScale object specifying target calendar (see calendar()). NULL, rata die returned. row.names, optional Currently used.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/data.frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Data Frame \u2014 data.frame","text":"data.frame extra time column giving (decimal) years time series sampled.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/data.frame.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Data Frame \u2014 data.frame","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":null,"dir":"Reference","previous_headings":"","what":"Event and Accumulation Dates \u2014 event","title":"Event and Accumulation Dates \u2014 event","text":"Fits date event model.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Event and Accumulation Dates \u2014 event","text":"","code":"event(object, dates, ...)  # S4 method for data.frame,numeric event(object, dates, rank = NULL, sup_row = NULL, calendar = CE(), ...)  # S4 method for matrix,numeric event(object, dates, rank = NULL, sup_row = NULL, calendar = CE(), ...)  # S4 method for EventDate summary(object, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Event and Accumulation Dates \u2014 event","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates numeric vector dates. named, names must match row names object. ... arguments passed internal methods. rank integer specifying number CA factorial components use linear model fitting (see details). NULL (default), axes corresponding least 60% inertia used. sup_row numeric logical vector specifying indices supplementary rows. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Event and Accumulation Dates \u2014 event","text":"EventDate object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Event and Accumulation Dates \u2014 event","text":"implementation chronological modeling method proposed Bellanger Husi (2012, 2013). Event accumulation dates density estimates occupation duration archaeological site (Bellanger Husi 2012, 2013). event date estimation terminus post-quem archaeological assemblage. accumulation date represents \"chronological profile\" assemblage. According Bellanger Husi (2012), accumulation date can interpreted \"best [...] formation process reflecting duration succession events scale archaeological time, worst, imprecise dating due contamination context residual intrusive material.\" words, accumulation dates estimate occurrence archaeological events rhythms long term. Dates converted rata die computation. method relies strong archaeological statistical assumptions (see vignette(\"event\")).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Event and Accumulation Dates \u2014 event","text":"Bellanger, L. & Husi, P. (2013). Mesurer et mod\u00e9liser le temps inscrit dans la mati\u00e8re \u00e0 partir d'une source mat\u00e9rielle : la c\u00e9ramique m\u00e9di\u00e9vale. Mesure et Histoire M\u00e9di\u00e9vale. Histoire ancienne et m\u00e9di\u00e9vale. Paris: Publication de la Sorbonne, p. 119-134. Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Bellanger, L., Tomassone, R. & Husi, P. (2008). Statistical Approach Dating Archaeological Contexts. Journal Data Science, 6, 135-154. Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes arch\u00e9ologiques. Revue de Statistique Appliqu\u00e9e, 54(2), 65-81. Bellanger, L., Husi, P. & Tomassone, R. (2006). Statistical Aspects Pottery Quantification Dating Archaeological Contexts. Archaeometry, 48(1), 169-183. doi:10.1111/j.1475-4754.2006.00249.x . Poblome, J. & Groenen, P. J. F. (2003). Constrained Correspondence Analysis Seriation Sagalassos Tablewares. Doerr, M. & Apostolis, S. (eds.), Digital Heritage Archaeology. Athens: Hellenic Ministry Culture.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Event and Accumulation Dates \u2014 event","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Event and Accumulation Dates \u2014 event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Frequency Increment Test \u2014 fit","title":"Frequency Increment Test \u2014 fit","text":"Frequency Increment Test","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Frequency Increment Test \u2014 fit","text":"","code":"fit(object, dates, ...)  # S4 method for data.frame,numeric fit(object, dates, calendar = CE(), level = 0.95, roll = FALSE, window = 3)  # S4 method for matrix,numeric fit(object, dates, calendar = CE(), level = 0.95, roll = FALSE, window = 3)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Frequency Increment Test \u2014 fit","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates length-\\(m\\) numeric vector dates. ... Currently used. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era. level length-one numeric vector giving confidence level. roll logical scalar: time series subsetted look episodes selection? window odd integer giving size rolling window. used roll TRUE.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Frequency Increment Test \u2014 fit","text":"IncrementTest object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Frequency Increment Test \u2014 fit","text":"Frequency Increment Test (FIT) rejects neutrality distribution normalized variant frequency increments exhibits mean deviates significantly zero. roll TRUE, time series subsetted according window see episodes selection can identified among variables might show overall selection.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Frequency Increment Test \u2014 fit","text":"Feder, . F., Kryazhimskiy, S. & Plotkin, J. B. (2014). Identifying Signatures Selection Genetic Time Series. Genetics, 196(2): 509-522. doi:10.1534/genetics.113.158220 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Frequency Increment Test \u2014 fit","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Frequency Increment Test \u2014 fit","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Group by phase ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Frequency Increment Test freq <- fit(counts, dates, calendar = NULL)  ## Plot time vs abundance plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")   ## Plot time vs abundance and highlight selection freq <- fit(counts, dates, calendar = NULL, roll = TRUE, window = 5) plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/kairos-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in kairos \u2014 kairos-deprecated","title":"Deprecated Functions in kairos \u2014 kairos-deprecated","text":"functions still work removed (defunct) next version.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/kairos-package.html","id":null,"dir":"Reference","previous_headings":"","what":"kairos: Analysis of Chronological Patterns from Archaeological Count Data \u2014 kairos-package","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data \u2014 kairos-package","text":"toolkit absolute relative dating analysis chronological patterns. package includes functions chronological modeling dating archaeological assemblages count data. provides methods matrix seriation. also allows compute time point estimates density estimates occupation duration archaeological site.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/kairos-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data \u2014 kairos-package","text":"kairos uses following options() configure behavior: kairos.progress: logical scalar. progress bars displayed?","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/kairos-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data \u2014 kairos-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Ceramic Date \u2014 mcd","title":"Mean Ceramic Date \u2014 mcd","text":"Estimates Mean Ceramic Date assemblage.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean Ceramic Date \u2014 mcd","text":"","code":"mcd(object, dates, ...)  # S4 method for numeric,numeric mcd(object, dates, calendar = CE())  # S4 method for data.frame,numeric mcd(object, dates, calendar = CE())  # S4 method for matrix,numeric mcd(object, dates, calendar = CE())","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean Ceramic Date \u2014 mcd","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates length-\\(p\\) numeric vector dates expressed years. ... Currently used. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean Ceramic Date \u2014 mcd","text":"MeanDate object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mean Ceramic Date \u2014 mcd","text":"Mean Ceramic Date (MCD) point estimate occupation archaeological site (South 1977). MCD estimated weighted mean date midpoints ceramic types (based absolute dates known production interval) found given assemblage. weights relative frequencies respective types assemblage. bootstrapping procedure used estimate confidence interval given MCD. assemblage, large number new bootstrap replicates created, sample size, resampling original assemblage replacement. MCDs calculated replicates upper lower boundaries confidence interval associated MCD returned.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mean Ceramic Date \u2014 mcd","text":"South, S. . (1977). Method Theory Historical Archaeology. New York: Academic Press.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean Ceramic Date \u2014 mcd","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean Ceramic Date \u2014 mcd","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Set the start and end dates for each ceramic type dates <- list(   LINO = c(600, 875), KIAT = c(850, 950), RED = c(900, 1050),   GALL = c(1025, 1125), ESC = c(1050, 1150), PUBW = c(1050, 1150),   RES = c(1000, 1200), TULA = c(1175, 1300), PINE = c(1275, 1350),   PUBR = c(1000, 1200), WING = c(1100, 1200), WIPO = c(1125, 1225),   SJ = c(1200, 1300), LSJ = c(1250, 1300), SPR = c(1250, 1300),   PINER = c(1275, 1325), HESH = c(1275, 1450), KWAK = c(1275, 1450) )  ## Calculate date midpoints mid <- vapply(X = dates, FUN = mean, FUN.VALUE = numeric(1))  ## Calculate MCD (mc_dates <- mcd(zuni[100:125, ], dates = mid)) #> 26 x 18 x 1 time series observed between 276230.3 and 459613.1 r.d.  ## Get MCD in years CE time(mc_dates, calendar = CE()) #>  [1]  757.2912  796.6659  797.4991  952.5855  996.2952 1016.0738 1027.5011 #>  [8] 1059.5249 1073.6597 1075.5213 1089.5820 1092.8564 1100.0000 1127.7799 #> [15] 1137.1101 1200.0017 1204.3868 1207.1436 1219.4454 1227.3745 1235.4176 #> [22] 1237.5000 1238.8896 1253.1241 1256.2502 1259.3757  ## Plot plot(mc_dates)   ## Bootstrap resampling boot <- bootstrap(mc_dates, n = 30) head(boot) #>         original      mean     bias     error #> LZ0789  757.2917       NaN      NaN        NA #> LZ0783  796.6667  889.8714 93.20476 134.61587 #> LZ0782  797.5000  848.1494 50.64936 106.47415 #> LZ0778  952.5862  979.3251 26.73888 120.13747 #> LZ0777  996.2963 1012.4836 16.18726 107.80484 #> LZ0776 1016.0714 1038.9499 22.87846  83.47433  ## Jackknife resampling jack <- jackknife(mc_dates) head(jack) #>         original      mean       bias    error #> LZ0789  757.2917  768.2870 186.921296 207.5535 #> LZ0783  796.6667  806.9974 175.621693 228.0861 #> LZ0782  797.5000  804.1715 113.415558 169.0563 #> LZ0778  952.5862  954.5205  32.882529 138.6064 #> LZ0777  996.2963  996.6640   6.251785 111.0144 #> LZ0776 1016.0714 1017.1652  18.594831  72.6602  ## Simulation sim <- simulate(mc_dates, nsim = 30) plot(sim, interval = \"range\", pch = 16)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/model.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Model Results \u2014 model","title":"Extract Model Results \u2014 model","text":"coef() extracts model coefficients (see stats::coef()). fitted() extracts model fitted values (see stats::fitted()). residuals() extracts model residuals (see stats::residuals()). sigma() extracts residual standard deviation (see stats::sigma()). terms() extracts model terms (see stats::terms()).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Model Results \u2014 model","text":"","code":"# S4 method for EventDate coef(object, calendar = NULL, ...)  # S4 method for EventDate fitted(object, calendar = NULL, ...)  # S4 method for EventDate residuals(object, calendar = NULL, ...)  # S4 method for EventDate sigma(object, calendar = NULL, ...)  # S4 method for EventDate terms(x, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Model Results \u2014 model","text":"calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned. ... Currently used. x, object EventDate object.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/model.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Model Results \u2014 model","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Model Results \u2014 model","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mutators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or Set Parts of an Object \u2014 mutators","title":"Get or Set Parts of an Object \u2014 mutators","text":"Getters setters retrieve set parts object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mutators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get or Set Parts of an Object \u2014 mutators","text":"","code":"# S4 method for AoristicSum weights(object, ...)  # S4 method for CountApportion weights(object, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mutators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or Set Parts of an Object \u2014 mutators","text":"object object get set element(s). ... Currently used.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mutators.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get or Set Parts of an Object \u2014 mutators","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":null,"dir":"Reference","previous_headings":"","what":"Rearranges a Data Matrix \u2014 permute","title":"Rearranges a Data Matrix \u2014 permute","text":"permute() rearranges data matrix according permutation order. get_order() returns seriation order rows /columns.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rearranges a Data Matrix \u2014 permute","text":"","code":"permute(object, order, ...)  get_order(x, ...)  # S4 method for data.frame,PermutationOrder permute(object, order)  # S4 method for matrix,PermutationOrder permute(object, order)  # S4 method for PermutationOrder get_order(x, margin = c(1, 2))","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rearranges a Data Matrix \u2014 permute","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. x, order PermutationOrder object giving permutation order rows columns. margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rearranges a Data Matrix \u2014 permute","text":"permute() returns permuted matrix permuted data.frame (object).","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rearranges a Data Matrix \u2014 permute","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rearranges a Data Matrix \u2014 permute","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #> <RankPermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Aoristic Analysis \u2014 plot_aoristic","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"Plot Aoristic Analysis","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"","code":"# S4 method for AoristicSum,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   type = c(\"bar\"),   flip = FALSE,   ncol = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for AoristicSum image(x, calendar = getOption(\"kairos.calendar\"), ...)  # S4 method for RateOfChange,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   level = 0.95,   flip = FALSE,   ncol = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"x AoristicSum object. calendar TimeScale object specifying target calendar (see calendar()). type character string specifying whether bar density plotted? must one \"bar\" \"density\". unambiguous substring can given. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... parameters passed panel (e.g. graphical parameters). level length-one numeric vector giving confidence level.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Event Plot \u2014 plot_event","title":"Event Plot \u2014 plot_event","text":"Produces activity tempo plot.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Event Plot \u2014 plot_event","text":"","code":"# S4 method for EventDate,missing plot(   x,   type = c(\"activity\", \"tempo\"),   event = FALSE,   calendar = getOption(\"kairos.calendar\"),   select = 1,   n = 500,   eps = 1e-09,   col.accumulation = \"black\",   col.event = \"red\",   flip = FALSE,   ncol = NULL,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   ... )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Event Plot \u2014 plot_event","text":"x EventDate object. type character string indicating type plot. must one \"activity\" (default) \"tempo\" (see details). unambiguous substring can given. event logical scalar: distribution event date displayed? used type \"activity\". calendar TimeScale object specifying target calendar (see calendar()). select numeric character vector giving selection assemblage drawn. n length-one non-negative numeric vector giving desired length vector quantiles density computation. eps length-one numeric value giving cutoff values removed. col.accumulation color specification accumulation density curve. col.event color specification event density curve. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? ... parameters passed panel (e.g. graphical parameters).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Event Plot \u2014 plot_event","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"event-and-acccumulation-dates","dir":"Reference","previous_headings":"","what":"Event and Acccumulation Dates","title":"Event Plot \u2014 plot_event","text":"plot() displays probability estimate density curves archaeological assemblage dates (event accumulation dates; Bellanger Husi 2012). event date plotted line, accumulation date shown grey filled area. accumulation date can displayed tempo plot (Dye 2016) activity plot (Philippe Vibet 2020): tempo tempo plot estimates cumulative occurrence archaeological events, slope plot directly reflects pace change. activity activity plot displays first derivative tempo plot.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Event Plot \u2014 plot_event","text":"Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Dye, T. S. (2016). Long-Term Rhythms Development Hawaiian Social Stratification. Journal Archaeological Science, 71, 1-9. doi:10.1016/j.jas.2016.05.006 . Philippe, . & Vibet, M.-. (2020). Analysis Archaeological Phases Using R Package ArchaeoPhases. Journal Statistical Software, Code Snippets, 93(1), 1-25. doi:10.18637/jss.v093.c01 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Event Plot \u2014 plot_event","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Event Plot \u2014 plot_event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection of Selective Processes \u2014 plot_fit","title":"Detection of Selective Processes \u2014 plot_fit","text":"Produces abundance vs time diagram.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection of Selective Processes \u2014 plot_fit","text":"","code":"# S4 method for IncrementTest,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   col.neutral = \"#004488\",   col.selection = \"#BB5566\",   col.roll = \"grey\",   flip = FALSE,   ncol = NULL,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   ... )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection of Selective Processes \u2014 plot_fit","text":"x IncrementTest object plotted. calendar TimeScale object specifying target calendar (see calendar()). col.neutral, col.selection, col.roll vector colors. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? ... parameters passed panel (e.g. graphical parameters).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detection of Selective Processes \u2014 plot_fit","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Detection of Selective Processes \u2014 plot_fit","text":"Results frequency increment test can displayed abundance vs time diagram aid detection quantification selective processes archaeological record. roll TRUE, time series subsetted according window see episodes selection can identified among decoration types might show overall selection. , shading highlights data points fit() identifies selection.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Detection of Selective Processes \u2014 plot_fit","text":"Displaying FIT results abundance vs time diagram adapted Ben Marwick's original idea.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection of Selective Processes \u2014 plot_fit","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detection of Selective Processes \u2014 plot_fit","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Group by phase ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Frequency Increment Test freq <- fit(counts, dates, calendar = NULL)  ## Plot time vs abundance plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")   ## Plot time vs abundance and highlight selection freq <- fit(counts, dates, calendar = NULL, roll = TRUE, window = 5) plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"MCD Plot \u2014 plot_mcd","title":"MCD Plot \u2014 plot_mcd","text":"MCD Plot","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MCD Plot \u2014 plot_mcd","text":"","code":"# S4 method for MeanDate,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   decreasing = TRUE,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for SimulationMeanDate,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   interval = \"student\",   level = 0.8,   decreasing = TRUE,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MCD Plot \u2014 plot_mcd","text":"x MeanDate object. calendar TimeScale object specifying target calendar (see calendar()). decreasing logical scalar: sort increasing decreasing? main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters. interval character string giving type confidence interval returned. must one \"student\" (default), \"normal\", \"percentiles\" \"range\" (min-max). unambiguous substring can given. level length-one numeric vector giving confidence level. used interval \"range\".","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MCD Plot \u2014 plot_mcd","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"MCD Plot \u2014 plot_mcd","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MCD Plot \u2014 plot_mcd","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Set the start and end dates for each ceramic type dates <- list(   LINO = c(600, 875), KIAT = c(850, 950), RED = c(900, 1050),   GALL = c(1025, 1125), ESC = c(1050, 1150), PUBW = c(1050, 1150),   RES = c(1000, 1200), TULA = c(1175, 1300), PINE = c(1275, 1350),   PUBR = c(1000, 1200), WING = c(1100, 1200), WIPO = c(1125, 1225),   SJ = c(1200, 1300), LSJ = c(1250, 1300), SPR = c(1250, 1300),   PINER = c(1275, 1325), HESH = c(1275, 1450), KWAK = c(1275, 1450) )  ## Calculate date midpoints mid <- vapply(X = dates, FUN = mean, FUN.VALUE = numeric(1))  ## Calculate MCD (mc_dates <- mcd(zuni[100:125, ], dates = mid)) #> 26 x 18 x 1 time series observed between 276230.3 and 459613.1 r.d.  ## Get MCD in years CE time(mc_dates, calendar = CE()) #>  [1]  757.2912  796.6659  797.4991  952.5855  996.2952 1016.0738 1027.5011 #>  [8] 1059.5249 1073.6597 1075.5213 1089.5820 1092.8564 1100.0000 1127.7799 #> [15] 1137.1101 1200.0017 1204.3868 1207.1436 1219.4454 1227.3745 1235.4176 #> [22] 1237.5000 1238.8896 1253.1241 1256.2502 1259.3757  ## Plot plot(mc_dates)   ## Bootstrap resampling boot <- bootstrap(mc_dates, n = 30) head(boot) #>         original      mean     bias     error #> LZ0789  757.2917       NaN      NaN        NA #> LZ0783  796.6667  886.4197 89.75308 126.38757 #> LZ0782  797.5000       NaN      NaN        NA #> LZ0778  952.5862  987.3124 34.72619  96.21157 #> LZ0777  996.2963 1011.8145 15.51823  85.04925 #> LZ0776 1016.0714 1058.7937 42.72230  75.51616  ## Jackknife resampling jack <- jackknife(mc_dates) head(jack) #>         original      mean       bias    error #> LZ0789  757.2917  768.2870 186.921296 207.5535 #> LZ0783  796.6667  806.9974 175.621693 228.0861 #> LZ0782  797.5000  804.1715 113.415558 169.0563 #> LZ0778  952.5862  954.5205  32.882529 138.6064 #> LZ0777  996.2963  996.6640   6.251785 111.0144 #> LZ0776 1016.0714 1017.1652  18.594831  72.6602  ## Simulation sim <- simulate(mc_dates, nsim = 30) plot(sim, interval = \"range\", pch = 16)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Abundance vs Time Plot \u2014 plot_time","title":"Abundance vs Time Plot \u2014 plot_time","text":"Produces abundance vs time diagram.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abundance vs Time Plot \u2014 plot_time","text":"","code":"plot_time(object, dates, ...)  # S4 method for data.frame,numeric plot_time(object, dates, calendar = getOption(\"kairos.calendar\"), ...)  # S4 method for matrix,numeric plot_time(object, dates, calendar = getOption(\"kairos.calendar\"), ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abundance vs Time Plot \u2014 plot_time","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates numeric vector dates. ... parameters passed aion::plot(). calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Abundance vs Time Plot \u2014 plot_time","text":"plot_time() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Abundance vs Time Plot \u2014 plot_time","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Abundance vs Time Plot \u2014 plot_time","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Coerce the merzbach dataset to a count matrix ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Set dates ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Plot abundance vs time plot_time(counts, dates, calendar = NULL, ncol = 3, xlab = \"Phases\")","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Event and Accumulation Dates \u2014 predict_event","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"Estimates event accumulation dates assemblage.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"","code":"predict_event(object, data, ...)  predict_accumulation(object, data, ...)  # S4 method for EventDate,missing predict_event(object, margin = 1, level = 0.95, calendar = NULL)  # S4 method for EventDate,matrix predict_event(object, data, margin = 1, level = 0.95, calendar = NULL)  # S4 method for EventDate,missing predict_accumulation(object, calendar = NULL)  # S4 method for EventDate,matrix predict_accumulation(object, data, level = 0.95, calendar = NULL)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). data numeric matrix data.frame count data (absolute frequencies) predict event accumulation dates. ... arguments passed internal methods. margin numeric vector giving subscripts prediction applied : 1 indicates rows, 2 indicates columns. level length-one numeric vector giving confidence level. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"predict_event() returns data.frame. predict_accumulation() returns data.frame.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"Bellanger, L. & Husi, P. (2013). Mesurer et mod\u00e9liser le temps inscrit dans la mati\u00e8re \u00e0 partir d'une source mat\u00e9rielle : la c\u00e9ramique m\u00e9di\u00e9vale. Mesure et Histoire M\u00e9di\u00e9vale. Histoire ancienne et m\u00e9di\u00e9vale. Paris: Publication de la Sorbonne, p. 119-134. Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Bellanger, L., Tomassone, R. & Husi, P. (2008). Statistical Approach Dating Archaeological Contexts. Journal Data Science, 6, 135-154. Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes arch\u00e9ologiques. Revue de Statistique Appliqu\u00e9e, 54(2), 65-81. Bellanger, L., Husi, P. & Tomassone, R. (2006). Statistical Aspects Pottery Quantification Dating Archaeological Contexts. Archaeometry, 48(1), 169-183. doi:10.1111/j.1475-4754.2006.00249.x .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages \u2014 reexports","title":"Objects exported from other packages \u2014 reexports","text":"objects imported packages. Follow links see documentation. aion AD, BC, BCE, BP, CE, b2k, calendar, start, start, time, year_axis arkhe bootstrap, jackknife, remove_NA, remove_zero, replace_NA, replace_zero","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample Event Dates \u2014 resample_event","title":"Resample Event Dates \u2014 resample_event","text":"bootstrap() generate bootstrap estimations event. jackknife() generate jackknife estimations event.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample Event Dates \u2014 resample_event","text":"","code":"# S4 method for EventDate jackknife(object, level = 0.95, progress = getOption(\"kairos.progress\"), ...)  # S4 method for EventDate bootstrap(   object,   level = 0.95,   probs = c(0.05, 0.95),   n = 1000,   progress = getOption(\"kairos.progress\"),   ... )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample Event Dates \u2014 resample_event","text":"object EventDate object (typically returned event()). level length-one numeric vector giving confidence level. progress logical scalar: progress bar displayed? ... arguments passed internal methods. probs numeric vector probabilities values \\([0,1]\\). n non-negative integer specifying number bootstrap replications.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample Event Dates \u2014 resample_event","text":"data.frame.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Resample Event Dates \u2014 resample_event","text":"jackknife() used, one type/fabric removed time statistics recalculated. way, one can assess whether certain type/fabric substantial influence date estimate. three columns data.frame returned, giving results resampling procedure (jackknifing fabrics) assemblage (rows) following columns: mean jackknife mean (event date). bias jackknife estimate bias. error standard error predicted means. bootstrap() used, large number new bootstrap assemblages created, sample size, resampling original assemblage replacement. , examination bootstrap statistics makes possible pinpoint assemblages require investigation. five columns data.frame returned, giving bootstrap distribution statistics replicated assemblage (rows) following columns: min Minimum value. mean Mean value (event date). max Maximum value. Q5 Sample quantile 0.05 probability. Q95 Sample quantile 0.95 probability.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample Event Dates \u2014 resample_event","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample Mean Ceramic Dates \u2014 resample_mcd","title":"Resample Mean Ceramic Dates \u2014 resample_mcd","text":"bootstrap() generate bootstrap estimations MCD. jackknife() generate jackknife estimations MCD.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample Mean Ceramic Dates \u2014 resample_mcd","text":"","code":"# S4 method for MeanDate bootstrap(object, n = 1000, f = NULL, calendar = getOption(\"kairos.calendar\"))  # S4 method for MeanDate jackknife(object, f = NULL, calendar = getOption(\"kairos.calendar\"))  # S4 method for MeanDate simulate(object, nsim = 1000)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample Mean Ceramic Dates \u2014 resample_mcd","text":"object MeanDate object (typically returned mcd()). n non-negative integer specifying number bootstrap replications. f function takes single numeric vector (result resampling procedure) argument. calendar TimeScale object specifying target calendar (see calendar()). nsim non-negative integer specifying number simulations.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample Mean Ceramic Dates \u2014 resample_mcd","text":"f NULL, bootstrap() jackknife() return data.frame following elements (else, returns result f applied n resampled values) : original observed value. mean bootstrap/jackknife estimate mean. bias bootstrap/jackknife estimate bias. error boostrap/jackknife estimate standard erro.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample Mean Ceramic Dates \u2014 resample_mcd","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate of Change \u2014 roc","title":"Rate of Change \u2014 roc","text":"Computes rate change aoristic analysis.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate of Change \u2014 roc","text":"","code":"roc(object, ...)  # S4 method for AoristicSum roc(object, n = 100)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate of Change \u2014 roc","text":"object AoristicSum object. ... Currently used. n non-negative integer giving number replications (see details).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate of Change \u2014 roc","text":"RateOfChange object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rate of Change \u2014 roc","text":"Baxter, M. J. & Cool, H. E. M. (2016). Reinventing Wheel? Modelling Temporal Uncertainty Applications Brooch Distributions Roman Britain. Journal Archaeological Science, 66: 120-27. doi:10.1016/j.jas.2015.12.007 . Crema, E. R. (2012). Modelling Temporal Uncertainty Archaeological Analysis. Journal Archaeological Method Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rate of Change \u2014 roc","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rate of Change \u2014 roc","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Correspondence Analysis-Based Seriation \u2014 seriate_average","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"Correspondence Analysis-Based Seriation","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"","code":"seriate_average(object, ...)  # S4 method for data.frame seriate_average(object, margin = c(1, 2), axes = 1, ...)  # S4 method for matrix seriate_average(object, margin = c(1, 2), axes = 1, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns, c(2, 1) indicates columns rows. axes integer vector giving subscripts CA axes used.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"AveragePermutationOrder object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"Correspondence analysis (CA) effective method seriation archaeological assemblages. order rows columns given coordinates along one dimension CA space, assumed account temporal variation. direction temporal change within correspondence analysis space arbitrary: additional information needed determine actual order time.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"Ihm, P. (2005). Contribution History Seriation Archaeology. C. Weihs & W. Gaul (Eds.), Classification: Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. doi:10.1007/3-540-28084-7_34 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #> <RankPermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Reciprocal Ranking Seriation \u2014 seriate_rank","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"Reciprocal Ranking Seriation","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"","code":"seriate_rank(object, ...)  # S4 method for data.frame seriate_rank(object, EPPM = FALSE, margin = c(1, 2), stop = 100)  # S4 method for matrix seriate_rank(object, EPPM = FALSE, margin = c(1, 2), stop = 100)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. EPPM logical scalar: seriation computed EPPM instead raw data? margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns, c(2, 1) indicates columns rows. stop integer giving stopping rule (.e. maximum number iterations) avoid infinite loop.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"RankPermutationOrder object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"procedure iteratively rearrange rows /columns according weighted rank data matrix convergence. Note procedure enter infinite loop. convergence reached maximum number iterations, stops warning.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"Desachy, B. (2004). Le s\u00e9riographe EPPM: un outil informatis\u00e9 de s\u00e9riation graphique pour tableaux de comptages. Revue arch\u00e9ologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 . Dunnell, R. C. (1970). Seriation Method Evaluation. American Antiquity, 35(03), 305-319. doi:10.2307/278341 . Ihm, P. (2005). Contribution History Seriation Archaeology. C. Weihs & W. Gaul (Eds.), Classification: Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. doi:10.1007/3-540-28084-7_34 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #> <RankPermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":null,"dir":"Reference","previous_headings":"","what":"Refine CA-based Seriation \u2014 seriate_refine","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"Refine CA-based Seriation","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"","code":"seriate_refine(object, ...)  # S4 method for AveragePermutationOrder seriate_refine(object, cutoff, margin = 1, axes = 1, n = 30, ...)  # S4 method for BootstrapCA seriate_refine(object, cutoff, margin = 1, axes = 1, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"object PermutationOrder object (typically returned seriate_average()). ... arguments passed internal methods. cutoff function takes numeric vector argument returns single numeric value (see ). margin length-one numeric vector giving subscripts refinement applied : 1 indicates rows, 2 indicates columns. axes integer vector giving subscripts CA axes used. n non-negative integer giving number bootstrap replications.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"RefinePermutationOrder object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"seriate_refine() allows identify samples subject sampling error samples underlying structural relationships might influencing ordering along CA space. relies partial bootstrap approach CA-based seriation sample replicated n times. maximum dimension length convex hull around sample point cloud allows remove samples given cutoff value. According Peebles Schachner (2012), \"[] point removal procedure [results ] reduced dataset position individuals within CA highly stable produces ordering consistent assumptions frequency seriation.\"","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"Peeples, M. ., & Schachner, G. (2012). Refining correspondence analysis-based ceramic seriation regional data sets. Journal Archaeological Science, 39(8), 2818-2827. doi:10.1016/j.jas.2012.04.040 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/series.html","id":null,"dir":"Reference","previous_headings":"","what":"Sampling Times \u2014 series","title":"Sampling Times \u2014 series","text":"Get times time series sampled.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sampling Times \u2014 series","text":"","code":"# S4 method for EventDate time(x, calendar = NULL)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sampling Times \u2014 series","text":"x R object. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sampling Times \u2014 series","text":"numeric vector.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/series.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sampling Times \u2014 series","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object \u2014 subset","title":"Extract or Replace Parts of an Object \u2014 subset","text":"Operators acting objects extract replace parts.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object \u2014 subset","text":"","code":"# S4 method for MeanDate [(x, i, j, k, drop = FALSE)  # S4 method for IncrementTest [(x, i, j, k, drop = FALSE)  # S4 method for PermutationOrder,ANY,missing [[(x, i)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object \u2014 subset","text":"x object extract element(s) replace element(s). , j, k Indices specifying elements extract replace. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object \u2014 subset","text":"subsetted object.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object \u2014 subset","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"do-pca","dir":"Articles","previous_headings":"","what":"Do PCA","title":"Principal Components Analysis","text":"","code":"## Load data data(iris) head(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa  ## Compute PCA ## (non numeric variables are automatically removed) X <- pca(iris, center = TRUE, scale = TRUE) #> 1 qualitative variable was removed: Species.","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"explore-the-results","dir":"Articles","previous_headings":"","what":"Explore the results","title":"Principal Components Analysis","text":"dimensio provides several methods extract (get_*()) results: get_data() returns original data. get_contributions() returns contributions definition principal dimensions. get_coordinates() returns principal standard coordinates. get_correlations() returns correlations variables dimensions. get_cos2() returns cos2 values (.e.\u00a0quality representation points factor map). get_eigenvalues() returns eigenvalues, percentages variance cumulative percentages variance. package also allows quickly visualize (viz_*()) results: biplot() produces biplot. screeplot() produces scree plot. viz_rows()/viz_individuals() displays row/individual principal coordinates. viz_columns()/viz_variables() displays columns/variable principal coordinates. viz_contributions() displays (joint) contributions. viz_cos2() displays (joint) cos2.","code":"## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.9184978 73.342264   73.34226 #> F2   0.9140305 22.969715   96.31198 #> F3   0.1467569  3.688021  100.00000  ## Scree plot screeplot(X, cumulative = TRUE)  ## Plot variable contributions to the definition of the first two axes viz_contributions(X, margin = 2, axes = c(1, 2))","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"pca-biplot","dir":"Articles","previous_headings":"","what":"PCA biplot","title":"Principal Components Analysis","text":"biplot simultaneous representation rows columns rectangular dataset. generalization scatterplot case mutlivariate data: allows visualize much information possible single graph (Greenacre 2010). dimensio allows display two types biplots: form biplot (row-metric-preserving biplot) covariance biplot (column-metric-preserving biplot). See Greenacre (2010) details biplots. form biplot favors representation individuals: distance individuals approximates Euclidean distance rows. form biplot length vector approximates quality representation variable.  covariance biplot favors representation variables: length vector approximates standard deviation variable cosine angle formed two vectors approximates correlation two variables (Greenacre 2010). covariance biplot distance individuals approximates Mahalanobis distance rows.  Biplots drawbacks advantages: can quickly become difficult read display lot information . may preferable visualize results individuals variables separately.","code":"biplot(X, type = \"form\", label = \"variables\") biplot(X, type = \"covariance\", label = \"variables\")","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"plot-pca-loadings","dir":"Articles","previous_headings":"","what":"Plot PCA loadings","title":"Principal Components Analysis","text":"viz_variables() depicts variables rays emanating origin (lengths directions important interpretation).  viz_variables() allows highlight additional information varying different graphical elements (color, transparency, shape size symbols\u2026).","code":"## Plot variables factor map viz_variables(X) ## Highlight cos2 viz_variables(   x = X,    highlight = \"cos2\",    col = khroma::color(\"YlOrBr\")(4, range = c(0.5, 1)) )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"plot-pca-scores","dir":"Articles","previous_headings":"","what":"Plot PCA scores","title":"Principal Components Analysis","text":"viz_individuals() allows display individuals highlight additional information.","code":"## Plot individuals and color by species viz_individuals(   x = X,   highlight = iris$Species,   col = khroma::color(\"bright\")(3), # Custom color scale   pch = c(15, 16, 17) # Custom symbols ) ## Add ellipses viz_individuals(x = X) viz_tolerance(x = X, group = iris$Species, level = 0.95,               border = khroma::color(\"high contrast\")(3))  ## Add convex hull viz_individuals(x = X) viz_hull(x = X, group = iris$Species, level = 0.95,          border = khroma::color(\"high contrast\")(3)) ## Highlight petal length viz_individuals(   x = X,    highlight = iris$Petal.Length,   col = khroma::color(\"YlOrBr\")(12), # Custom color scale   cex = c(1, 2), # Custom size scale   pch = 16 ) ## Highlight contributions viz_individuals(   x = X,    highlight = \"contrib\",   col = khroma::color(\"iridescent\")(12), # Custom color scale   cex = c(1, 2), # Custom size scale   pch = 16 )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"custom-plot","dir":"Articles","previous_headings":"","what":"Custom plot","title":"Principal Components Analysis","text":"need flexibility, get_*() family tidy() augment() functions allow extract results data frames thus build custom graphs base graphics ggplot2.","code":"iris_tidy <- tidy(X, margin = 2) head(iris_tidy) #>          label component supplementary coordinate contribution        cos2 #> 1 Petal.Length        F1         FALSE 0.99155518  33.68793618 0.983181682 #> 2 Petal.Length        F2         FALSE 0.02341519   0.05998389 0.000548271 #> 3 Petal.Length        F3         FALSE 0.05444699   2.01999049 0.002964475 #> 4  Petal.Width        F1         FALSE 0.96497896  31.90629060 0.931184395 #> 5  Petal.Width        F2         FALSE 0.06399985   0.44812296 0.004095980 #> 6  Petal.Width        F3         FALSE 0.24298265  40.23019050 0.059040571  iris_augment <- augment(X, margin = 1) head(iris_augment) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.264703  0.4800266     1         FALSE 0.006666667 5.359304     3.572870 #> 2 -2.080961 -0.6741336     2         FALSE 0.006666667 4.784855     3.189904 #> 3 -2.364229 -0.3419080     3         FALSE 0.006666667 5.706480     3.804320 #> 4 -2.299384 -0.5973945     4         FALSE 0.006666667 5.644048     3.762699 #> 5 -2.389842  0.6468354     5         FALSE 0.006666667 6.129742     4.086494 #> 6 -2.075631  1.4891775     6         FALSE 0.006666667 6.525894     4.350596 #>        cos2 #> 1 0.9968578 #> 2 0.9864650 #> 3 0.9995167 #> 4 0.9977577 #> 5 0.9997491 #> 6 0.9998819 ## Custom plot with ggplot2 ggplot2::ggplot(data = iris_augment) +   ggplot2::aes(x = F1, y = F2, colour = contribution) +   ggplot2::geom_vline(xintercept = 0, linewidth = 0.5, linetype = \"dashed\") +   ggplot2::geom_hline(yintercept = 0, linewidth = 0.5, linetype = \"dashed\") +   ggplot2::geom_point() +   ggplot2::coord_fixed() + # /!\\   ggplot2::theme_bw() +   khroma::scale_color_iridescent()","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Multivariate Data Analysis","text":"Simple Principal Components Analysis (PCA; see vignette(\"pca\")) Correspondence Analysis (CA) based Singular Value Decomposition (SVD). package provides S4 classes methods compute, extract, summarize visualize results multivariate data analysis. also includes methods partial bootstrap validation. many good packages multivariate data analysis (FactoMineR, ade4, vegan ca, extended FactoExtra). dimensio designed simple possible, providing necessary tools explore results analysis.","code":"To cite dimensio in publications use:    Frerebeau N (2023). _dimensio: Multivariate Data Analysis_.   Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.4478530 <https://doi.org/10.5281/zenodo.4478530>,   R package version 0.4.0, <https://packages.tesselle.org/dimensio/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{dimensio: Multivariate Data Analysis}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 0.4.0},     doi = {10.5281/zenodo.4478530},     url = {https://packages.tesselle.org/dimensio/},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Multivariate Data Analysis","text":"can install released version dimensio CRAN : development version GitHub :","code":"install.packages(\"dimensio\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/dimensio\")","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Multivariate Data Analysis","text":"","code":"## Install extra packages (if needed) # install.packages(\"khroma\")  ## Load packages library(dimensio)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"compute","dir":"","previous_headings":"Usage","what":"Compute","title":"Multivariate Data Analysis","text":"","code":"## Load data data(iris)  ## Compute PCA ## (non numeric variables are automatically removed) X <- pca(iris, center = TRUE, scale = TRUE) #> 1 qualitative variable was removed: Species.","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"extract","dir":"","previous_headings":"Usage","what":"Extract","title":"Multivariate Data Analysis","text":"dimensio provides several methods extract results: get_data() returns original data. get_contributions() returns contributions definition principal dimensions. get_coordinates() returns principal standard coordinates. get_correlations() returns correlations variables dimensions. get_cos2() returns cos2 values (.e.\u00a0quality representation points factor map). get_eigenvalues() returns eigenvalues, percentages variance cumulative percentages variance.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"visualize","dir":"","previous_headings":"Usage","what":"Visualize","title":"Multivariate Data Analysis","text":"package allows quickly visualize results: biplot() produces biplot. screeplot() produces scree plot. viz_rows()/viz_individuals() displays row/individual principal coordinates. viz_columns()/viz_variables() displays columns/variable principal coordinates. viz_variables() depicts variables rays emanating origin (lengths directions important interpretation). viz_contributions() displays (joint) contributions. viz_cos2() displays (joint) cos2. viz_*() functions allow highlight additional information varying different graphical elements (color, transparency, shape size symbols\u2026).","code":"## Form biplot biplot(X, type = \"form\") ## Highlight species viz_individuals(   x = X,    highlight = iris$Species,    col = khroma::color(\"high contrast\")(3),    pch = 16 ) ## Add ellipses viz_tolerance(   x = X,    group = iris$Species,    level = 0.95,   border = khroma::color(\"high contrast\")(3) )  ## Highlight petal length viz_individuals(   x = X,   highlight = iris$Petal.Length,   col = khroma::color(\"iridescent\")(255),    cex = c(1, 2),   pch = 16 ) ## Plot variables factor map viz_variables(X)  ## Scree plot screeplot(X, eigenvalues = FALSE, cumulative = TRUE)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Multivariate Data Analysis","text":"Please note dimensio project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":[],"package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/CA-class.html","id":null,"dir":"Reference","previous_headings":"","what":"CA Results \u2014 CA-class","title":"CA Results \u2014 CA-class","text":"S4 class store results simple correspondence analysis.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/CA-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"CA Results \u2014 CA-class","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/CA-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CA Results \u2014 CA-class","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## The chi square of independence between the two variables stats::chisq.test(colours) #>  #> \tPearson's Chi-squared test #>  #> data:  colours #> X-squared = 138.29, df = 9, p-value < 2.2e-16 #>   ## Compute correspondence analysis X <- ca(colours)  ## Plot rows viz_rows(X, labels = TRUE)   ## Plot columns viz_columns(X, labels = TRUE)   ## Get row coordinates head(get_coordinates(X, margin = 1)) #>                  F1          F2           F3  .sup #> marron   -0.4921577 -0.08832151  0.021611305 FALSE #> noisette -0.2125969  0.16739109 -0.100518284 FALSE #> vert      0.1617534  0.33903957  0.087597437 FALSE #> bleu      0.5474139 -0.08295428 -0.004709408 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                 F1          F2          F3  .sup #> brun    -0.5045624 -0.21482046  0.05550909 FALSE #> chatain -0.1482527  0.03266635 -0.04880414 FALSE #> roux    -0.1295233  0.31964240  0.08315117 FALSE #> blond    0.8353478 -0.06957934  0.01621471 FALSE  ## Get row distances to centroid head(get_distances(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.25048691 0.08332116 0.14878530 0.30656555   ## Get row inertias head(get_inertia(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.09308635 0.01308930 0.01608490 0.11133715   ## Get row contributions head(get_contributions(X, margin = 1)) #>                 F1       F2         F3 #> marron   43.115744 13.04249  6.6795992 #> noisette  3.400961 19.80398 61.0855951 #> vert      1.354851 55.90952 31.9248234 #> bleu     52.128445 11.24401  0.3099822  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1 0.208772652 89.372732   89.37273 #> F2 0.022226615  9.514911   98.88764 #> F3 0.002598439  1.112356  100.00000","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateAnalysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Output of Multivariate Data Analysis \u2014 MultivariateAnalysis","title":"Output of Multivariate Data Analysis \u2014 MultivariateAnalysis","text":"virtual S4 class store output multivariate data analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateAnalysis.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Output of Multivariate Data Analysis \u2014 MultivariateAnalysis","text":"data numeric matrix. dimension integer giving dimension solution. singular_values numeric vector giving singular values. rows MultivariateResults object. columns MultivariateResults object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateAnalysis.html","id":"subset","dir":"Reference","previous_headings":"","what":"Subset","title":"Output of Multivariate Data Analysis \u2014 MultivariateAnalysis","text":"code snippets , x MultivariateAnalysis object. x[[]] Extracts information slot selected subscript . length-one character vector.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateAnalysis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Output of Multivariate Data Analysis \u2014 MultivariateAnalysis","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateBootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Output of Bootstrap Replications \u2014 MultivariateBootstrap","title":"Output of Bootstrap Replications \u2014 MultivariateBootstrap","text":"virtual S4 class store output bootstrap analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateBootstrap.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Output of Bootstrap Replications \u2014 MultivariateBootstrap","text":"replications integer giving number bootstrap replications.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateBootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Output of Bootstrap Replications \u2014 MultivariateBootstrap","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateResults.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Data Analysis Results \u2014 MultivariateResults","title":"Multivariate Data Analysis Results \u2014 MultivariateResults","text":"S4 class store results multivariate data analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateResults.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Multivariate Data Analysis Results \u2014 MultivariateResults","text":"names character vector specifying row names. principal numeric matrix giving principal coordinates. standard numeric matrix giving standard coordinates. contributions numeric matrix giving contributions definition dimensions. cosine numeric matrix giving \\(cos^2\\) values. distances numeric vector giving distances centroid. weights numeric vector giving masses/weights. supplement logical vector specifying supplementary points. order integer vector giving original indices data (computation moves supplementary points end results). groups character vector specifying class observation.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateResults.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Multivariate Data Analysis Results \u2014 MultivariateResults","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateSummary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of Multivariate Data Analysis \u2014 MultivariateSummary","title":"Summary of Multivariate Data Analysis \u2014 MultivariateSummary","text":"virtual S4 class store summary multivariate data analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateSummary.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Summary of Multivariate Data Analysis \u2014 MultivariateSummary","text":"data numeric matrix. eigenvalues numeric matrix. results numeric matrix. supplement logical vector specifying supplementary points. margin integer.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateSummary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary of Multivariate Data Analysis \u2014 MultivariateSummary","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/PCA-class.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Results \u2014 PCA-class","title":"PCA Results \u2014 PCA-class","text":"S4 class store results principal components analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/PCA-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"PCA Results \u2014 PCA-class","text":"center numeric vector giving column mean initial dataset (active individuals ). scale numeric vector giving column standard deviations initial dataset (active individuals ).","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/PCA-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"PCA Results \u2014 PCA-class","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/PCA-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Results \u2014 PCA-class","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = sample(150, 10), sup_col = 2) #> 1 qualitative variable was removed: Species.  ## Get row coordinates head(get_coordinates(X, margin = 1)) #>          F1          F2  .sup #> 1 -2.093090  0.29090646 FALSE #> 2 -2.230345  0.09157279 FALSE #> 3 -2.401333 -0.09328414 FALSE #> 4 -2.402495 -0.22190447 FALSE #> 5 -2.161718  0.19123962 FALSE #> 6 -1.633770  0.40889077 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                      F1         F2  .sup #> Sepal.Length  0.9308275  0.3643076 FALSE #> Petal.Length  0.9841342 -0.1138202 FALSE #> Petal.Width   0.9656191 -0.2351788 FALSE #> Sepal.Width  -0.3360454  0.4364270  TRUE  ## Get correlations between variables and dimensions head(get_correlations(X)) #>                      F1         F2  .sup #> Sepal.Length  0.9308275  0.3643076 FALSE #> Petal.Length  0.9841342 -0.1138202 FALSE #> Petal.Width   0.9656191 -0.2351788 FALSE #> Sepal.Width  -0.3360454  0.4364270  TRUE  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.7673802 93.229129   93.22913 #> F2   0.2009841  6.770871  100.00000","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/benthos.html","id":null,"dir":"Reference","previous_headings":"","what":"Benthos \u2014 benthos","title":"Benthos \u2014 benthos","text":"Abundances Marine Species Sea-Bed Samples","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/benthos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Benthos \u2014 benthos","text":"","code":"benthos","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/benthos.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Benthos \u2014 benthos","text":"data.frame 13 columns (sites) 92 rows (species).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/benthos.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Benthos \u2014 benthos","text":"http://www.carme-n.org/?sec=data7","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Biplot \u2014 biplot","title":"Biplot \u2014 biplot","text":"Biplot","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Biplot \u2014 biplot","text":"","code":"# S4 method for CA biplot(   x,   axes = c(1, 2),   type = c(\"rows\", \"columns\", \"contributions\"),   active = TRUE,   sup = TRUE,   labels = \"columns\",   col.rows = \"#004488\",   col.columns = \"#BB5566\",   cex.rows = graphics::par(\"cex\"),   cex.columns = graphics::par(\"cex\"),   pch.rows = 16,   pch.columns = 17,   main = NULL,   sub = NULL,   ... )  # S4 method for PCA biplot(   x,   axes = c(1, 2),   type = c(\"form\", \"covariance\"),   active = TRUE,   sup = TRUE,   labels = \"variables\",   col.rows = \"#004488\",   col.columns = \"#BB5566\",   pch.rows = 16,   pch.columns = 17,   lty = \"solid\",   lwd = 2,   main = NULL,   sub = NULL,   ... )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Biplot \u2014 biplot","text":"x CA PCA object. axes length-two numeric vector giving dimensions plotted. type character string specifying biplot plotted (see ). must one \"rows\", \"columns\", \"contribution\" (CA), \"form\" \"covariance\" (PCA). unambiguous substring can given. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels character vector specifying whether \"rows\"/\"individuals\" /\"columns\"/\"variables\" names must drawn. unambiguous substring can given. col.rows, col.columns color specification. cex.rows, cex.columns numerical vector giving amount plotting characters symbols scaled relative default. pch.rows, pch.columns symbol specification. main character string giving main title plot. sub character string giving subtitle plot. ... parameters passed wordcloud::wordlayout(). lty, lwd specification line type width.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Biplot \u2014 biplot","text":"biplot() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Biplot \u2014 biplot","text":"biplot simultaneous representation rows columns rectangular dataset. generalization scatterplot case mutlivariate data: allows visualize much information possible single graph (Greenacre 2010). Biplots drawbacks advantages: can quickly become difficult read display lot information . may preferable visualize results individuals variables separately.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"pca-biplots","dir":"Reference","previous_headings":"","what":"PCA Biplots","title":"Biplot \u2014 biplot","text":"form Form biplot (row-metric-preserving). form biplot favors representation individuals: distance individuals approximates Euclidean distance rows. form biplot length vector approximates quality representation variable. covariance Covariance biplot (column-metric-preserving). covariance biplot favors representation variables: length vector approximates standard deviation variable cosine angle formed two vectors approximates correlation two variables. covariance biplot distance individuals approximates Mahalanobis distance rows.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"ca-biplots","dir":"Reference","previous_headings":"","what":"CA Biplots","title":"Biplot \u2014 biplot","text":"rows Row principal biplot. columns Column principal biplot. contribution Contribution biplot","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Biplot \u2014 biplot","text":"Aitchison, J. Greenacre, M. (2002). Biplots Compositional Data. Journal Royal Statistical Society: Series C (Applied Statistics), 51(4): 375-92. doi:10.1111/1467-9876.00275 . Greenacre, M. J. Biplots Practice. Bilbao: Fundaci\u00f3n BBVA, 2010.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Biplot \u2014 biplot","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Biplot \u2014 biplot","text":"","code":"## Replicate examples from Greenacre 2007, p. 59-68 data(\"countries\")  ## Compute principal components analysis ## All rows and all columns obtain the same weight row_w <- rep(1 / nrow(countries), nrow(countries)) # 1/13 col_w <- rep(1 / ncol(countries), ncol(countries)) # 1/6 Y <- pca(countries, scale = FALSE, weight_row = row_w, weight_col = col_w)  ## Row-metric-preserving biplot (form biplot) biplot(Y, type = \"form\")   ## Column-metric-preserving biplot (covariance biplot) biplot(Y, type = \"covariance\")   ## Replicate examples from Greenacre 2007, p. 79-88 data(\"benthos\")  ## Compute correspondence analysis X <- ca(benthos)  ## Row principal CA biplot biplot(X, type = \"row\", labels = \"columns\")   ## Column principal CA biplot biplot(X, type = \"column\", labels = \"columns\")   ## Contribution CA biplot biplot(X, type = \"contrib\", labels = NULL)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial Bootstrap Analysis \u2014 bootstrap","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"Checks analysis partial bootstrap resampling.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"","code":"bootstrap(object, ...)  # S4 method for CA bootstrap(object, n = 30)  # S4 method for PCA bootstrap(object, n = 30)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"object CA PCA object. ... Currently used. n non-negative integer giving number bootstrap replications.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"#' Returns BootstrapCA BootstrapPCA object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"Greenacre, Michael J. Theory Applications Correspondence Analysis. London: Academic Press, 1984. Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inf\u00e9rence en fouille de donn\u00e9es. Paris: Dunod, 2006.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"","code":"## Bootstrap on CA ## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## Compute correspondence analysis X <- ca(colours)  ## Bootstrap (30 replicates) Y <- bootstrap(X, n = 30)  if (FALSE) { ## Get replicated coordinates get_replications(Y, margin = 1) get_replications(Y, margin = 2) }  ## Plot with ellipses viz_rows(Y) viz_tolerance(Y, margin = 1, level = c(0.68, 0.95))   viz_columns(Y) viz_tolerance(Y, margin = 2, level = c(0.68, 0.95))   ## Plot with convex hulls viz_columns(Y) viz_hull(Y, margin = 2)   ## Bootstrap on PCA ## Compute principal components analysis data(\"iris\") X <- pca(iris) #> 1 qualitative variable was removed: Species.  ## Bootstrap (30 replicates) Y <- bootstrap(X, n = 30)  ## Plot with ellipses viz_variables(Y) viz_tolerance(Y, margin = 2, level = c(0.68, 0.95))","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":null,"dir":"Reference","previous_headings":"","what":"Correspondence Analysis \u2014 ca","title":"Correspondence Analysis \u2014 ca","text":"Computes simple correspondence analysis based singular value decomposition.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correspondence Analysis \u2014 ca","text":"","code":"ca(object, ...)  # S4 method for data.frame ca(object, rank = NULL, sup_row = NULL, sup_col = NULL)  # S4 method for matrix ca(object, rank = NULL, sup_row = NULL, sup_col = NULL)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correspondence Analysis \u2014 ca","text":"object \\(m \\times p\\) numeric matrix data.frame. ... Currently used. rank integer value specifying maximal number components kept results. NULL (default), \\(min(m, p) - 1\\) components returned. sup_row numeric logical vector specifying indices supplementary rows. sup_col numeric logical vector specifying indices supplementary columns.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correspondence Analysis \u2014 ca","text":"CA object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correspondence Analysis \u2014 ca","text":"Greenacre, M. J. Theory Applications Correspondence Analysis. London: Academic Press, 1984. Greenacre, M. J. Correspondence Analysis Practice. Seconde edition. Interdisciplinary Statistics Series. Boca Raton: Chapman & Hall/CRC, 2007. Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inf\u00e9rence en fouille de donn\u00e9es. Paris: Dunod, 2006.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correspondence Analysis \u2014 ca","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correspondence Analysis \u2014 ca","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## The chi square of independence between the two variables stats::chisq.test(colours) #>  #> \tPearson's Chi-squared test #>  #> data:  colours #> X-squared = 138.29, df = 9, p-value < 2.2e-16 #>   ## Compute correspondence analysis X <- ca(colours)  ## Plot rows viz_rows(X, labels = TRUE)   ## Plot columns viz_columns(X, labels = TRUE)   ## Get row coordinates head(get_coordinates(X, margin = 1)) #>                  F1          F2           F3  .sup #> marron   -0.4921577 -0.08832151  0.021611305 FALSE #> noisette -0.2125969  0.16739109 -0.100518284 FALSE #> vert      0.1617534  0.33903957  0.087597437 FALSE #> bleu      0.5474139 -0.08295428 -0.004709408 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                 F1          F2          F3  .sup #> brun    -0.5045624 -0.21482046  0.05550909 FALSE #> chatain -0.1482527  0.03266635 -0.04880414 FALSE #> roux    -0.1295233  0.31964240  0.08315117 FALSE #> blond    0.8353478 -0.06957934  0.01621471 FALSE  ## Get row distances to centroid head(get_distances(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.25048691 0.08332116 0.14878530 0.30656555   ## Get row inertias head(get_inertia(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.09308635 0.01308930 0.01608490 0.11133715   ## Get row contributions head(get_contributions(X, margin = 1)) #>                 F1       F2         F3 #> marron   43.115744 13.04249  6.6795992 #> noisette  3.400961 19.80398 61.0855951 #> vert      1.354851 55.90952 31.9248234 #> bleu     52.128445 11.24401  0.3099822  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1 0.208772652 89.372732   89.37273 #> F2 0.022226615  9.514911   98.88764 #> F3 0.002598439  1.112356  100.00000","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/colours.html","id":null,"dir":"Reference","previous_headings":"","what":"Colours \u2014 colours","title":"Colours \u2014 colours","text":"Contingency table eye hair colours different individuals.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/colours.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Colours \u2014 colours","text":"","code":"colours","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/colours.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Colours \u2014 colours","text":"data.frame 4 columns (hair colours) 4 rows (eye colours).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/colours.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Colours \u2014 colours","text":"Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inf\u00e9rence en fouille de donn\u00e9es. Paris: Dunod, 2006, p. 170-172","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/countries.html","id":null,"dir":"Reference","previous_headings":"","what":"Countries \u2014 countries","title":"Countries \u2014 countries","text":"Student ratings 13 countries six attributes.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/countries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Countries \u2014 countries","text":"","code":"countries","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/countries.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Countries \u2014 countries","text":"data.frame 6 columns (attributes) 13 rows (countries).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/countries.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Countries \u2014 countries","text":"Greenacre, M. J. Biplots Practice. Bilbao: Fundaci\u00f3n BBVA, 2010.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimensio-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in dimensio \u2014 dimensio-deprecated","title":"Deprecated Functions in dimensio \u2014 dimensio-deprecated","text":"functions still work removed (defunct) next version.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimensio-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions in dimensio \u2014 dimensio-deprecated","text":"","code":"stat_hull(   mapping = NULL,   data = NULL,   geom = \"polygon\",   position = \"identity\",   na.rm = FALSE,   show.legend = NA,   inherit.aes = TRUE,   ... )  plot_rows(object, ...)  # S4 method for MultivariateAnalysis plot_rows(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_columns(object, ...)  # S4 method for MultivariateAnalysis plot_columns(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  # S4 method for BootstrapPCA plot_columns(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_individuals(object, ...)  # S4 method for PCA plot_individuals(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_variables(object, ...)  # S4 method for PCA plot_variables(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   linetype = NULL,   size = NULL,   group = NULL )  plot_variance(object, ...)  # S4 method for MultivariateAnalysis plot_variance(   object,   select = NULL,   variance = TRUE,   cumulative = TRUE,   fill = \"grey30\",   border = \"grey10\",   colour = \"red\" )  plot_contributions(object, ...)  # S4 method for MultivariateAnalysis plot_contributions(   object,   margin = 2,   axes = 1,   sort = TRUE,   decreasing = TRUE,   limit = 10,   fill = \"grey30\",   border = \"grey10\" )  plot_cos2(object, ...)  # S4 method for MultivariateAnalysis plot_cos2(   object,   margin = 2,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   sort = TRUE,   decreasing = TRUE,   limit = 10,   fill = \"grey30\",   border = \"grey10\" )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimensio-package.html","id":null,"dir":"Reference","previous_headings":"","what":"dimensio: Multivariate Data Analysis \u2014 dimensio-package","title":"dimensio: Multivariate Data Analysis \u2014 dimensio-package","text":"Simple Principal Components Analysis (PCA) Correspondence Analysis (CA) based Singular Value Decomposition (SVD). package provides S4 classes methods compute, extract, summarize visualize results multivariate data analysis. also includes methods partial bootstrap validation described Greenacre (1984) <isbn:978-0-12-299050-2> Lebart et al. (2006) <isbn:978-2-10-049616-7>.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimensio-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"dimensio: Multivariate Data Analysis \u2014 dimensio-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimnames.html","id":null,"dir":"Reference","previous_headings":"","what":"Dimnames of an Object \u2014 dimnames","title":"Dimnames of an Object \u2014 dimnames","text":"Retrieve set dimnames object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimnames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dimnames of an Object \u2014 dimnames","text":"","code":"# S4 method for MultivariateAnalysis dim(x)  # S4 method for MultivariateAnalysis rownames(x, do.NULL = TRUE, prefix = \"row\")  # S4 method for MultivariateAnalysis colnames(x, do.NULL = TRUE, prefix = \"col\")  # S4 method for MultivariateAnalysis dimnames(x)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimnames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dimnames of an Object \u2014 dimnames","text":"x object retrieve row column names (CA PCA object). .NULL logical scalar. FALSE names NULL, names created. prefix character string specifying prefix created names.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimnames.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dimnames of an Object \u2014 dimnames","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_contributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Contributions \u2014 get_contributions","title":"Get Contributions \u2014 get_contributions","text":"Get Contributions","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_contributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Contributions \u2014 get_contributions","text":"","code":"get_contributions(x, ...)  get_correlations(x, ...)  get_cos2(x, ...)  # S4 method for MultivariateAnalysis get_contributions(x, margin = 1)  # S4 method for PCA get_correlations(x, sup_name = \".sup\")  # S4 method for MultivariateAnalysis get_cos2(x, margin = 1, sup_name = \".sup\")","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_contributions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Contributions \u2014 get_contributions","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. sup_name character string specifying name column create supplementary points attribution (see ).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_contributions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Contributions \u2014 get_contributions","text":"get_contributions() returns data.frame contributions definition principal dimensions. get_correlations() returns data.frame correlations variables dimensions (PCA). extra column (named sup_name) added specifying whether observation supplementary point . get_cos2() returns data.frame \\(cos^2\\) values (.e. quality representation points factor map). extra column (named sup_name) added specifying whether observation supplementary point .","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_contributions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Contributions \u2014 get_contributions","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Coordinates \u2014 get_coordinates","title":"Get Coordinates \u2014 get_coordinates","text":"Get Coordinates","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Coordinates \u2014 get_coordinates","text":"","code":"get_coordinates(x, ...)  get_replications(x, ...)  # S4 method for MultivariateAnalysis get_coordinates(x, margin = 1, principal = TRUE, sup_name = \".sup\")  # S4 method for MultivariateBootstrap get_replications(x, margin = 1)  # S4 method for BootstrapPCA get_replications(x)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Coordinates \u2014 get_coordinates","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. principal logical scalar: principal coordinates returned? FALSE, standard coordinates returned. sup_name character string specifying name column create supplementary points attribution (see ).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Coordinates \u2014 get_coordinates","text":"get_coordinates() returns data.frame coordinates. extra column (named sup_name) added specifying whether observation supplementary point . get_replications() returns array coordinates.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Coordinates \u2014 get_coordinates","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Coordinates \u2014 get_coordinates","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 5:10) #> 1 qualitative variable was removed: Species.  ## Get row principal coordinates head(get_coordinates(X, margin = 1, principal = TRUE)) #>           F1         F2          F3  .sup #> 1  -2.385846  0.5084067 -0.12383101 FALSE #> 2  -2.215654 -0.6515155 -0.23313750 FALSE #> 3  -2.496566 -0.3169280  0.04974232 FALSE #> 4  -2.433607 -0.5744432  0.09690301 FALSE #> 11 -2.279483  1.0742447 -0.26629760 FALSE #> 12 -2.450871  0.1591839  0.09988982 FALSE  ## Get row standard coordinates head(get_coordinates(X, margin = 1, principal = FALSE)) #>          F1         F2         F3  .sup #> 1 -1.403075  0.5268995 -0.3142569 FALSE #> 2 -1.302987 -0.6752138 -0.5916536 FALSE #> 3 -1.468187 -0.3284560  0.1262355 FALSE #> 4 -1.431162 -0.5953380  0.2459193 FALSE #> 5 -1.340525  1.1133193 -0.6758069 FALSE #> 6 -1.441315  0.1649741  0.2534992 FALSE  ## Tidy principal coordinates head(tidy(X, margin = 1)) #>   label component supplementary coordinate contribution        cos2 #> 1     1        F1         FALSE -2.3858463   1.36709655 0.954033073 #> 2     1        F2         FALSE  0.5084067   0.19279380 0.043321256 #> 3     1        F3         FALSE -0.1238310   0.06858151 0.002570025 #> 4    10        F1          TRUE -2.3173283           NA 0.953213276 #> 5    10        F2          TRUE -0.4448514           NA 0.035127301 #> 6    10        F3          TRUE -0.2525218           NA 0.011319124 head(tidy(X, margin = 2)) #>          label component supplementary coordinate contribution         cos2 #> 1 Petal.Length        F1         FALSE 0.99121597  33.97921607 0.9825091061 #> 2 Petal.Length        F2         FALSE 0.01655680   0.02944324 0.0002741275 #> 3 Petal.Length        F3         FALSE 0.05477927   1.93260521 0.0030007681 #> 4  Petal.Width        F1         FALSE 0.96350955  32.10619376 0.9283506618 #> 5  Petal.Width        F2         FALSE 0.05756108   0.35586957 0.0033132775 #> 6  Petal.Width        F3         FALSE 0.24948796  40.08758618 0.0622442430  head(augment(X, margin = 1, axes = c(1, 2))) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.385846  0.5084067     1         FALSE 0.006944444 5.950740     4.132458 #> 2 -2.215654 -0.6515155     2         FALSE 0.006944444 5.333593     3.703884 #> 3 -2.496566 -0.3169280     3         FALSE 0.006944444 6.333284     4.398114 #> 4 -2.433607 -0.5744432     4         FALSE 0.006944444 6.252426     4.341962 #> 5 -2.279483  1.0742447    11         FALSE          NA 6.350046     4.409754 #> 6 -2.450871  0.1591839    12         FALSE          NA 6.032107     4.188963 #>        cos2 #> 1 0.9973543 #> 2 0.9880287 #> 3 0.9995093 #> 4 0.9977072 #> 5 0.9889258 #> 6 0.9951651 head(augment(X, margin = 2, axes = c(1, 2))) #>           F1         F2        label supplementary mass       sum contribution #> 1  0.8875399 0.36087785 Sepal.Length         FALSE    1 0.9179599     91.79599 #> 2 -0.4392190 0.89286998  Sepal.Width         FALSE    1 0.9901302     99.01302 #> 3  0.9912160 0.01655680 Petal.Length         FALSE    1 0.9827832     98.27832 #> 4  0.9635096 0.05756108  Petal.Width         FALSE    1 0.9316639     93.16639 #>        cos2 #> 1 0.9179599 #> 2 0.9901302 #> 3 0.9827832 #> 4 0.9316639","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Original Data \u2014 get_data","title":"Get Original Data \u2014 get_data","text":"Get Original Data","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Original Data \u2014 get_data","text":"","code":"get_data(x, ...)  # S4 method for MultivariateAnalysis get_data(x)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Original Data \u2014 get_data","text":"x object get element(s) (CA PCA object). ... Currently used.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Original Data \u2014 get_data","text":"Returns data.frame original data.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Original Data \u2014 get_data","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_distances.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Distances \u2014 get_distances","title":"Get Distances \u2014 get_distances","text":"Get Distances","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_distances.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Distances \u2014 get_distances","text":"","code":"get_distances(x, ...)  # S4 method for MultivariateAnalysis get_distances(x, margin = 1)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_distances.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Distances \u2014 get_distances","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_distances.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Distances \u2014 get_distances","text":"get_distances() returns numeric vector squared distance centroide.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_distances.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Distances \u2014 get_distances","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_eigenvalues.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Eigenvalues \u2014 get_eigenvalues","title":"Get Eigenvalues \u2014 get_eigenvalues","text":"Get Eigenvalues","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_eigenvalues.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Eigenvalues \u2014 get_eigenvalues","text":"","code":"get_eigenvalues(x)  get_inertia(x, ...)  get_variance(x, ...)  # S4 method for MultivariateAnalysis get_eigenvalues(x)  # S4 method for MultivariateAnalysis get_inertia(x, margin = 1)  # S4 method for MultivariateAnalysis get_variance(x, digits = 2)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_eigenvalues.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Eigenvalues \u2014 get_eigenvalues","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. digits integer indicating number decimal places used.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_eigenvalues.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Eigenvalues \u2014 get_eigenvalues","text":"get_eigenvalues() returns data.frame following columns: eigenvalues, variance (percentage variance) cumulative (cumulative percentage variance). get_variance() returns numeric vector giving percentage explained variance dimension. get_inertia() returns numeric vector.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_eigenvalues.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Eigenvalues \u2014 get_eigenvalues","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Loadings \u2014 loadings","title":"Extract Loadings \u2014 loadings","text":"Extract loadingsin principal components analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Loadings \u2014 loadings","text":"","code":"# S4 method for PCA loadings(x)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Loadings \u2014 loadings","text":"x PCA object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Loadings \u2014 loadings","text":"Returns variable loadings (.e. coefficients linear combination original variables).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Extract Loadings \u2014 loadings","text":"loadings() implemented consistency [stats][stats::loadings].","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Loadings \u2014 loadings","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Components Analysis \u2014 pca","title":"Principal Components Analysis \u2014 pca","text":"Computes principal components analysis based singular value decomposition.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Components Analysis \u2014 pca","text":"","code":"pca(object, ...)  # S4 method for data.frame pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )  # S4 method for matrix pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Components Analysis \u2014 pca","text":"object \\(m \\times p\\) numeric matrix data.frame. ... Currently used. center logical scalar: variables shifted zero centered? scale logical scalar: variables scaled unit variance? rank integer value specifying maximal number components kept results. NULL (default), \\(p - 1\\) components returned. sup_row numeric logical vector specifying indices supplementary rows (individuals). sup_col numeric logical vector specifying indices supplementary columns (variables). weight_row numeric vector specifying active row (individual) weights. NULL (default), uniform weights used. Row weights internally normalized sum 1 weight_col numeric vector specifying active column (variable) weights. NULL (default), uniform weights (1) used.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Components Analysis \u2014 pca","text":"PCA object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Principal Components Analysis \u2014 pca","text":"Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inf\u00e9rence en fouille de donn\u00e9es. Paris: Dunod, 2006.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Components Analysis \u2014 pca","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Components Analysis \u2014 pca","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = sample(150, 10), sup_col = 2) #> 1 qualitative variable was removed: Species.  ## Get row coordinates head(get_coordinates(X, margin = 1)) #>          F1          F2  .sup #> 1 -2.032409  0.28085613 FALSE #> 2 -2.170509  0.08045204 FALSE #> 3 -2.342144 -0.10541898 FALSE #> 4 -2.344124 -0.23468718 FALSE #> 5 -2.101459  0.18065408 FALSE #> 6 -1.571063  0.39938987 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                      F1         F2  .sup #> Sepal.Length  0.9327177  0.3595041 FALSE #> Petal.Length  0.9844532 -0.1133177 FALSE #> Petal.Width   0.9666608 -0.2314771 FALSE #> Sepal.Width  -0.3298748  0.4616406  TRUE  ## Get correlations between variables and dimensions head(get_correlations(X)) #>                      F1         F2  .sup #> Sepal.Length  0.9327177  0.3595041 FALSE #> Petal.Length  0.9844532 -0.1133177 FALSE #> Petal.Width   0.9666608 -0.2314771 FALSE #> Sepal.Width  -0.3298748  0.4616406  TRUE  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.7735436 93.410174   93.41017 #> F2   0.1956657  6.589826  100.00000","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict New Coordinates \u2014 predict","title":"Predict New Coordinates \u2014 predict","text":"Predict projection new individuals/rows variables/columns.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict New Coordinates \u2014 predict","text":"","code":"# S4 method for CA predict(object, newdata, margin = 1)  # S4 method for PCA predict(object, newdata, margin = 1)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict New Coordinates \u2014 predict","text":"object CA PCA object. newdata object supplementary points coercible matrix compute principal coordinates. margin length-one numeric vector giving subscript data predicted: 1 indicates individuals/rows (default), 2 indicates variables/columns.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict New Coordinates \u2014 predict","text":"data.frame coordinates.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict New Coordinates \u2014 predict","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict New Coordinates \u2014 predict","text":"","code":"## Create a matrix A <- matrix(data = sample(1:10, 100, TRUE), nrow = 10, ncol = 10)  ## Compute correspondence analysis X <- ca(A, sup_row = 8:10, sup_col = 7:10)  ## Predict new row coordinates Y <- matrix(data = sample(1:10, 120, TRUE), nrow = 20, ncol = 6) predict(X, Y, margin = 1) #>             F1          F2          F3          F4          F5 #> 1  -0.44396581 -0.50960821  0.44521328 -0.09770053  0.19779361 #> 2   0.19836692  0.28394335 -0.08031012 -0.51922149  0.28705168 #> 3  -0.14828872 -0.07140818  0.68064737  0.26048664  0.08963952 #> 4   0.04632205  0.40020759  0.15806088  0.03129221 -0.12639647 #> 5   0.33755045  0.17157959 -0.01355668 -0.20094255  0.15179069 #> 6   0.21722785  0.04239695  0.24485768 -0.06068072  0.31685301 #> 7   0.45136318  0.26013909  0.24718420  0.33535267  0.34573930 #> 8  -0.05059758 -0.18276555  0.14466080  0.16930475  0.17045871 #> 9   0.32840651  0.21439863  0.65551072 -0.07000846 -0.19508160 #> 10  0.15328381 -0.06551832 -0.29128753 -0.30113902 -0.06946447 #> 11 -0.02630774  0.18617672  0.28477312  0.21727088  0.06039769 #> 12  0.33902976  0.23489949 -0.06362375  0.01758543 -0.29224505 #> 13  0.27388911  0.11625301  0.50640086  0.06492469  0.24160926 #> 14  0.15696908 -0.11715021  0.38554698 -0.01077783  0.08172497 #> 15  0.21872640 -0.08520612  0.08375413  0.49815924 -0.22868599 #> 16 -0.16272667  0.12336792  0.42885241  0.06363075 -0.07825940 #> 17  0.26248106  0.33507917  0.20327736  0.32884948  0.37402828 #> 18  0.24615051  0.40284117  0.49777530 -0.06869328  0.26444720 #> 19  0.42466799  0.07774676  0.42707271  0.26049874  0.07735685 #> 20 -0.16884380  0.22022691  0.36517303  0.15716462 -0.03671480  ## Predict new column coordinates Z <- matrix(data = sample(1:10, 140, TRUE), nrow = 7, ncol = 20) predict(X, Z, margin = 2) #>               F1          F2          F3          F4           F5 #> 1  -0.1018982365  0.29882807 -0.27140920 -0.18039316  0.052531985 #> 2   0.2940803070  0.34894911 -0.19760172 -0.31854592  0.147804092 #> 3  -0.0866274641 -0.04704703  0.36507461  0.16909461  0.288678190 #> 4   0.2264308353 -0.45054827 -0.14049575 -0.34590057  0.389833718 #> 5   0.0151806240  0.39716068 -0.12193430  0.31923682  0.279931589 #> 6  -0.2636794882  0.13111318 -0.59403188 -0.17977473  0.295699739 #> 7   0.2120152064 -0.20202350  0.39028813 -0.28120128  0.470056421 #> 8  -0.2960293331  0.27368247  0.20664072  0.26069896 -0.212168230 #> 9  -0.1205503901  0.24424172  0.02458879 -0.31233567  0.368516988 #> 10 -0.2653849039 -0.02381985 -0.32195073  0.17872148 -0.008980461 #> 11 -0.0721349372 -0.08015807 -0.22080949  0.18964535 -0.013628829 #> 12 -0.1374204174  0.04208102 -0.23254432 -0.22256464  0.142539719 #> 13 -0.1385523930 -0.04779562 -0.43994198 -0.05507586  0.303967597 #> 14  0.2118293055  0.35526431  0.03897595 -0.24048200 -0.069765068 #> 15  0.1622483149  0.04367921  0.26549873  0.58227678 -0.112120438 #> 16 -0.2645058157 -0.13938609 -0.60611914 -0.14292183  0.317423370 #> 17 -0.0003190798 -0.49058572 -0.30606841 -0.10380016  0.390737894 #> 18 -0.1525116212  0.38692138 -0.10264325  0.11090332 -0.123887174 #> 19  0.3838415162 -0.08328281 -0.17426651  0.10062214  0.434387259 #> 20  0.3058252336 -0.23074533 -0.13948352  0.01718978  0.260282581","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Scree Plot \u2014 screeplot","title":"Scree Plot \u2014 screeplot","text":"Plot eigenvalues (scree plot) variances histogram.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scree Plot \u2014 screeplot","text":"","code":"# S4 method for MultivariateAnalysis screeplot(   x,   eigenvalues = FALSE,   cumulative = FALSE,   labels = TRUE,   limit = 10,   col = \"grey90\",   border = \"grey10\",   col.cumulative = \"red\",   lty.cumulative = \"solid\",   lwd.cumulative = 2,   ... )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scree Plot \u2014 screeplot","text":"x CA PCA object. eigenvalues logical scalar: eigenvalues plotted instead variance/inertia? cumulative logical scalar: cumulative percentages variance plotted? labels logical scalar: text labels drawn top bars? limit integer specifying number top elements displayed. col, border character string specifying bars infilling border colors. col.cumulative specification line color. lty.cumulative specification line type. lwd.cumulative specification line width. ... Extra parameters passed graphics::barplot().","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scree Plot \u2014 screeplot","text":"screeplot() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scree Plot \u2014 screeplot","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scree Plot \u2014 screeplot","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Screeplot screeplot(X)  screeplot(X, cumulative = TRUE)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Parts of an Object \u2014 subset","title":"Extract Parts of an Object \u2014 subset","text":"Operators acting objects extract parts.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Parts of an Object \u2014 subset","text":"","code":"# S4 method for CA,ANY,missing [[(x, i)  # S4 method for PCA,ANY,missing [[(x, i)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Parts of an Object \u2014 subset","text":"x object extract element(s) replace element(s). character string specifying elements extract. unambiguous substring can given (see details).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Parts of an Object \u2014 subset","text":"list.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Parts of an Object \u2014 subset","text":"\"data\", returns list following elements: data numeric matrix raw data. mean numeric vector giving variables means (PCA). sd numeric vector giving variables standard deviations (PCA). \"rows\", returns list following elements: coord numeric matrix rows/individuals coordinates. cos2 numeric matrix rows/individuals squared cosine. masses numeric vector giving rows masses/individual weights. sup logical vector specifying whether point supplementary observation . \"columns\", returns list following elements: coord numeric matrix columns/variables coordinates. cor numeric matrix correlation variables dimensions (PCA). cos2 numeric matrix columns/variables squared cosine. masses numeric vector giving columns masses/variable weights. sup logical vector specifying whether point supplementary observation . \"eigenvalues\", returns numeric vector eigenvalues.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Parts of an Object \u2014 subset","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Parts of an Object \u2014 subset","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 8:10, sup_col = 1) #> 1 qualitative variable was removed: Species.  ## Get results for the individuals X[[\"rows\"]] #> $coordinates #>             F1           F2 #> 1   -2.1799607  0.107860619 #> 2   -1.6996139 -0.926588874 #> 3   -1.9288393 -0.527485410 #> 4   -1.7585967 -0.705022642 #> 5   -2.2760301  0.314750517 #> 6   -2.2841520  1.069266219 #> 7   -1.9994783 -0.054120776 #> 11  -2.3350128  0.536316749 #> 12  -2.0097181 -0.069676614 #> 13  -1.7840271 -0.971497378 #> 14  -1.8952870 -1.015526377 #> 15  -2.7344808  1.112957446 #> 16  -2.8386721  2.074363046 #> 17  -2.4324986  1.010560888 #> 18  -2.0955476  0.152769123 #> 19  -2.2724958  0.817467817 #> 20  -2.3466691  0.788115151 #> 21  -1.9726315 -0.055000281 #> 22  -2.1661866  0.626133757 #> 23  -2.4243767  0.256045186 #> 24  -1.6233228 -0.127164668 #> 25  -1.8984582 -0.025647615 #> 26  -1.6254407 -0.897236208 #> 27  -1.8408919  0.020140394 #> 28  -2.1428741  0.122536952 #> 29  -2.0838914 -0.099029280 #> 30  -1.8175794 -0.483456411 #> 31  -1.7215100 -0.690346310 #> 32  -1.8779785  0.005464061 #> 33  -2.8037034  1.318967839 #> 34  -2.8524463  1.556089908 #> 35  -1.7585967 -0.705022642 #> 36  -1.9659259 -0.542161743 #> 37  -2.2170474  0.093184286 #> 38  -2.3604432  0.269842013 #> 39  -1.7367006 -0.941265207 #> 40  -2.0468047 -0.084352947 #> 41  -2.1326343  0.138092790 #> 42  -0.9798020 -2.344585993 #> 43  -1.9288393 -0.527485410 #> 44  -1.7681350  0.316847300 #> 45  -2.1139094  0.891728987 #> 46  -1.6152008 -0.881680370 #> 47  -2.3939955  0.757882980 #> 48  -1.8917527 -0.512809077 #> 49  -2.3350128  0.536316749 #> 50  -1.9878220 -0.305919178 #> 51   0.3450638  0.510411957 #> 52   0.3553036  0.525967795 #> 53   0.5997195  0.377783228 #> 54   0.8656684 -1.499239964 #> 55   0.7766677 -0.286915466 #> 56   0.5707548 -0.391408807 #> 57   0.4178206  0.807118863 #> 58   0.2567533 -1.529809908 #> 59   0.5117721 -0.169842576 #> 60   0.5287175 -0.641448199 #> 61   0.7152040 -2.328016836 #> 62   0.4361824  0.068158999 #> 63   0.7084985 -1.840855375 #> 64   0.6332719 -0.110257739 #> 65   0.1409057 -0.316605905 #> 66   0.3298732  0.259493060 #> 67   0.5474423  0.112187998 #> 68   0.2652383 -0.791729549 #> 69   1.3159972 -1.542931190 #> 70   0.4676168 -1.189953508 #> 71   0.7198029  0.704722305 #> 72   0.3853216 -0.464790472 #> 73   1.1761357 -0.863556163 #> 74   0.5605150 -0.406964645 #> 75   0.4005122 -0.213871574 #> 76   0.4259426  0.052603161 #> 77   0.7664279 -0.302471304 #> 78   0.9017017  0.275386670 #> 79   0.6435117 -0.094701901 #> 80   0.1387878 -1.086677445 #> 81   0.5265996 -1.411519740 #> 82   0.4050998 -1.471104576 #> 83   0.3598912 -0.731265207 #> 84   1.1425834 -0.375515196 #> 85   0.5474423  0.112187998 #> 86   0.2475780  0.984656096 #> 87   0.5255462  0.348430562 #> 88   1.0140150 -1.440534633 #> 89   0.2302695 -0.036334342 #> 90   0.6735297 -1.085460167 #> 91   0.6413938 -0.864773441 #> 92   0.5001159  0.081955827 #> 93   0.4930472 -0.923478773 #> 94   0.3528226 -1.736699807 #> 95   0.5555643 -0.642327704 #> 96   0.1829431 -0.066566513 #> 97   0.3634255 -0.228547907 #> 98   0.4005122 -0.213871574 #> 99   0.1338371 -1.322040504 #> 100  0.4224083 -0.450114139 #> 101  1.6596649  1.402087727 #> 102  1.3958227 -0.240789684 #> 103  1.5731339  0.587107682 #> 104  1.3047041  0.201463273 #> 105  1.6204604  0.617339853 #> 106  1.8327404  0.689842013 #> 107  1.1966153 -0.832444487 #> 108  1.5643105  0.304197604 #> 109  1.7631548 -0.596743655 #> 110  1.4085435  2.037433755 #> 111  0.9998890  0.838568312 #> 112  1.4699960 -0.211437019 #> 113  1.4247874  0.528402351 #> 114  1.6352879 -0.624337311 #> 115  1.7218189  0.190642734 #> 116  1.3273016  1.002646490 #> 117  1.1715481  0.393676839 #> 118  1.1856853  2.404546038 #> 119  2.4971040 -0.003871575 #> 120  1.5014304 -1.469549526 #> 121  1.4756482  1.061351821 #> 122  1.3099931 -0.018343948 #> 123  1.9775527  0.245830045 #> 124  1.2372363 -0.315050854 #> 125  1.2107526  1.178424712 #> 126  1.1648425  0.880838301 #> 127  1.1040803 -0.122837289 #> 128  0.9490282  0.305618841 #> 129  1.6540127  0.129298887 #> 130  1.1139818  0.347888830 #> 131  1.6706197  0.112863543 #> 132  0.9055992  2.270700031 #> 133  1.7384258  0.174207390 #> 134  0.9621009 -0.213533802 #> 135  1.2552597 -0.598840438 #> 136  1.8161334  0.706277356 #> 137  1.3308359  1.505363789 #> 138  1.0754787  0.600566737 #> 139  0.9119416  0.290942508 #> 140  1.2916314  0.720615916 #> 141  1.6190440  0.884694094 #> 142  1.3491977  0.766403925 #> 143  1.3958227 -0.240789684 #> 144  1.5498214  1.090704487 #> 145  1.5484050  1.358058728 #> 146  1.4823537  0.574190360 #> 147  1.5508748 -0.669245814 #> 148  1.2291144  0.439464848 #> 149  1.1722495  1.431102620 #> 150  1.0232015  0.334971507 #> 8   -2.0468047 -0.084352947 #> 9   -1.6035446 -1.133478772 #> 10  -1.8430098 -0.749931146 #>  #> $contributions #>              F1           F2 #> 1   1.453343684 0.0106993487 #> 2   0.883428800 0.7895973615 #> 3   1.137792525 0.2558894786 #> 4   0.945809064 0.4571278240 #> 5   1.584261926 0.0911096611 #> 6   1.595588876 1.0514853344 #> 7   1.222656219 0.0026937641 #> 11  1.667437334 0.2645295822 #> 12  1.235211341 0.0044648369 #> 13  0.973360803 0.8679901399 #> 14  1.098552806 0.9484489111 #> 15  2.286760836 1.1391702764 #> 16  2.464344658 3.9573190026 #> 17  1.809573309 0.9391965689 #> 18  1.342969212 0.0214636064 #> 19  1.579345535 0.6145721192 #> 20  1.684126390 0.5712298316 #> 21  1.190043653 0.0027820270 #> 22  1.435035728 0.3605502185 #> 23  1.797509409 0.0602927030 #> 24  0.805899240 0.0148718312 #> 25  1.102232091 0.0006049584 #> 26  0.808003463 0.7403636858 #> 27  1.036400344 0.0003730503 #> 28  1.404314222 0.0138091094 #> 29  1.328070529 0.0090190079 #> 30  1.010317225 0.2149543180 #> 31  0.906337800 0.4382940161 #> 32  1.078579659 0.0000274576 #> 33  2.404003572 1.5999261560 #> 34  2.488318246 2.2269009410 #> 35  0.945809064 0.4571278240 #> 36  1.181966834 0.2703268996 #> 37  1.503214416 0.0079857717 #> 38  1.703954895 0.0669654302 #> 39  0.922403375 0.8148084750 #> 40  1.281220300 0.0065438305 #> 41  1.390925123 0.0175377362 #> 42  0.293594279 5.0554970160 #> 43  1.137792525 0.2558894786 #> 44  0.956096741 0.0923276004 #> 45  1.366607256 0.7313028984 #> 46  0.797855133 0.7149141115 #> 47  1.752740637 0.5282455348 #> 48  1.094459487 0.2418482414 #> 49  1.667437334 0.2645295822 #> 50  1.208442464 0.0860686388 #> 51  0.036414113 0.2395924925 #> 52  0.038607369 0.2544191706 #> 53  0.109993587 0.1312553072 #> 54  0.229178638 2.0671587361 #> 55  0.184476671 0.0757075942 #> 56  0.099625421 0.1408940695 #> 57  0.053388873 0.5991099343 #> 58  0.020160574 2.1523181354 #> 59  0.080098468 0.0265292309 #> 60  0.085490582 0.3784031185 #> 61  0.156433881 4.9842952461 #> 62  0.058184494 0.0042724593 #> 63  0.153514287 3.1165268007 #> 64  0.122645427 0.0111802030 #> 65  0.006071949 0.0921869714 #> 66  0.033278603 0.0619274246 #> 67  0.091653220 0.0115750885 #> 68  0.021515104 0.5764813136 #> 69  0.529640167 2.1893976274 #> 70  0.066873073 1.3022414468 #> 71  0.158452140 0.4567384375 #> 72  0.045406471 0.1986762522 #> 73  0.423044289 0.6858240370 #> 74  0.096082758 0.1523157768 #> 75  0.049057166 0.0420666239 #> 76  0.055484680 0.0025448091 #> 77  0.179644343 0.0841394918 #> 78  0.248654774 0.0697456870 #> 79  0.126643778 0.0082480050 #> 80  0.005890792 1.0860075196 #> 81  0.084807053 1.8323373666 #> 82  0.050187444 1.9903003497 #> 83  0.039610792 0.4917918728 #> 84  0.399251696 0.1296840484 #> 85  0.091653220 0.0115750885 #> 86  0.018745415 0.8916628530 #> 87  0.084468126 0.1116513580 #> 88  0.314455886 1.9084418605 #> 89  0.016216010 0.0012141326 #> 90  0.138734505 1.0835758275 #> 91  0.125811545 0.6877588893 #> 92  0.076491334 0.0061771934 #> 93  0.074344362 0.7843056805 #> 94  0.038070081 2.7738376469 #> 95  0.094392957 0.3794415052 #> 96  0.010235356 0.0040751457 #> 97  0.040392607 0.0480381224 #> 98  0.049057166 0.0420666239 #> 99  0.005478023 1.6073892100 #> 100 0.054567719 0.1863274482 #> 101 0.842387320 1.8079312085 #> 102 0.595842510 0.0533221045 #> 103 0.756837054 0.3170056865 #> 104 0.520589025 0.0373270182 #> 105 0.803059702 0.3504936509 #> 106 1.027242319 0.4376539041 #> 107 0.437905211 0.6372973156 #> 108 0.748370960 0.0851026540 #> 109 0.950718311 0.3274968644 #> 110 0.606752411 3.8176711884 #> 111 0.305755696 0.6467082790 #> 112 0.660850565 0.0411143642 #> 113 0.620827724 0.2567798899 #> 114 0.817823172 0.3584842707 #> 115 0.906663024 0.0334250480 #> 116 0.538778450 0.9245431850 #> 117 0.419750477 0.1425316314 #> 118 0.429941982 5.3173802664 #> 119 1.906971687 0.0000137850 #> 120 0.689416059 1.9860948268 #> 121 0.665942333 1.0359773621 #> 122 0.524818373 0.0003094690 #> 123 1.195988781 0.0555778122 #> 124 0.468140574 0.0912836189 #> 125 0.448313474 1.2771303006 #> 126 0.414959229 0.7135491729 #> 127 0.372796904 0.0138768844 #> 128 0.275441407 0.0858997255 #> 129 0.836659379 0.0153752114 #> 130 0.379513426 0.1113044416 #> 131 0.853544550 0.0117149082 #> 132 0.250808937 4.7418854471 #> 133 0.924236944 0.0279103111 #> 134 0.283081973 0.0419338551 #> 135 0.481879173 0.3298023644 #> 136 1.008710388 0.4587563529 #> 137 0.541651566 2.0840803395 #> 138 0.353732210 0.3317065721 #> 139 0.254334345 0.0778477176 #> 140 0.510209050 0.4775724073 #> 141 0.801656426 0.7198098450 #> 142 0.556701191 0.5401905310 #> 143 0.595842510 0.0533221045 #> 144 0.734571902 1.0940715530 #> 145 0.733229824 1.6961670731 #> 146 0.672008322 0.3032098599 #> 147 0.735570728 0.4119104916 #> 148 0.462014453 0.1776150699 #> 149 0.420253277 1.8835323329 #> 150 0.320179346 0.1031922926 #>  #> $cos2 #>              F1           F2 #> 1   0.997537883 2.442069e-03 #> 2   0.770275213 2.289389e-01 #> 3   0.929285621 6.949892e-02 #> 4   0.861533164 1.384668e-01 #> 5   0.981140144 1.876324e-02 #> 6   0.820250332 1.797497e-01 #> 7   0.997006440 7.304536e-04 #> 11  0.948921305 5.006041e-02 #> 12  0.997254638 1.198698e-03 #> 13  0.771015900 2.286355e-01 #> 14  0.775718588 2.227084e-01 #> 15  0.857879117 1.421129e-01 #> 16  0.651874030 3.480990e-01 #> 17  0.849498051 1.466163e-01 #> 18  0.993184069 5.278443e-03 #> 19  0.884472859 1.144510e-01 #> 20  0.898639841 1.013588e-01 #> 21  0.995527356 7.739112e-04 #> 22  0.920782450 7.693067e-02 #> 23  0.985634960 1.099383e-02 #> 24  0.983278582 6.033923e-03 #> 25  0.988580295 1.804280e-04 #> 26  0.766287189 2.334871e-01 #> 27  0.996619952 1.192913e-04 #> 28  0.996181238 3.257461e-03 #> 29  0.997744694 2.253183e-03 #> 30  0.933168940 6.602192e-02 #> 31  0.861052131 1.384664e-01 #> 32  0.993951874 8.414246e-06 #> 33  0.814686002 1.802995e-01 #> 34  0.769931500 2.291324e-01 #> 35  0.861533164 1.384668e-01 #> 36  0.926583440 7.047060e-02 #> 37  0.998037699 1.763122e-03 #> 38  0.984800233 1.287005e-02 #> 39  0.771150422 2.265235e-01 #> 40  0.997960094 1.694966e-03 #> 41  0.992507874 4.161436e-03 #> 42  0.146925900 8.413062e-01 #> 43  0.929285621 6.949892e-02 #> 44  0.946393484 3.039071e-02 #> 45  0.848161656 1.509286e-01 #> 46  0.765582021 2.281188e-01 #> 47  0.906464208 9.084652e-02 #> 48  0.931348538 6.843763e-02 #> 49  0.948921305 5.006041e-02 #> 50  0.976803096 2.313478e-02 #> 51  0.277995466 6.082479e-01 #> 52  0.311683132 6.830184e-01 #> 53  0.664146963 2.635440e-01 #> 54  0.249361414 7.479428e-01 #> 55  0.878241702 1.198537e-01 #> 56  0.637260992 2.996949e-01 #> 57  0.210729871 7.863598e-01 #> 58  0.027308118 9.694709e-01 #> 59  0.758623302 8.355378e-02 #> 60  0.387926000 5.709851e-01 #> 61  0.086194377 9.132535e-01 #> 62  0.926162213 2.261503e-02 #> 63  0.128022840 8.642691e-01 #> 64  0.898327863 2.723157e-02 #> 65  0.130964651 6.612032e-01 #> 66  0.593595505 3.673229e-01 #> 67  0.958380776 4.024891e-02 #> 68  0.090772461 8.087892e-01 #> 69  0.420435646 5.779399e-01 #> 70  0.133260226 8.629393e-01 #> 71  0.504882110 4.839482e-01 #> 72  0.406578554 5.915782e-01 #> 73  0.645293645 3.478754e-01 #> 74  0.519247169 2.737241e-01 #> 75  0.734870489 2.095489e-01 #> 76  0.958138539 1.461335e-02 #> 77  0.812191218 1.264981e-01 #> 78  0.912896321 8.514938e-02 #> 79  0.978657862 2.119510e-02 #> 80  0.016044824 9.836341e-01 #> 81  0.122144323 8.775790e-01 #> 82  0.070309187 9.272035e-01 #> 83  0.194938448 8.048312e-01 #> 84  0.891022209 9.624269e-02 #> 85  0.958380776 4.024891e-02 #> 86  0.059437245 9.401643e-01 #> 87  0.672034771 2.953945e-01 #> 88  0.330736771 6.674844e-01 #> 89  0.946744678 2.357189e-02 #> 90  0.277285564 7.201808e-01 #> 91  0.342463747 6.225436e-01 #> 92  0.891508689 2.394109e-02 #> 93  0.221487022 7.770072e-01 #> 94  0.039508517 9.572545e-01 #> 95  0.426831711 5.705601e-01 #> 96  0.490691231 6.496627e-02 #> 97  0.698527791 2.762532e-01 #> 98  0.734870489 2.095489e-01 #> 99  0.009680752 9.445952e-01 #> 100 0.467996371 5.314018e-01 #> 101 0.576108690 4.111629e-01 #> 102 0.961696898 2.861893e-02 #> 103 0.877177165 1.221776e-01 #> 104 0.958249977 2.284791e-02 #> 105 0.870879776 1.263950e-01 #> 106 0.851672810 1.206619e-01 #> 107 0.657810722 3.183482e-01 #> 108 0.886186912 3.351128e-02 #> 109 0.885499558 1.014339e-01 #> 110 0.321916434 6.735496e-01 #> 111 0.577480507 4.061726e-01 #> 112 0.978244776 2.023845e-02 #> 113 0.873487112 1.201395e-01 #> 114 0.848034409 1.236129e-01 #> 115 0.885718143 1.085828e-02 #> 116 0.607788840 3.468240e-01 #> 117 0.884632536 9.989009e-02 #> 118 0.191705611 7.884282e-01 #> 119 0.992440547 2.385649e-06 #> 120 0.508966080 4.875811e-01 #> 121 0.651305390 3.369285e-01 #> 122 0.949464886 1.861772e-04 #> 123 0.939398567 1.451656e-02 #> 124 0.929464957 6.026831e-02 #> 125 0.513528277 4.864713e-01 #> 126 0.594651369 3.400324e-01 #> 127 0.968111697 1.198353e-02 #> 128 0.898455967 9.317487e-02 #> 129 0.989867285 6.049073e-03 #> 130 0.787377903 7.679059e-02 #> 131 0.967682818 4.416571e-03 #> 132 0.132869218 8.353566e-01 #> 133 0.977445867 9.815525e-03 #> 134 0.899008289 4.428491e-02 #> 135 0.716813201 1.631402e-01 #> 136 0.867820065 1.312456e-01 #> 137 0.428750425 5.485781e-01 #> 138 0.748409494 2.333771e-01 #> 139 0.890507966 9.063963e-02 #> 140 0.754859896 2.349616e-01 #> 141 0.744129523 2.221864e-01 #> 142 0.695525700 2.244282e-01 #> 143 0.961696898 2.861893e-02 #> 144 0.666268263 3.299894e-01 #> 145 0.547542725 4.211977e-01 #> 146 0.810397292 1.215923e-01 #> 147 0.830568366 1.546654e-01 #> 148 0.874671814 1.118173e-01 #> 149 0.391519529 5.835188e-01 #> 150 0.903130625 9.679300e-02 #> 8   0.997960094 1.694966e-03 #> 9   0.666045138 3.327885e-01 #> 10  0.856123843 1.417503e-01 #>  #> $masses #>           1           2           3           4           5           6  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>           7          14          15          16          17          18  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          19          20          21          22          23          24  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          25          26          27          28          29          30  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          31          32          33          34          35          36  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          37          38          39          40          41          42  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          43          44          45          46          47          48  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          49          50          51          52          53          54  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          55          56          57          58          59          60  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          61          62          63          64          65          66  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          67          68          69          70          71          72  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          73          74          75          76          77          78  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          79          80          81          82          83          84  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          85          86          87          88          89          90  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          91          92          93          94          95          96  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          97          98          99         100         101         102  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         103         104         105         106         107         108  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         109         110         111         112         113         114  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         115         116         117         118         119         120  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         121         122         123         124         125         126  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         127         128         129         130         131         132  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         133         134         135         136         137         138  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         139         140         141         142         143         144  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         145         146         147         148         149         150  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>           8           9          10  #> 0.006802721 0.006802721 0.006802721  #>  #> $supplement #>   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [145] FALSE FALSE FALSE  TRUE  TRUE  TRUE #>","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Object Summaries \u2014 summary","title":"Object Summaries \u2014 summary","text":"Provides summary results multivariate data analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Object Summaries \u2014 summary","text":"","code":"# S4 method for CA summary(object, margin = 1, active = TRUE, sup = TRUE, rank = 3)  # S4 method for PCA summary(object, margin = 1, active = TRUE, sup = TRUE, rank = 3)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Object Summaries \u2014 summary","text":"object CA PCA object. margin length-one numeric vector giving subscript data summarized: 1 indicates individuals/rows (default), 2 indicates variables/columns. active logical scalar: active observations summarized? sup logical scalar: supplementary observations summarized? rank integer value specifying maximal number components kept results.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/summary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Object Summaries \u2014 summary","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Object Summaries \u2014 summary","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## Compute correspondence analysis X <- ca(colours)  ## Rows summary summary(X, margin = 1) #> --- Correspondence Analysis (CA) ------------------------------------------------ #>  #> Eigenvalues: #>    eigenvalues variance cumulative #> F1       0.209   89.373     89.373 #> F2       0.022    9.515     98.888 #> F3       0.003    1.112    100.000 #>  #> Active rows: #>          inertia CA1_coord CA1_contrib CA1_cos2 CA2_coord CA2_contrib CA2_cos2 #> marron    93.086    -0.492      43.116    0.967    -0.088      13.042    0.031 #> noisette  13.089    -0.213       3.401    0.542     0.167      19.804    0.336 #> vert      16.085     0.162       1.355    0.176     0.339      55.910    0.773 #> bleu     111.337     0.547      52.128    0.977    -0.083      11.244    0.022 #>          CA3_coord CA3_contrib CA3_cos2 #> marron       0.022       6.680    0.002 #> noisette    -0.101      61.086    0.121 #> vert         0.088      31.925    0.052 #> bleu        -0.005       0.310    0.000  ## Columns summary summary(X, margin = 2) #> --- Correspondence Analysis (CA) ------------------------------------------------ #>  #> Eigenvalues: #>    eigenvalues variance cumulative #> F1       0.209   89.373     89.373 #> F2       0.022    9.515     98.888 #> F3       0.003    1.112    100.000 #>  #> Active columns: #>         inertia CA1_coord CA1_contrib CA1_cos2 CA2_coord CA2_contrib CA2_cos2 #> brun     55.425    -0.505      22.246    0.838    -0.215      37.877    0.152 #> chatain  12.284    -0.148       5.086    0.864     0.033       2.319    0.042 #> roux     15.095    -0.130       0.964    0.133     0.320      55.131    0.812 #> blond   150.793     0.835      71.704    0.993    -0.070       4.673    0.007 #>         CA3_coord CA3_contrib CA3_cos2 #> brun        0.056      21.633    0.010 #> chatain    -0.049      44.284    0.094 #> roux        0.083      31.913    0.055 #> blond       0.016       2.171    0.000","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Coordinates \u2014 tidy","title":"Tidy Coordinates \u2014 tidy","text":"Tidy Coordinates","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Coordinates \u2014 tidy","text":"","code":"tidy(x, ...)  augment(x, ...)  # S4 method for MultivariateAnalysis augment(x, margin = 1, axes = c(1, 2), principal = TRUE, ...)  # S4 method for MultivariateAnalysis tidy(x, margin = 1, principal = TRUE, ...)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Coordinates \u2014 tidy","text":"x CA PCA object. ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. principal logical scalar: principal coordinates returned? FALSE, standard coordinates returned.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Coordinates \u2014 tidy","text":"tidy() returns long data.frame following columns: label Row/column names original data. component Component. supplementary Whether observation active supplementary. coordinate Coordinates. contribution Contributions definition components. cos2 \\(cos^2\\). augment() returns wide data.frame row/column coordinates along axes following columns: label Row/column names original data. supplementary Whether observation active supplementary. mass Weight/mass observation. sum Sum squared coordinates along axes. contribution Joint contributions definition axes. cos2 Joint \\(cos^2\\) along axes.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Coordinates \u2014 tidy","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Coordinates \u2014 tidy","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 5:10) #> 1 qualitative variable was removed: Species.  ## Get row principal coordinates head(get_coordinates(X, margin = 1, principal = TRUE)) #>           F1         F2          F3  .sup #> 1  -2.385846  0.5084067 -0.12383101 FALSE #> 2  -2.215654 -0.6515155 -0.23313750 FALSE #> 3  -2.496566 -0.3169280  0.04974232 FALSE #> 4  -2.433607 -0.5744432  0.09690301 FALSE #> 11 -2.279483  1.0742447 -0.26629760 FALSE #> 12 -2.450871  0.1591839  0.09988982 FALSE  ## Get row standard coordinates head(get_coordinates(X, margin = 1, principal = FALSE)) #>          F1         F2         F3  .sup #> 1 -1.403075  0.5268995 -0.3142569 FALSE #> 2 -1.302987 -0.6752138 -0.5916536 FALSE #> 3 -1.468187 -0.3284560  0.1262355 FALSE #> 4 -1.431162 -0.5953380  0.2459193 FALSE #> 5 -1.340525  1.1133193 -0.6758069 FALSE #> 6 -1.441315  0.1649741  0.2534992 FALSE  ## Tidy principal coordinates head(tidy(X, margin = 1)) #>   label component supplementary coordinate contribution        cos2 #> 1     1        F1         FALSE -2.3858463   1.36709655 0.954033073 #> 2     1        F2         FALSE  0.5084067   0.19279380 0.043321256 #> 3     1        F3         FALSE -0.1238310   0.06858151 0.002570025 #> 4    10        F1          TRUE -2.3173283           NA 0.953213276 #> 5    10        F2          TRUE -0.4448514           NA 0.035127301 #> 6    10        F3          TRUE -0.2525218           NA 0.011319124 head(tidy(X, margin = 2)) #>          label component supplementary coordinate contribution         cos2 #> 1 Petal.Length        F1         FALSE 0.99121597  33.97921607 0.9825091061 #> 2 Petal.Length        F2         FALSE 0.01655680   0.02944324 0.0002741275 #> 3 Petal.Length        F3         FALSE 0.05477927   1.93260521 0.0030007681 #> 4  Petal.Width        F1         FALSE 0.96350955  32.10619376 0.9283506618 #> 5  Petal.Width        F2         FALSE 0.05756108   0.35586957 0.0033132775 #> 6  Petal.Width        F3         FALSE 0.24948796  40.08758618 0.0622442430  head(augment(X, margin = 1, axes = c(1, 2))) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.385846  0.5084067     1         FALSE 0.006944444 5.950740     4.132458 #> 2 -2.215654 -0.6515155     2         FALSE 0.006944444 5.333593     3.703884 #> 3 -2.496566 -0.3169280     3         FALSE 0.006944444 6.333284     4.398114 #> 4 -2.433607 -0.5744432     4         FALSE 0.006944444 6.252426     4.341962 #> 5 -2.279483  1.0742447    11         FALSE          NA 6.350046     4.409754 #> 6 -2.450871  0.1591839    12         FALSE          NA 6.032107     4.188963 #>        cos2 #> 1 0.9973543 #> 2 0.9880287 #> 3 0.9995093 #> 4 0.9977072 #> 5 0.9889258 #> 6 0.9951651 head(augment(X, margin = 2, axes = c(1, 2))) #>           F1         F2        label supplementary mass       sum contribution #> 1  0.8875399 0.36087785 Sepal.Length         FALSE    1 0.9179599     91.79599 #> 2 -0.4392190 0.89286998  Sepal.Width         FALSE    1 0.9901302     99.01302 #> 3  0.9912160 0.01655680 Petal.Length         FALSE    1 0.9827832     98.27832 #> 4  0.9635096 0.05756108  Petal.Width         FALSE    1 0.9316639     93.16639 #>        cos2 #> 1 0.9179599 #> 2 0.9901302 #> 3 0.9827832 #> 4 0.9316639","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Contributions and cos2 \u2014 viz_contributions","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"Plots contributions histogram \\(cos^2\\) scatterplot.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"","code":"viz_contributions(x, ...)  viz_cos2(x, ...)  # S4 method for MultivariateAnalysis viz_contributions(   x,   margin = 2,   axes = 1,   sort = TRUE,   decreasing = TRUE,   limit = 10,   horiz = FALSE,   col = \"grey90\",   border = \"grey10\",   ... )  # S4 method for MultivariateAnalysis viz_cos2(   x,   margin = 2,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   sort = TRUE,   decreasing = TRUE,   limit = 10,   horiz = FALSE,   col = \"grey90\",   border = \"grey10\",   ... )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"x CA PCA object. ... Extra parameters passed graphics::barplot(). margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-one numeric vector giving dimensions plotted. sort logical scalar: data sorted? decreasing logical scalar: sort order decreasing? used sort TRUE. limit integer specifying number top elements displayed. horiz logical scalar: bars drawn horizontally first bottom? col, border character string specifying bars infilling border colors. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted?","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"viz_contributions() viz_cos2() called side-effects: result graphic displayed. Invisibly return x.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Get row contributions head(get_contributions(X, margin = 1)) #>          F1         F2          F3 #> 1 1.1715796 0.16806554 0.074085470 #> 2 0.9891845 0.33146674 0.250034006 #> 3 1.2768164 0.08526419 0.008875320 #> 4 1.2077372 0.26029781 0.037858004 #> 5 1.3046313 0.30516562 0.001125175 #> 6 0.9841236 1.61748779 0.003303827  ## Plot contributions viz_contributions(X)   ## Plot cos2 viz_cos2(X)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Individuals Factor Map \u2014 viz_individuals","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"Plots row/individual principal coordinates.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"","code":"viz_individuals(x, ...)  viz_rows(x, ...)  # S4 method for MultivariateAnalysis viz_rows(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapCA viz_rows(x, axes = c(1, 2), ...)  # S4 method for PCA viz_individuals(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"x CA PCA object. ... graphical parameters (see details). axes length-two numeric vector giving dimensions plotted. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels logical scalar: labels drawn? highlight vector specifying information highlighted. NULL (default), highlighting applied. mapped least one graphical parameters explicitly specified (see examples). single character string passed, must one \"observation\", \"mass\", \"sum\", \"contribution\" \"cos2\" (see augment()). unambiguous substring can given. main character string giving main title plot. sub character string giving subtitle plot. panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"viz_*() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"Commonly used graphical parameters : pch vector plotting characters symbols. can either single character integer code one set graphics symbols. cex numerical vector giving amount plotting characters symbols scaled relative default. col colors lines points. Multiple colors can specified point can given color. bg background color open plot symbols given pch = 21:25.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"careful: graphical parameters silently recycled.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Plot individuals viz_individuals(X, panel.last = graphics::grid())   ## Plot variables viz_variables(X, panel.last = graphics::grid())   ## Graphical parameters ## Continuous values viz_individuals(X, highlight = iris$Petal.Length, pch = 16)  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"))  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"),                 cex = c(1, 2))   viz_variables(X, highlight = \"contribution\",               col = grDevices::hcl.colors(12, \"BluGrn\", rev = TRUE),               lwd = c(1, 5))   ## Discrete values viz_individuals(X, highlight = iris$Species, pch = 21:23)  viz_individuals(X, highlight = iris$Species, pch = 21:23,                 bg = c(\"#004488\", \"#DDAA33\", \"#BB5566\"),                 col = \"black\")   viz_variables(X, highlight = c(\"Petal\", \"Petal\", \"Sepal\", \"Sepal\"),               col = c(\"#EE7733\", \"#0077BB\"),               lty = c(1, 3))","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Variables Factor Map \u2014 viz_variables","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"Plots column/variable principal coordinates.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"","code":"viz_variables(x, ...)  viz_columns(x, ...)  # S4 method for MultivariateAnalysis viz_columns(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapCA viz_columns(x, axes = c(1, 2), ...)  # S4 method for PCA viz_variables(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = TRUE,   highlight = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapPCA viz_variables(x, axes = c(1, 2), ...)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"x CA PCA object. ... graphical parameters (see details). axes length-two numeric vector giving dimensions plotted. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels logical scalar: labels drawn? highlight vector specifying information highlighted. NULL (default), highlighting applied. mapped least one graphical parameters explicitly specified (see examples). single character string passed, must one \"observation\", \"mass\", \"sum\", \"contribution\" \"cos2\" (see augment()). unambiguous substring can given. main character string giving main title plot. sub character string giving subtitle plot. panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"viz_*() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"Commonly used graphical parameters : pch vector plotting characters symbols. can either single character integer code one set graphics symbols. cex numerical vector giving amount plotting characters symbols scaled relative default. lty vector line types. lwd vector line widths. col colors lines points. Multiple colors can specified point can given color. bg background color open plot symbols given pch = 21:25.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"careful: graphical parameters silently recycled.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Plot individuals viz_individuals(X, panel.last = graphics::grid())   ## Plot variables viz_variables(X, panel.last = graphics::grid())   ## Graphical parameters ## Continuous values viz_individuals(X, highlight = iris$Petal.Length, pch = 16)  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"))  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"),                 cex = c(1, 2))   viz_variables(X, highlight = \"contribution\",               col = grDevices::hcl.colors(12, \"BluGrn\", rev = TRUE),               lwd = c(1, 5))   ## Discrete values viz_individuals(X, highlight = iris$Species, pch = 21:23)  viz_individuals(X, highlight = iris$Species, pch = 21:23,                 bg = c(\"#004488\", \"#DDAA33\", \"#BB5566\"),                 col = \"black\")   viz_variables(X, highlight = c(\"Petal\", \"Petal\", \"Sepal\", \"Sepal\"),               col = c(\"#EE7733\", \"#0077BB\"),               lty = c(1, 3))","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Envelopes \u2014 viz_wrap","title":"Plot Envelopes \u2014 viz_wrap","text":"Plot Envelopes","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Envelopes \u2014 viz_wrap","text":"","code":"viz_hull(x, ...)  viz_confidence(x, ...)  viz_tolerance(x, ...)  # S4 method for MultivariateAnalysis viz_tolerance(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95, ...)  # S4 method for BootstrapCA viz_tolerance(x, margin = 1, axes = c(1, 2), level = 0.95, ...)  # S4 method for MultivariateAnalysis viz_confidence(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95, ...)  # S4 method for BootstrapCA viz_confidence(x, margin = 1, axes = c(1, 2), level = 0.95, ...)  # S4 method for MultivariateAnalysis viz_hull(x, margin = 1, axes = c(1, 2), group = NULL, ...)  # S4 method for BootstrapCA viz_hull(x, margin = 1, axes = c(1, 2), ...)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Envelopes \u2014 viz_wrap","text":"x object wrap observations (CA PCA object). ... graphical parameters passed graphics::polygon(). margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. group vector specifying group observation belongs . level numeric vector specifying confidence/tolerance level.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Envelopes \u2014 viz_wrap","text":"viz_*()called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Envelopes \u2014 viz_wrap","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Envelopes \u2014 viz_wrap","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Convex hull coordinates hulls <- wrap_hull(X, margin = 1, group = iris$Species)  ## Confidence ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species,                         level = c(0.68, 0.95))  ## Tolerance ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species, level = 0.95)  ## Plot with convex hulls col <- c(\"#004488\", \"#DDAA33\", \"#BB5566\") viz_rows(X, highlight = iris$Species, col = col) viz_hull(X, group = iris$Species, border = col)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrap Observations \u2014 wrap","title":"Wrap Observations \u2014 wrap","text":"wrap_hull() computes convex hull set observations. wrap_confidence() computes confidence ellipse. wrap_tolerance() computes tolerance ellipse.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrap Observations \u2014 wrap","text":"","code":"wrap_hull(x, ...)  wrap_confidence(x, ...)  wrap_tolerance(x, ...)  # S4 method for MultivariateAnalysis wrap_confidence(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95)  # S4 method for MultivariateAnalysis wrap_tolerance(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95)  # S4 method for MultivariateAnalysis wrap_hull(x, margin = 1, axes = c(1, 2), group = NULL)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrap Observations \u2014 wrap","text":"x object wrap observations (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. group vector specifying group observation belongs . level numeric vector specifying confidence/tolerance level.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrap Observations \u2014 wrap","text":"wrap_*() returns data.frame envelope x y coordinates. extra column named group added specifying group observation belongs .","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Wrap Observations \u2014 wrap","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrap Observations \u2014 wrap","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Convex hull coordinates hulls <- wrap_hull(X, margin = 1, group = iris$Species)  ## Confidence ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species,                         level = c(0.68, 0.95))  ## Tolerance ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species, level = 0.95)  ## Plot with convex hulls col <- c(\"#004488\", \"#DDAA33\", \"#BB5566\") viz_rows(X, highlight = iris$Species, col = col) viz_hull(X, group = iris$Species, border = col)","package":"dimensio"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"heterogeneity-and-evenness","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity","what":"Heterogeneity and Evenness","title":"Diversity Measures","text":"Diversity measurement assumes individuals specific taxa equivalent types equally different (Peet 1974). measure diversity can achieved using indices built relative abundance taxa. indices (sometimes referred non-parametric indices) benefit making assumptions underlying distribution taxa abundance: take relative abundances species present species richness account. Peet (1974) refers indices heterogeneity (\\(H\\)). Diversity indices focus one aspect taxa abundance emphasize either richness (weighting towards uncommon taxa) dominance [weighting towards abundant taxa; Magurran (1988)]. Evenness (\\(E\\)) measure evenly individuals distributed across sample.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"shannon-wiener-diversity-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness > Information theory index","what":"Shannon-Wiener diversity index","title":"Diversity Measures","text":"Shannon-Wiener index (Shannon 1948) assumes individuals randomly sampled infinite population taxa represented sample (reflect sample size). main source error arises failure include taxa sample: error increases proportion species discovered sample declines (Peet 1974; Magurran 1988). maximum likelihood estimator (MLE) used relative abundance, known negatively biased sample size. Heterogeneity infinite sample: \\[ H' = - \\sum_{= 1}^{S} p_i \\ln p_i \\] Heterogeneity finite sample: \\[ H' = - \\sum_{= 1}^{S} \\frac{n_i}{N} \\ln \\frac{n_i}{N} \\] Evenness: \\[ E = \\frac{H}{H_{max}} = \\frac{H'}{\\ln S} = - \\sum_{= 1}^{S} p_i \\log_S p_i \\] \\(p_i\\) unknown population, estimate given \\(\\hat{p}_i =\\frac{n_i}{N}\\) (maximum likelihood estimator - MLE). use \\(\\hat{p}_i\\) results biased estimate, Hutcheson (1970) Bowman et al. (1971) suggest use : \\[ \\hat{H}' = - \\sum_{= 1}^{S} \\hat{p}_i \\ln \\hat{p}_i - \\frac{S - 1}{N} + \\frac{1 - \\sum_{= 1}^{S} \\hat{p}_i^{-1}}{12N^2} + \\frac{\\sum_{= 1}^{S} (\\hat{p}_i^{-1} - \\hat{p}_i^{-2})}{12N^3} + \\cdots \\] error rarely significant (Peet 1974), unbiased form implemented (now).","code":"heterogeneity(mississippi, method = \"shannon\") #>  [1] 1.2027955 0.7646565 0.9293974 0.8228576 0.7901428 0.9998430 1.2051989 #>  [8] 1.1776226 1.1533432 1.2884172 1.1725355 1.5296294 1.7952443 1.1627477 #> [15] 1.0718463 0.9205717 1.1751002 0.7307620 1.1270126 1.0270291 evenness(mississippi, method = \"shannon\") #>  [1] 0.8676335 0.5515831 0.5187066 0.5112702 0.4909433 0.9100964 0.7488322 #>  [8] 0.7316981 0.6436931 0.7190793 0.5638704 0.7860740 0.8633300 0.5049749 #> [15] 0.4654969 0.4427014 0.5651037 0.3514222 0.4894554 0.4938966","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"brillouin-diversity-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness > Information theory index","what":"Brillouin diversity index","title":"Diversity Measures","text":"Brillouin index (Brillouin 1956) describes known collection: assume random sampling infinite population. Pielou (1975) Laxton (1978) argues use Brillouin index circumstances, especially preference Shannon index. Diversity: \\[ H' = \\frac{\\ln (N!) - \\sum_{= 1}^{S} \\ln (n_i!)}{N} \\] Evenness: \\[ E = \\frac{H'}{H'_{max}} \\] : \\[ H'_{max} = \\frac{1}{N} \\ln \\frac{N!}{\\left( \\lfloor \\frac{N}{S} \\rfloor! \\right)^{S - r} \\left[ \\left( \\lfloor \\frac{N}{S} \\rfloor + 1 \\right)! \\right]^{r}} \\] : \\(r = N - S \\lfloor \\frac{N}{S} \\rfloor\\).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"dominance-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness","what":"Dominance index","title":"Diversity Measures","text":"following methods return dominance index, reciprocal inverse form usually adopted, increase value index accompanies decrease diversity.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"simpson-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness > Dominance index","what":"Simpson index","title":"Diversity Measures","text":"Simpson index (Simpson 1949) expresses probability two individuals randomly picked finite sample belong two different types. can interpreted weighted mean proportional abundances. metric true probability value, ranges \\(0\\) (perfectly uneven) \\(1\\) (perfectly even). Dominance infinite sample: \\[ D = \\sum_{= 1}^{S} p_i^2 \\] Dominance finite sample: \\[ D = \\sum_{= 1}^{S} \\frac{n_i \\left( n_i - 1 \\right)}{N \\left( N - 1 \\right)} \\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"mcintosh-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness > Dominance index","what":"McIntosh index","title":"Diversity Measures","text":"McIntosh index (McIntosh 1967) expresses heterogeneity sample geometric terms. describes sample point \\(S\\)-dimensional hypervolume uses Euclidean distance point origin. Dominance: \\[ D = \\frac{N - U}{N - \\sqrt{N}} \\] Evenness: \\[ E = \\frac{N - U}{N - \\frac{N}{\\sqrt{S}}} \\] \\(U\\) distance sample origin \\(S\\) dimensional hypervolume: \\[U = \\sqrt{\\sum_{= 1}^{S} n_i^2}\\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"berger-parker-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness > Dominance index","what":"Berger-Parker index","title":"Diversity Measures","text":"Berger-Parker index (Berger Parker 1970) expresses proportional importance abundant type. metric highly biased sample size richness, moreover make use information available sample. Dominance: \\[ D = \\frac{n_{max}}{N} \\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"richness-and-rarefaction","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity","what":"Richness and Rarefaction","title":"Diversity Measures","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa [e.g.\u00a0open system; Peet (1974)]. alternative, richness (\\(R\\)) can used concept taxa number (McIntosh 1967). Richness refers variety taxa/species/types present assemblage community (Bobrowsky Ball 1989) \u201cnumber species present collection containing specified number individuals\u201d (Hurlbert 1971). always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (\\(\\hat{S}\\)) number taxa expected samples standard size \\(n\\) (.e.\u00a0taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.  Richness measures (Bobrowsky Ball 1989). *: implemented. Rarefaction measures (Bobrowsky Ball 1989). *: implemented. : \\(S\\) number observed species/types, \\(N_i\\) number individuals \\(\\)-th species/type, \\(N = \\sum_{= 1}^{S} N_i\\) total number individuals, \\(\\) area isolate collection. \\(m\\) number individuals rarest species/type. \\(\\alpha\\) Fisher\u2019s slope constant, \\(y_{0}\\) number species/types modal class interval, \\(\\hat{\\sigma}\\) estimate standard deviation, \\(n\\) sub-sample size, \\(R\\) constant rate increment, \\(\\hat{S}\\) number expected predicted species/types, \\(k\\), \\(d\\), \\(\\) \\(b\\) empirically derived coefficients regression.","code":"richness(mississippi, method = \"margalef\") #>  [1] 0.5963696 0.4524421 0.6971143 0.6193544 0.5599404 0.4577237 0.7292886 #>  [8] 0.7779583 1.0304965 0.9224182 1.1892416 1.1412278 1.5518107 1.2645413 #> [15] 1.2090820 1.0903435 1.1570758 1.1892416 1.2552092 1.0158754 composition(mississippi, method = \"chao1\") #>  [1]  4.000000  4.000000  6.000000  5.000000  5.000000  3.000000  5.000000 #>  [8]  5.000000  7.984375  6.000000  8.000000  7.000000  8.494505 10.000000 #> [15] 10.000000  8.998371  8.498821  8.249306 10.000000  8.499491 ## Baxter rarefaction RA <- rarefaction(mississippi, sample = 100, method = \"baxter\") plot(RA)","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"chao1-estimator-chao1984","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Richness and Rarefaction > Asymptotic Species Richness","what":"Chao1 estimator (Chao 1984)","title":"Diversity Measures","text":"\\[ \\hat{S}_{Chao1} = \\begin{cases}   S + \\frac{N - 1}{N} \\frac{s_1^2}{2 s_2} & s_2 > 0 \\\\   S + \\frac{N - 1}{N} \\frac{s_1 (s_1 - 1)}{2} & s_2 = 0 \\end{cases} \\] special case homogeneous case, bias-corrected estimator : \\[ \\hat{S}_{bcChao1} = S + \\frac{N - 1}{N} \\frac{s_1 (s_1 - 1)}{2 s_2 + 1}\\] improved Chao1 estimator makes use additional information tripletons quadrupletons (Chiu et al. 2014): \\[ \\hat{S}_{iChao1} = \\hat{S}_{Chao1} + \\frac{N - 3}{4 N} \\frac{s_3}{s_4} \\times \\max\\left(s_1 - \\frac{N - 3}{N - 1} \\frac{s_2 s_3}{2 s_4} , 0\\right)\\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"abundance-based-coverage-estimator-chao1992","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Richness and Rarefaction > Asymptotic Species Richness","what":"Abundance-based Coverage Estimator (Chao and Lee 1992)","title":"Diversity Measures","text":"\\[ \\hat{S}_{ACE} = \\hat{S}_{abun} + \\frac{\\hat{S}_{rare}}{\\hat{C}_{rare}} + \\frac{s_1}{\\hat{C}_{rare}} \\times \\hat{\\gamma}^2_{rare} \\] \\(\\hat{S}_{rare} = \\sum_{= 1}^{k} s_i\\) number rare taxa, \\(\\hat{S}_{abun} = \\sum_{> k}^{N} s_i\\) number abundant taxa (given cut-value \\(k\\)), \\(\\hat{C}_{rare} = 1 - \\frac{s_1}{N_{rare}}\\) Turing\u2019s coverage estimate : \\[ \\hat{\\gamma}^2_{rare} = \\max\\left[\\frac{\\hat{S}_{rare}}{\\hat{C}_{rare}} \\frac{\\sum_{= 1}^{k} (- 1)s_i}{\\left(\\sum_{= 1}^{k} is_i\\right)\\left(\\sum_{= 1}^{k} is_i - 1\\right)} - 1, 0\\right]  \\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"chao2-estimator-chao1987","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Richness and Rarefaction > Asymptotic Species Richness","what":"Chao2 estimator (Chao 1987)","title":"Diversity Measures","text":"replicated incidence data (.e.\u00a0\\(m \\times p\\) logical matrix), Chao2 estimator : \\[ \\hat{S}_{Chao2} = \\begin{cases}   S + \\frac{m - 1}{m} \\frac{q_1^2}{2 q_2} & q_2 > 0 \\\\   S + \\frac{m - 1}{m} \\frac{q_1 (q_1 - 1)}{2} & q_2 = 0 \\end{cases} \\] Improved Chao2 estimator (Chiu et al. 2014): \\[ \\hat{S}_{iChao2} = \\hat{S}_{Chao2} + \\frac{m - 3}{4 m} \\frac{q_3}{q_4} \\times \\max\\left(q_1 - \\frac{m - 3}{m - 1} \\frac{q_2 q_3}{2 q_4} , 0\\right)\\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"incidence-based-coverage-estimator-chao2016","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Richness and Rarefaction > Asymptotic Species Richness","what":"Incidence-based Coverage Estimator (Chao and Chiu 2016)","title":"Diversity Measures","text":"\\[ \\hat{S}_{ICE} = \\hat{S}_{freq} + \\frac{\\hat{S}_{infreq}}{\\hat{C}_{infreq}} + \\frac{q_1}{\\hat{C}_{infreq}} \\times \\hat{\\gamma}^2_{infreq} \\] \\(\\hat{S}_{infreq} = \\sum_{= 1}^{k} q_i\\) number infrequent taxa, \\(\\hat{S}_{freq} = \\sum_{> k}^{N} q_i\\) number frequent taxa (given cut-value \\(k\\)), \\(\\hat{C}_{infreq} = 1 - \\frac{Q_1}{\\sum_{= 1}^{k} iq_i}\\) Turing\u2019s coverage estimate : \\[ \\hat{\\gamma}^2_{infreq} = \\max\\left[\\frac{\\hat{S}_{infreq}}{\\hat{C}_{infreq}} \\frac{m_{infreq}}{m_{infreq} - 1} \\frac{\\sum_{= 1}^{k} (- 1)q_i}{\\left(\\sum_{= 1}^{k} iq_i\\right)\\left(\\sum_{= 1}^{k} iq_i - 1\\right)} - 1, 0\\right]  \\] \\(m_{infreq}\\) number sampling units include least one infrequent species.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"turnover","dir":"Articles","previous_headings":"\\(\\beta\\)-diversity","what":"Turnover","title":"Diversity Measures","text":"following methods can used ascertain degree turnover taxa composition along gradient qualitative (presence/absence) data. assumes order matrix rows (1 \\(m\\)) follows progression along gradient/transect. denote \\(m \\times p\\) incidence matrix \\(X = \\left[ x_{ij} \\right] ~\\forall \\\\left[ 1,m \\right], j \\\\left[ 1,p \\right]\\) \\(p \\times p\\) corresponding co-occurrence matrix \\(Y = \\left[ y_{ij} \\right] ~\\forall ,j \\\\left[ 1,p \\right]\\), row column sums: \\[\\begin{align} x_{\\cdot} = \\sum_{j = 1}^{p} x_{ij} && x_{\\cdot j} = \\sum_{= 1}^{m} x_{ij} && x_{\\cdot \\cdot} = \\sum_{j = 1}^{p} \\sum_{= 1}^{m} x_{ij} && \\forall x_{ij} \\\\lbrace 0,1 \\rbrace \\\\  y_{\\cdot} = \\sum_{j \\geqslant }^{p} y_{ij} && y_{\\cdot j} = \\sum_{\\leqslant j}^{p} y_{ij} && y_{\\cdot \\cdot} = \\sum_{= 1}^{p} \\sum_{j \\geqslant }^{p} y_{ij} && \\forall y_{ij} \\\\lbrace 0,1 \\rbrace \\end{align}\\] Turnover measures. *: implemented. : \\(\\alpha\\) mean sample diversity: \\(\\alpha = \\frac{x_{\\cdot \\cdot}}{m}\\), \\(g(H)\\) number taxa gained along transect, \\(l(H)\\) number taxa lost along transect.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"similarity","dir":"Articles","previous_headings":"\\(\\beta\\)-diversity","what":"Similarity","title":"Diversity Measures","text":"Similarity two samples \\(\\) \\(b\\) two types \\(x\\) \\(y\\) can measured follow. indices provide scale similarity \\(0\\)-\\(1\\) \\(1\\) perfect similarity \\(0\\) similarity, exception Brainerd-Robinson index scaled \\(0\\) \\(200\\). Qualitative similarity measures (samples). *: implemented. Quantitative similarity measures (samples). *: implemented. Quantitative similarity measures (types). *: implemented. : \\(S_a\\) \\(S_b\\) denote total number taxa observed samples \\(\\) \\(b\\), respectively, \\(N_a\\) \\(N_b\\) denote total number individuals samples \\(\\) \\(b\\), respectively, \\(a_j\\) \\(b_j\\) denote number individuals \\(j\\)-th type/taxon, \\(j \\\\left[ 1,S \\right]\\), \\(x_i\\) \\(y_i\\) denote number individuals \\(\\)-th sample/case, \\(\\\\left[ 1,m \\right]\\), \\(o_i\\) denotes number sample/case common type/taxon: \\(o_i = \\sum_{k = 1}^{m} x_k \\cap y_k\\), \\(o_j\\) denotes number type/taxon common sample/case: \\(o_j = \\sum_{k = 1}^{S} a_k \\cap b_k\\).","code":"## Brainerd-Robinson (similarity between assemblages) BR <- similarity(mississippi, method = \"brainerd\") plot_spot(BR, col = khroma::colour(\"YlOrBr\")(12)) ## Binomial co-occurrence (similarity between types) BI <- similarity(mississippi, method = \"binomial\") plot_spot(BI, col = khroma::colour(\"PRGn\")(12))","package":"tabula"},{"path":[],"package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Analysis and Visualization of Archaeological Count Data","text":"easy way examine archaeological count data. package provides several tests measures diversity: heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). allows easily visualize count data statistical thresholds: rank vs.\u00a0abundance plots, heatmaps, Ford (1962) Bertin (1977) diagrams, etc. tabula provides methods : Diversity measurement: heterogeneity(), evenness(), richness(), rarefaction(), turnover() Similarity measurement co-occurrence: similarity(), occurrence() Assessing sample size significance: bootstrap(), jackknife(), simulate() Bertin (1977) Ford (1962) (battleship curve) diagrams: plot_bertin(), plot_ford() Seriograph (Desachy 2004): seriograph() Heatmaps: plot_heatmap(), plot_spot() kairos companion package tabula provides functions chronological modeling dating archaeological assemblages count data.","code":"To cite tabula in publications use:    Frerebeau N (2019). \"tabula: An R Package for Analysis, Seriation,   and Visualization of Archaeological Count Data.\" _Journal of Open   Source Software_, *4*(44). doi:10.21105/joss.01821   <https://doi.org/10.21105/joss.01821>.    Frerebeau N (2023). _tabula: Analysis and Visualization of   Archaeological Count Data_. Universit\u00e9 Bordeaux Montaigne, Pessac,   France. doi:10.5281/zenodo.1489944   <https://doi.org/10.5281/zenodo.1489944>, R package version 3.0.0,   <https://packages.tesselle.org/tabula/>.  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analysis and Visualization of Archaeological Count Data","text":"can install released version tabula CRAN : development version GitHub :","code":"install.packages(\"tabula\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/tabula\")","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Analysis and Visualization of Archaeological Count Data","text":"assumes keep data tidy: variable (type/taxa) must saved column observation (sample/case) must saved row.","code":"## Install extra packages (if needed) # install.packages(\"folio\")  ## Load packages library(tabula) ## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\")  ## Ford diagram plot_ford(mississippi) ## Co-occurrence of ceramic types mississippi |>    occurrence() |>    plot_spot() ## Data from Conkey 1980, Kintigh 1989, p. 28 data(\"chevelon\", package = \"folio\")  ## Measure diversity by comparing to simulated assemblages set.seed(12345)  chevelon |>   heterogeneity(method = \"shannon\") |>   simulate() |>   plot()  chevelon |>   richness(method = \"count\") |>   simulate() |>   plot()","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Analysis and Visualization of Archaeological Count Data","text":"Please note tabula project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/paper.html","id":null,"dir":"","previous_headings":"","what":"Background","title":"Background","text":"Detecting quantifying material cultural variations time space important methodological issues archaeology. solve issues, need construct reliable chronologies quantitative descriptions archaeological assemblages, . e. archaeological sites intrasite units, described set p different objects. Building chronologies involves distinguishing relative (providing chronological sequence) absolute dating methods (yield calendric indicators) [@obrien2002]. Within relative dating, matrix seriation long-established method\u2014first formulated @petrie1899\u2014allowed construction reference chronologies [@ihm2005]. set X n archaeological assemblages, seriation problem comes discovering X order inferred chronological. approach relies set well-defined statistical archaeological assumptions [@dunnell1970]. may use priori information, e.g., absolute dates stratigraphical constraints [@poblome2003] allows analysis chronological patterns socio-economic cultural perspective (e.g., [@bellanger2012], [@lipo2015]). quantitative analysis archaeological assemblages can thus carried synchronic, e.g., diversity measurements, diachronic, e.g., evolutionary studies: selection process, patterns cultural transmission, etc., way. approaches cover wide range applications led development multitude statistical models, none systematically implemented enable deployment reproducible workflows.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/paper.html","id":"summary","dir":"","previous_headings":"","what":"Summary","title":"Background","text":"tabula provides convenient reproducible toolkit analyzing, seriating, visualizing archaeological count data, artifacts faunal remains. Several R packages, e.g.\u00a0ade4 [@dray2007], SpadeR [@chao2016] vegan [@oksanen2019], allow estimation diversity indices implement seriation/ordination methods, packages mainly oriented towards ecological issues. tabula provides archaeologically-orientated implementations allow integration specific data (dates, stratigraphy, etc.) offers consistent framework. latter particular value since tabula designed used archaeologists students little background courses dating methods applied statistics archaeology. package uses set S4 classes archaeological data matrices extend matrix data type. new classes represent different specialized matrices: incidence, abundance, co-occurrence, (dis)similarity. Methods variety functions applied objects classes provide tools relative absolute dating analysis (chronological) patterns. tabula includes functions matrix seriation (seriate_*), well chronological modeling dating (date_*) archaeological assemblages objects. Resulting models can checked stability refined resampling methods (refine_*). Estimated dates can displayed tempo activity plot [@dye2016] assess rhythms long periods. Beyond , tabula provides several tests (test_*) measures diversity within archaeological assemblages (index_*): heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). Finally, package makes easy visualize count data statistical thresholds (plot_*): rank vs.\u00a0abundance plots, heatmaps, @ford1962 @bertin1977 diagrams.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/paper.html","id":"acknowledgements","dir":"","previous_headings":"","what":"Acknowledgements","title":"Background","text":"following contributors made possible develop project helpful discussion bringing new ideas: Jean-Baptiste Fourvel, Brice Lebrun, Ben Marwick, Matthew Peeples, Anne Philippe.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":[],"package":"tabula"},{"path":[],"package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Composition Index \u2014 CompositionIndex-class","title":"Composition Index \u2014 CompositionIndex-class","text":"S4 class represent composition measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Composition Index \u2014 CompositionIndex-class","text":"class inherits DiversityIndex.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Composition Index \u2014 CompositionIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Index \u2014 DiversityIndex-class","title":"Diversity Index \u2014 DiversityIndex-class","text":"S4 class represent diversity measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Diversity Index \u2014 DiversityIndex-class","text":".Data numeric vector diversity measures. labels character vector giving sample names. size integer vector giving sample sizes. data numeric matrix count data. method character string specifying method used. simulation four columns numeric matrix giving diversity measures simulated assemblage (sample size, mean estimate, lower upper boundaries confidence interval).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Diversity Index \u2014 DiversityIndex-class","text":"class inherits base numeric.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Diversity Index \u2014 DiversityIndex-class","text":"code snippets , x DiversityIndex object. .data.frame(x) Coerces data.frame.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Index \u2014 DiversityIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Evenness Index \u2014 EvennessIndex-class","title":"Evenness Index \u2014 EvennessIndex-class","text":"S4 class represent evenness measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Evenness Index \u2014 EvennessIndex-class","text":"class inherits DiversityIndex.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Evenness Index \u2014 EvennessIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity Index \u2014 HeterogeneityIndex-class","title":"Heterogeneity Index \u2014 HeterogeneityIndex-class","text":"S4 class represent heterogeneity measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Heterogeneity Index \u2014 HeterogeneityIndex-class","text":"class inherits DiversityIndex.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity Index \u2014 HeterogeneityIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Index \u2014 RarefactionIndex-class","title":"Rarefaction Index \u2014 RarefactionIndex-class","text":"S4 class represent rarefaction measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Rarefaction Index \u2014 RarefactionIndex-class","text":".Data numeric matrix count data (expected taxa per fixed number individuals). labels character vector giving sample names. size integer vector giving sample sizes. method character string specifying method used.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rarefaction Index \u2014 RarefactionIndex-class","text":"class inherits base matrix.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Index \u2014 RarefactionIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness Index \u2014 RichnessIndex-class","title":"Richness Index \u2014 RichnessIndex-class","text":"S4 class represent richness measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Richness Index \u2014 RichnessIndex-class","text":"class inherits DiversityIndex.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness Index \u2014 RichnessIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":null,"dir":"Reference","previous_headings":"","what":"Birds Species and Abundances \u2014 aves","title":"Birds Species and Abundances \u2014 aves","text":"dataset birds species abundances managed unmanaged areas along River Wye (UK).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Birds Species and Abundances \u2014 aves","text":"","code":"aves","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Birds Species and Abundances \u2014 aves","text":"data.frame 2 rows 26 variables (bird species).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Birds Species and Abundances \u2014 aves","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Estimation \u2014 bootstrap_diversity","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"Samples randomly elements object replacement.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"","code":"# S4 method for DiversityIndex bootstrap(object, n = 1000, f = NULL)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"object R object (typically DiversityIndex object). n non-negative integer giving number bootstrap replications. f function takes single numeric vector (result ) argument.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"f NULL (default), bootstrap() returns named numeric vector following elements: original observed value applied object. mean bootstrap estimate mean . bias bootstrap estimate bias . error bootstrap estimate standard error . f function, bootstrap() returns result f applied n values .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187  ## Bootstrap resampling bootstrap(h, f = NULL) #>                  original     mean         bias      error #> Altamira         3.269200 3.281415  0.012215108 0.08094605 #> Cueto de la Mina 2.955298 2.976502  0.021203835 0.14867535 #> El Juyo          2.491683 2.533877  0.042194118 0.19348027 #> El Cierro        2.485604 2.492820  0.007216276 0.21902418 #> La Paloma        2.329187 2.322210 -0.006976781 0.26597814  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         2.980130 3.227869 3.285167 3.278950 3.332218 3.526771 #> Cueto de la Mina 2.379574 2.877482 2.974412 2.969057 3.075389 3.376285 #> El Juyo          1.364750 2.402382 2.550036 2.532691 2.673939 3.090122 #> El Cierro        1.349792 2.341877 2.485570 2.472341 2.624417 3.106595 #> La Paloma        1.329661 2.176777 2.345239 2.323145 2.493576 2.986485  quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant) #>                       25%      50% #> Altamira         3.218863 3.279698 #> Cueto de la Mina 2.878694 2.981459 #> El Juyo          2.406119 2.541281 #> El Cierro        2.353266 2.509887 #> La Paloma        2.143952 2.331598  ## Jackknife resampling jackknife(h) #>                  original     mean       bias      error #> Altamira         3.269200 3.246457 -0.9779158 0.08145529 #> Cueto de la Mina 2.955298 2.932856 -0.9649994 0.15907033 #> El Juyo          2.491683 2.469588 -0.9500688 0.19670002 #> El Cierro        2.485604 2.462723 -0.9838709 0.22195573 #> La Paloma        2.329187 2.305876 -1.0023704 0.25266291  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         2.907398 3.227045 3.285983 3.280298 3.334200 3.475782 #> Cueto de la Mina 2.422481 2.887438 2.982113 2.980356 3.077014 3.442116 #> El Juyo          1.718474 2.400674 2.534033 2.520185 2.659175 3.121564 #> El Cierro        1.524290 2.347204 2.498433 2.487297 2.647794 3.099865 #> La Paloma        1.098612 2.163956 2.347580 2.319130 2.506859 2.916372","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":null,"dir":"Reference","previous_headings":"","what":"Early Magdalenian Engraved Bones \u2014 cantabria","title":"Early Magdalenian Engraved Bones \u2014 cantabria","text":"dataset design elements engraved bones Cantabrian Spain.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Early Magdalenian Engraved Bones \u2014 cantabria","text":"","code":"cantabria","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Early Magdalenian Engraved Bones \u2014 cantabria","text":"data.frame 5 rows 44 variables (designs).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Early Magdalenian Engraved Bones \u2014 cantabria","text":"Conkey, M. W. (1980). Identification prehistoric hunter-gatherer aggregation sites: case Altamira. Current Anthropology, 21(5), 609-630. Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity and Evenness \u2014 heterogeneity","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"heterogeneity() returns heterogeneity dominance index. evenness() returns evenness measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"","code":"heterogeneity(object, ...)  evenness(object, ...)  index_berger(x, ...)  index_boone(x, ...)  index_brillouin(x, ...)  index_mcintosh(x, ...)  index_shannon(x, ...)  index_simpson(x, ...)  # S4 method for matrix heterogeneity(   object,   method = c(\"berger\", \"boone\", \"brillouin\", \"mcintosh\", \"shannon\", \"simpson\"),   j = NULL )  # S4 method for data.frame heterogeneity(   object,   method = c(\"berger\", \"boone\", \"brillouin\", \"mcintosh\", \"shannon\", \"simpson\"),   j = NULL )  # S4 method for matrix evenness(object, method = c(\"shannon\", \"brillouin\", \"mcintosh\", \"simpson\"))  # S4 method for data.frame evenness(object, method = c(\"shannon\", \"brillouin\", \"mcintosh\", \"simpson\"))  # S4 method for numeric index_berger(x, na.rm = FALSE, ...)  # S4 method for matrix index_boone(x, j = NULL, na.rm = FALSE, ...)  # S4 method for numeric index_brillouin(x, evenness = FALSE, na.rm = FALSE, ...)  # S4 method for numeric index_mcintosh(x, evenness = FALSE, na.rm = FALSE, ...)  # S4 method for numeric index_shannon(x, evenness = FALSE, base = exp(1), na.rm = FALSE, ...)  # S4 method for numeric index_simpson(x, evenness = FALSE, na.rm = FALSE, ...)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. x numeric vector count data (absolute frequencies). method character string specifying index computed (see details). unambiguous substring can given. j integer giving index reference type/taxa. NULL (default), frequent type/taxa assemblage used. na.rm numeric scalar: missing values (including NaN) removed? evenness logical scalar: evenness measure computed instead heterogeneity/dominance index? base positive numeric value specifying base respect logarithms computed.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"heterogeneity() returns HeterogeneityIndex object. evenness() returns EvennessIndex object. index_*() return numeric vector.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"Diversity measurement assumes individuals specific taxa equivalent types equally different (Peet 1974). measure diversity can achieved using indices built relative abundance taxa. indices (sometimes referred non-parametric indices) benefit making assumptions underlying distribution taxa abundance: take relative abundances species present species richness account. Peet (1974) refers indices heterogeneity. Diversity indices focus one aspect taxa abundance emphasize either richness (weighting towards uncommon taxa) dominance (weighting towards abundant taxa; Magurran 1988). Evenness measure evenly individuals distributed across sample.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"Ramanujan approximation used \\(x!\\) computation \\(x > 170\\).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"heterogeneity-and-evenness-measures","dir":"Reference","previous_headings":"","what":"Heterogeneity and Evenness Measures","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"following heterogeneity index corresponding evenness measures available (see Magurran 1988 details): berger Berger-Parker dominance index. Berger-Parker index expresses proportional importance abundant type. metric highly biased sample size richness, moreover make use information available sample. boone Boone heterogeneity measure. brillouin Brillouin diversity index. Brillouin index describes known collection: assume random sampling infinite population. Pielou (1975) Laxton (1978) argues use Brillouin index circumstances, especially preference Shannon index. mcintosh McIntosh dominance index. McIntosh index expresses heterogeneity sample geometric terms. describes sample point \\(S\\)-dimensional hypervolume uses Euclidean distance point origin. shannon Shannon-Wiener diversity index. Shannon index assumes individuals randomly sampled infinite population taxa represented sample (reflect sample size). main source error arises failure include taxa sample: error increases proportion species discovered sample declines (Peet 1974, Magurran 1988). maximum likelihood estimator (MLE) used relative abundance, known negatively biased sample size. simpson Simpson dominance index finite sample. Simpson index expresses probability two individuals randomly picked finite sample belong two different types. can interpreted weighted mean proportional abundances. metric true probability value, ranges \\(0\\) (perfectly uneven) \\(1\\) (perfectly even). berger, mcintosh simpson methods return dominance index, reciprocal inverse form usually adopted, increase value index accompanies decrease diversity.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"Berger, W. H. & Parker, F. L. (1970). Diversity Planktonic Foraminifera Deep-Sea Sediments. Science, 168(3937), 1345-1347. doi:10.1126/science.168.3937.1345 . Boone, J. L. (1987). Defining Measuring Midden Catchment. American Antiquity, 52(2), 336-45. doi:10.2307/281785 . Brillouin, L. (1956). Science information theory. New York: Academic Press. Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36. Laxton, R. R. (1978). measure diversity. Journal Theoretical Biology, 70(1), 51-67. doi:10.1016/0022-5193(78)90302-8 . Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . McIntosh, R. P. (1967). Index Diversity Relation Certain Concepts Diversity. Ecology, 48(3), 392-404. doi:10.2307/1932674 . Peet, R. K. (1974). Measurement Species Diversity. Annual Review Ecology Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441 . Pielou, E. C. (1975). Ecological Diversity. New York: Wiley. doi:10.4319/lo.1977.22.1.0174b Shannon, C. E. (1948). Mathematical Theory Communication. Bell System Technical Journal, 27, 379-423. doi:10.1002/j.1538-7305.1948.tb01338.x . Simpson, E. H. (1949). Measurement Diversity. Nature, 163(4148), 688-688. doi:10.1038/163688a0 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187 (e <- evenness(cantabria, method = \"shannon\")) #> [1] 0.8987278 0.8966760 0.8462335 0.9178574 0.9373336  plot(h)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":null,"dir":"Reference","previous_headings":"","what":"Jackknife Estimation \u2014 jackknife_diversity","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"Jackknife Estimation","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"","code":"# S4 method for DiversityIndex jackknife(object, f = NULL)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"object R object (typically DiversityIndex object). f function takes single numeric vector (leave-one-values ) argument.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"f NULL (default), jackknife() returns named numeric vector following elements: original observed value applied object. mean jackknife estimate mean . bias jackknife estimate bias . error jackknife estimate standard error . f function, jackknife() returns result f applied leave-one-values .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187  ## Bootstrap resampling bootstrap(h, f = NULL) #>                  original     mean         bias      error #> Altamira         3.269200 3.278206  0.009006676 0.08307194 #> Cueto de la Mina 2.955298 2.972727  0.017429550 0.15398977 #> El Juyo          2.491683 2.512902  0.021218780 0.20477828 #> El Cierro        2.485604 2.489387  0.003782870 0.21801177 #> La Paloma        2.329187 2.304147 -0.025039501 0.26161788  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         3.025561 3.223493 3.274640 3.274596 3.331732 3.510869 #> Cueto de la Mina 2.510647 2.879026 2.986806 2.977599 3.078147 3.392700 #> El Juyo          1.867719 2.400420 2.540724 2.524350 2.667965 3.082037 #> El Cierro        1.671531 2.369194 2.515860 2.495118 2.651108 3.035360 #> La Paloma        1.213008 2.163956 2.343947 2.316331 2.499333 3.061994  quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant) #>                       25%      50% #> Altamira         3.229519 3.285076 #> Cueto de la Mina 2.878630 2.980912 #> El Juyo          2.396635 2.532030 #> El Cierro        2.366988 2.504375 #> La Paloma        2.145842 2.329187  ## Jackknife resampling jackknife(h) #>                  original     mean       bias      error #> Altamira         3.269200 3.246457 -0.9779158 0.08145529 #> Cueto de la Mina 2.955298 2.932856 -0.9649994 0.15907033 #> El Juyo          2.491683 2.469588 -0.9500688 0.19670002 #> El Cierro        2.485604 2.462723 -0.9838709 0.22195573 #> La Paloma        2.329187 2.305876 -1.0023704 0.25266291  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         3.045532 3.229899 3.283922 3.281962 3.334989 3.514534 #> Cueto de la Mina 2.301774 2.872228 2.977945 2.968116 3.075886 3.332967 #> El Juyo          1.695724 2.396411 2.537224 2.526913 2.674774 3.222411 #> El Cierro        1.723392 2.333030 2.496161 2.472532 2.637416 2.985356 #> La Paloma        1.277034 2.163956 2.338372 2.313336 2.491497 2.959830","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrigraph \u2014 matrigraph","title":"Matrigraph \u2014 matrigraph","text":"matrigraph() produces heatmap highlighting deviations independence. pvi() computes cell numeric matrix percentage column theoretical independence value.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrigraph \u2014 matrigraph","text":"","code":"matrigraph(object, ...)  pvi(object, ...)  # S4 method for matrix pvi(object)  # S4 method for data.frame pvi(object)  # S4 method for matrix matrigraph(object, reverse = FALSE, axes = TRUE, ...)  # S4 method for data.frame matrigraph(object, reverse = FALSE, ...)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrigraph \u2014 matrigraph","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. reverse logical scalar: negative deviations centered (see details)? axes logical scalar: axes drawn plot?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrigraph \u2014 matrigraph","text":"matrigraph() called side-effects: results graphic displayed (invisibly returns object). pvi() returns numeric matrix.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Matrigraph \u2014 matrigraph","text":"PVI (french \"pourcentages de valeur d'ind\u00e9pendance\") calculated cell percentage column theoretical independence value: PVI greater \\(1\\) represent positive deviations independence, whereas PVI smaller \\(1\\) represent negative deviations (Desachy 2004). PVI matrix allows explore deviations independence (intuitive approach \\(\\chi^2\\)), way high-contrast matrix quite significant deviations, low risk due randomness (Desachy 2004). matrigraph() displays deviations independence: PVI equal \\(1\\) (statistical independence), cell matrix filled grey. PVI less \\(1\\) (negative deviation independence), size grey square proportional PVI (white margin thus represents fraction negative deviation). PVI greater \\(1\\) (positive deviation), black square representing fraction positive deviations superimposed. large positive deviations (PVI greater \\(2\\)), cell filled black. reverse TRUE, fraction negative deviations displayed white square.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Matrigraph \u2014 matrigraph","text":"Desachy, B. (2004). Le s\u00e9riographe EPPM: un outil informatis\u00e9 de s\u00e9riation graphique pour tableaux de comptages. Revue arch\u00e9ologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Matrigraph \u2014 matrigraph","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrigraph \u2014 matrigraph","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Matrigraph matrigraph(compiegne)  matrigraph(compiegne, reverse = TRUE)   ## Compute PVI counts_pvi <- pvi(compiegne) plot_heatmap(counts_pvi, col = khroma::color(\"iridescent\")(12))","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or Set Parts of an Object \u2014 mutators","title":"Get or Set Parts of an Object \u2014 mutators","text":"Getters setters extract replace parts object.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get or Set Parts of an Object \u2014 mutators","text":"","code":"get_method(x)  # S4 method for DiversityIndex labels(object)  # S4 method for RarefactionIndex labels(object)  # S4 method for DiversityIndex get_method(x)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or Set Parts of an Object \u2014 mutators","text":"object, x R object get set element(s).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get or Set Parts of an Object \u2014 mutators","text":"labels() returns suitable set labels object use printing plotting.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get or Set Parts of an Object \u2014 mutators","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Co-Occurrence \u2014 occurrence","title":"Co-Occurrence \u2014 occurrence","text":"Co-Occurrence","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Co-Occurrence \u2014 occurrence","text":"","code":"occurrence(object, ...)  # S4 method for matrix occurrence(object)  # S4 method for data.frame occurrence(object)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Co-Occurrence \u2014 occurrence","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Co-Occurrence \u2014 occurrence","text":"stats::dist object.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Co-Occurrence \u2014 occurrence","text":"co-occurrence matrix symmetric matrix zeros main diagonal, works many times pairs taxa/types occur together least one sample.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Co-Occurrence \u2014 occurrence","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Co-Occurrence \u2014 occurrence","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Plot spot diagram of a co-occurrence matrix occ <- occurrence(cantabria) plot_spot(occ)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":null,"dir":"Reference","previous_headings":"","what":"Bertin Diagram \u2014 plot_bertin","title":"Bertin Diagram \u2014 plot_bertin","text":"Plots Bertin diagram.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bertin Diagram \u2014 plot_bertin","text":"","code":"plot_bertin(object, ...)  # S4 method for matrix plot_bertin(   object,   threshold = NULL,   freq = FALSE,   margin = 1,   col = c(\"white\", \"black\"),   flip = TRUE,   axes = TRUE,   ... )  # S4 method for data.frame plot_bertin(   object,   threshold = NULL,   freq = FALSE,   margin = 1,   col = c(\"white\", \"black\"),   flip = TRUE,   axes = TRUE,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bertin Diagram \u2014 plot_bertin","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. threshold function takes numeric vector argument returns numeric threshold value (see ). NULL (default), threshold computed. used freq FALSE. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. col vector colors. flip logical scalar: x y axis flipped? Defaults TRUE. axes logical scalar: axes drawn plot?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bertin Diagram \u2014 plot_bertin","text":"plot_bertin() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"bertin-matrix","dir":"Reference","previous_headings":"","what":"Bertin Matrix","title":"Bertin Diagram \u2014 plot_bertin","text":"de Falguerolles et al. (1997) points : \"abstract terms, Bertin matrix matrix  displays. ... fix ideas, think data matrix, variable case, real valued variables. variable, draw bar chart variable value case. High-light bars representing value sample threshold variable.\"","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bertin Diagram \u2014 plot_bertin","text":"Bertin, J. (1977). La graphique et le traitement graphique de l'information. Paris: Flammarion. Nouvelle Biblioth\u00e8que Scientifique. de Falguerolles, ., Friedrich, F. & Sawitzki, G. (1997). Tribute J. Bertin's Graphical Data Analysis. W. Badilla & F. Faulbaum (eds.), SoftStat '97: Advances Statistical Software 6. Stuttgart: Lucius & Lucius, p. 11-20.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bertin Diagram \u2014 plot_bertin","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bertin Diagram \u2014 plot_bertin","text":"","code":"## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\")  ## Plot a Bertin diagram... ## ...without threshold plot_bertin(mississippi)   ## ...with the variable mean as threshold plot_bertin(mississippi, threshold = mean)   ## Plot conditional proportions plot_bertin(mississippi, freq = TRUE, margin = 1)  plot_bertin(mississippi, freq = TRUE, margin = 2)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":null,"dir":"Reference","previous_headings":"","what":"Dice-Leraas Diagram \u2014 plot_diceleraas","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"Plots Dice-Leraas diagram.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"","code":"plot_diceleraas(object, ...)  # S4 method for matrix plot_diceleraas(   object,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = FALSE,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for data.frame plot_diceleraas(   object,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = FALSE,   panel.first = NULL,   panel.last = NULL,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"plot_diceleraas() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"Dice-Leraas diagram, horizontal line represents range data (min-max) small vertical line indicates mean. black rectangle twice standard error mean, white rectangle one standard deviation either side mean.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"Dice, L. R., & Leraas, H. J. (1936). Graphic Method Comparing Several Sets Measurements. Contributions Laboratory Vertebrate Genetics, 3: 1-3. Hubbs, C. L., & C. Hubbs (1953). Improved Graphical Analysis Comparison Series Samples. Systematic Biology, 2(2): 49-56. doi:10.2307/sysbio/2.2.49 . Simpson, G. G., Roe, ., & Lewontin, R. C. Quantitative Zoology. New York: Harcourt, Brace Company, 1960.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Plot a Dice-Leraas diagram plot_diceleraas(compiegne)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Plot \u2014 plot_diversity","title":"Diversity Plot \u2014 plot_diversity","text":"Diversity Plot","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Plot \u2014 plot_diversity","text":"","code":"# S4 method for DiversityIndex,missing plot(   x,   log = \"x\",   col.mean = \"#DDAA33\",   col.interval = \"#004488\",   lty.mean = \"solid\",   lty.interval = \"dashed\",   lwd.mean = 1,   lwd.interval = 1,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Plot \u2014 plot_diversity","text":"x DiversityIndex object plotted. log character string indicating axes log scale. Defaults x. col.mean, col.interval character string specifying color lines. lty.mean, lty.interval character string numeric value specifying line types. lwd.mean, lwd.interval non-negative numeric value specifying line widths. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters passed graphics::points(), particularly, cex, col pch.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Plot \u2014 plot_diversity","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Plot \u2014 plot_diversity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Plot \u2014 plot_diversity","text":"","code":"# \\donttest{ ## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = \"shannon\") h_sim <- simulate(h) plot(h_sim)   r <- richness(cantabria, method = \"count\") r_sim <- simulate(r) plot(r_sim)  # }","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":null,"dir":"Reference","previous_headings":"","what":"Ford Diagram \u2014 plot_ford","title":"Ford Diagram \u2014 plot_ford","text":"Plots Ford (battleship curve) diagram.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ford Diagram \u2014 plot_ford","text":"","code":"plot_ford(object, ...)  # S4 method for matrix plot_ford(   object,   weights = FALSE,   EPPM = FALSE,   fill = \"darkgrey\",   border = NA,   axes = TRUE,   ... )  # S4 method for data.frame plot_ford(object, weights = FALSE, EPPM = FALSE)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ford Diagram \u2014 plot_ford","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. weights logical scalar: row sums displayed? EPPM logical scalar: EPPM drawn? See seriograph(). fill color filling bars. border color draw borders. axes logical scalar: axes drawn plot?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ford Diagram \u2014 plot_ford","text":"plot_ford() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Ford Diagram \u2014 plot_ford","text":"Ford, J. . (1962). quantitative method deriving cultural chronology. Washington, DC: Pan American Union. Technical manual 1.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ford Diagram \u2014 plot_ford","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ford Diagram \u2014 plot_ford","text":"","code":"## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\")  ## Plot a Ford diagram plot_ford(mississippi)   plot_ford(mississippi, weights = TRUE)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap \u2014 plot_heatmap","title":"Heatmap \u2014 plot_heatmap","text":"Plots heatmap.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap \u2014 plot_heatmap","text":"","code":"plot_heatmap(object, ...)  # S4 method for matrix plot_heatmap(   object,   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = TRUE,   upper = TRUE,   lower = TRUE,   freq = FALSE,   margin = 1,   axes = TRUE,   legend = TRUE,   ... )  # S4 method for data.frame plot_heatmap(   object,   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = TRUE,   upper = TRUE,   lower = TRUE,   freq = FALSE,   margin = 1,   axes = TRUE,   legend = TRUE,   ... )  # S4 method for dist plot_heatmap(   object,   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = FALSE,   upper = FALSE,   lower = !upper,   axes = TRUE,   legend = TRUE,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap \u2014 plot_heatmap","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. col vector colors. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. axes logical scalar: axes drawn plot? legend logical scalar: legend displayed?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap \u2014 plot_heatmap","text":"plot_heatmap() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heatmap \u2014 plot_heatmap","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heatmap \u2014 plot_heatmap","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Plot raw data plot_heatmap(cantabria)   ## Plot conditional proportions plot_heatmap(cantabria, freq = TRUE, margin = 1)  plot_heatmap(cantabria, freq = TRUE, margin = 2)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix Plot \u2014 plot_matrix","title":"Matrix Plot \u2014 plot_matrix","text":"Matrix Plot","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix Plot \u2014 plot_matrix","text":"","code":"plot_matrix(   object,   panel,   diag = TRUE,   upper = TRUE,   lower = TRUE,   freq = FALSE,   margin = 1,   scale = TRUE,   drop_zero = TRUE,   col = graphics::par(\"fg\"),   midpoint = NULL,   axes = TRUE,   legend = TRUE,   asp = 1,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix Plot \u2014 plot_matrix","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). panel function form function(x, y, z, color, ...) gives action carried panel display. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. scale logical scalar indicating whether data rescaled \\([-1,1]\\). used freq FALSE. drop_zero logical scalar: zeros discarded? col vector colors. midpoint numeric value specifying data midpoint. axes logical scalar: axes drawn plot? legend logical scalar: legend displayed? asp length-one numeric vector, giving aspect ratio \\(y/x\\). ... arguments passed panel.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Rank Plot \u2014 plot_rank","title":"Rank Plot \u2014 plot_rank","text":"Plots rank vs relative abundance diagram.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rank Plot \u2014 plot_rank","text":"","code":"plot_rank(object, ...)  # S4 method for matrix plot_rank(   object,   log = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   legend = list(x = \"topright\"),   ... )  # S4 method for data.frame plot_rank(   object,   log = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   legend = list(x = \"topright\"),   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rank Plot \u2014 plot_rank","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters. log character string contains \"x\" x axis logarithmic, \"y\" y axis logarithmic \"xy\" \"yx\" axes logarithmic (base 10). main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rank Plot \u2014 plot_rank","text":"plot_rank() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rank Plot \u2014 plot_rank","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rank Plot \u2014 plot_rank","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rank Plot \u2014 plot_rank","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Plot rank vs abundance plot_rank(cantabria)   ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot_rank(cantabria, col = col, pch = 15:19, lty = 2)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Plot \u2014 plot_rarefaction","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"Rarefaction Plot","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"","code":"# S4 method for RarefactionIndex,missing plot(   x,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   legend = list(x = \"topleft\"),   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"x RarefactionIndex object plotted. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed. ... graphical parameters passed graphics::lines().","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = \"baxter\") plot(rare, panel.first = graphics::grid())   ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot(rare, col = col, lty = 1:5)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":null,"dir":"Reference","previous_headings":"","what":"Spot Plot \u2014 plot_spot","title":"Spot Plot \u2014 plot_spot","text":"Plots spot matrix.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spot Plot \u2014 plot_spot","text":"","code":"plot_spot(object, ...)  # S4 method for matrix plot_spot(   object,   type = c(\"ring\", \"plain\"),   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = TRUE,   upper = TRUE,   lower = TRUE,   freq = FALSE,   margin = 1,   axes = TRUE,   legend = TRUE,   ... )  # S4 method for data.frame plot_spot(   object,   type = c(\"ring\", \"plain\"),   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = TRUE,   upper = TRUE,   lower = TRUE,   freq = FALSE,   margin = 1,   axes = TRUE,   legend = TRUE,   ... )  # S4 method for dist plot_spot(   object,   type = c(\"ring\", \"plain\"),   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = FALSE,   upper = FALSE,   lower = !upper,   axes = TRUE,   legend = TRUE,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spot Plot \u2014 plot_spot","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. type character string specifying graph plotted. must one \"ring\" (default) \"plain\". unambiguous substring can given. col vector colors. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. axes logical scalar: axes drawn plot? legend logical scalar: legend displayed?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spot Plot \u2014 plot_spot","text":"plot_spot() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spot Plot \u2014 plot_spot","text":"spot matrix can considered variant Bertin diagram data first transformed relative frequencies.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Spot Plot \u2014 plot_spot","text":"Adapted Dan Gopstein's original idea.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spot Plot \u2014 plot_spot","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spot Plot \u2014 plot_spot","text":"","code":"## Data from Huntley 2004, 2008 data(\"pueblo\")  ## Plot spot diagram of count data plot_spot(pueblo, type = \"ring\")  plot_spot(pueblo, type = \"plain\")   ## Plot conditional proportions plot_spot(pueblo, freq = TRUE, margin = 1)  plot_spot(pueblo, freq = TRUE, margin = 2)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":null,"dir":"Reference","previous_headings":"","what":"Pueblo IV Period Ceramics \u2014 pueblo","title":"Pueblo IV Period Ceramics \u2014 pueblo","text":"dataset ceramic counts Zuni region.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pueblo IV Period Ceramics \u2014 pueblo","text":"","code":"pueblo","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pueblo IV Period Ceramics \u2014 pueblo","text":"data.frame 9 rows 5 variables (compositional groups).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Pueblo IV Period Ceramics \u2014 pueblo","text":"Huntley, D. L. (2004). Interaction, Boundaries, Identities: Multiscalar Approach Organizational Scale Pueblo IV Zuni Society. Ph.D. Dissertation, Arizona State University. Huntley, D. L. (2022). Ancestral Zuni Glaze-Decorated Pottery: Viewing Pueblo IV Regional Organization Ceramic Production Exchange. Anthropological Papers University Arizona 72. Tucson: University Arizona Press. doi:10.2307/j.ctv2ngx5n8 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction \u2014 rarefaction","title":"Rarefaction \u2014 rarefaction","text":"Rarefaction","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction \u2014 rarefaction","text":"","code":"rarefaction(object, ...)  index_baxter(x, ...)  index_hurlbert(x, ...)  # S4 method for matrix rarefaction(object, sample = NULL, method = c(\"hurlbert\", \"baxter\"), step = 1)  # S4 method for data.frame rarefaction(object, sample = NULL, method = c(\"hurlbert\", \"baxter\"), step = 1)  # S4 method for numeric index_baxter(x, sample, ...)  # S4 method for numeric index_hurlbert(x, sample, ...)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction \u2014 rarefaction","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. x numeric vector count data (absolute frequencies). sample length-one numeric vector giving sub-sample size. size sample smaller total community size. method character string vector strings specifying index computed (see details). unambiguous substring can given. step integer giving increment sample size.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction \u2014 rarefaction","text":"rarefaction() returns RarefactionIndex object. index_*() return numeric vector.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"rarefaction-measures","dir":"Reference","previous_headings":"","what":"Rarefaction Measures","title":"Rarefaction \u2014 rarefaction","text":"following rarefaction measures available count data: baxter Baxter's rarefaction. hurlbert Hurlbert's unbiased estimate Sander's rarefaction.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rarefaction \u2014 rarefaction","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa (e.g. open system; Peet 1974). alternative, richness (\\(S\\)) can used concept taxa number (McIntosh 1967). always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (\\(E(S)\\)) number taxa expected samples standard size (.e. taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rarefaction \u2014 rarefaction","text":"Baxter, M. J. (2001). Methodological Issues Study Assemblage Diversity. American Antiquity, 66(4), 715-725. doi:10.2307/2694184 . Hurlbert, S. H. (1971). Nonconcept Species Diversity: Critique Alternative Parameters. Ecology, 52(4), 577-586. doi:10.2307/1934145 . Sander, H. L. (1968). Marine Benthic Diversity: Comparative Study. American Naturalist, 102(925), 243-282.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction \u2014 rarefaction","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction \u2014 rarefaction","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = \"baxter\") plot(rare, panel.first = graphics::grid())   ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot(rare, col = col, lty = 1:5)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages \u2014 reexports","title":"Objects exported from other packages \u2014 reexports","text":"objects imported packages. Follow links see documentation. arkhe bootstrap, jackknife, remove_NA, remove_zero, replace_NA, replace_zero","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample \u2014 resample","title":"Resample \u2014 resample","text":"Simulates observations multinomial distribution.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample \u2014 resample","text":"","code":"resample(object, ...)  # S4 method for numeric resample(object, do, n, size = sum(object), ..., f = NULL)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample \u2014 resample","text":"object numeric vector count data (absolute frequencies). ... Extra arguments passed . function takes object argument returns single numeric value. n non-negative integer specifying number bootstrap replications. size non-negative integer specifying sample size. f function takes single numeric vector (result ) argument.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample \u2014 resample","text":"f NULL, resample() returns n values . Else, returns result f applied n values .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample \u2014 resample","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resample \u2014 resample","text":"","code":"## Sample observations from a multinomial distribution x <- sample(1:100, 50, TRUE) resample(x, do = median, n = 100) #>   [1] 55.5 55.0 52.5 53.0 52.0 56.5 56.5 53.0 53.5 56.0 52.5 51.0 54.5 53.5 53.0 #>  [16] 53.0 54.0 57.5 56.0 56.5 56.0 54.5 55.5 57.0 56.5 54.0 55.0 57.0 55.5 56.0 #>  [31] 55.5 55.5 56.0 57.0 55.5 51.5 54.0 55.0 56.0 55.5 53.0 54.0 54.0 51.0 52.0 #>  [46] 51.5 53.0 53.5 54.0 56.0 52.0 51.5 49.5 54.0 55.5 56.5 53.0 55.5 54.0 54.5 #>  [61] 55.0 54.5 57.5 54.5 54.5 50.0 57.0 54.5 53.0 57.5 57.0 54.5 55.0 53.0 54.0 #>  [76] 53.0 49.0 51.5 53.5 57.0 53.0 54.0 53.0 50.5 54.0 57.0 50.5 58.0 54.5 56.0 #>  [91] 51.5 53.0 55.5 54.0 53.0 54.5 54.0 56.0 55.0 54.0  ## Estimate the 25th, 50th and 95th percentiles quant <- function(x) { quantile(x, probs = c(0.25, 0.50, 0.75)) } resample(x, n = 100, do = median, f = quant) #>    25%    50%    75%  #> 52.875 54.000 55.500","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness \u2014 richness","title":"Richness \u2014 richness","text":"richness() returns sample richness. composition() returns asymptotic species richness.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Richness \u2014 richness","text":"","code":"richness(object, ...)  composition(object, ...)  index_ace(x, ...)  index_ice(x, ...)  index_chao1(x, ...)  index_chao2(x, ...)  index_margalef(x, ...)  index_menhinick(x, ...)  # S4 method for matrix richness(object, method = c(\"count\", \"margalef\", \"menhinick\"))  # S4 method for data.frame richness(object, method = c(\"count\", \"margalef\", \"menhinick\"))  # S4 method for matrix composition(   object,   method = c(\"chao1\", \"ace\", \"chao2\", \"ice\"),   unbiased = FALSE,   improved = FALSE,   k = 10 )  # S4 method for data.frame composition(   object,   method = c(\"chao1\", \"ace\", \"chao2\", \"ice\"),   unbiased = FALSE,   improved = FALSE,   k = 10 )  # S4 method for numeric index_margalef(x, na.rm = FALSE, ...)  # S4 method for numeric index_menhinick(x, na.rm = FALSE, ...)  # S4 method for numeric index_ace(x, k = 10, ...)  # S4 method for numeric index_chao1(x, unbiased = FALSE, improved = FALSE, ...)  # S4 method for matrix index_ice(x, k = 10, ...)  # S4 method for matrix index_chao2(x, unbiased = FALSE, improved = FALSE, ...)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Richness \u2014 richness","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. x numeric vector matrix count data (absolute frequencies). method character string vector strings specifying index computed (see details). unambiguous substring can given. unbiased logical scalar. bias-corrected estimator used? used \"chao1\" \"chao2\" (improved) estimator. improved logical scalar. improved estimator used? used \"chao1\" \"chao2\". k length-one numeric vector giving threshold rare/infrequent abundant/frequent species. used method \"ace\" \"ice\". na.rm numeric scalar: missing values (including NaN) removed?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Richness \u2014 richness","text":"richness() returns RichnessIndex object. composition() returns CompositionIndex object. index_*() return numeric vector.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Richness \u2014 richness","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa (e.g. open system; Peet 1974). alternative, richness (\\(S\\)) can used concept taxa number (McIntosh 1967). always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (\\(E(S)\\)) number taxa expected samples standard size (.e. taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"richness-measures","dir":"Reference","previous_headings":"","what":"Richness Measures","title":"Richness \u2014 richness","text":"following richness measures available count data: count Returns number observed taxa/types. margalef Margalef richness index. menhinick Menhinick richness index.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"asymptotic-species-richness","dir":"Reference","previous_headings":"","what":"Asymptotic Species Richness","title":"Richness \u2014 richness","text":"following measures available count data: ace Abundance-based Coverage Estimator. chao1 (improved/unbiased) Chao1 estimator. following measures available replicated incidence data: ice Incidence-based Coverage Estimator. chao2 (improved/unbiased) Chao2 estimator.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Richness \u2014 richness","text":"Chao, . (1984). Nonparametric Estimation Number Classes Population. Scandinavian Journal Statistics, 11(4), 265-270. Chao, . (1987). Estimating Population Size Capture-Recapture Data Unequal Catchability. Biometrics 43(4), 783-791. doi:10.2307/2531532 . Chao, . & Chiu, C.-H. (2016). Species Richness: Estimation Comparison. Balakrishnan, N., Colton, T., Everitt, B., Piegorsch, B., Ruggeri, F. & Teugels, J. L. (Eds.), Wiley StatsRef: Statistics Reference Online. Chichester, UK: John Wiley & Sons, Ltd., 1-26. doi:10.1002/9781118445112.stat03432.pub2 Chao, . & Lee, S.-M. (1992). Estimating Number Classes via Sample Coverage. Journal American Statistical Association, 87(417), 210-217. doi:10.1080/01621459.1992.10475194 . Chiu, C.-H., Wang, Y.-T., Walther, B. . & Chao, . (2014). improved nonparametric lower bound species richness via modified good-turing frequency formula. Biometrics, 70(3), 671-682. doi:10.1111/biom.12200 . Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36. Magurran, E. & Brian J. McGill (2011). Biological Diversity: Frontiers Measurement Assessment. Oxford: Oxford University Press. Margalef, R. (1958). Information Theory Ecology. General Systems, 3, 36-71. Menhinick, E. F. (1964). Comparison Species-Individuals Diversity Indices Applied Samples Field Insects. Ecology, 45(4), 859-861. doi:10.2307/1934933 . McIntosh, R. P. (1967). Index Diversity Relation Certain Concepts Diversity. Ecology, 48(3), 392-404. doi:10.2307/1932674 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness \u2014 richness","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Richness \u2014 richness","text":"","code":"## Data from Magurran 1988, p. 128-129 trap <- matrix(data = c(9, 3, 0, 4, 2, 1, 1, 0, 1, 0, 1, 1,                         1, 0, 1, 0, 0, 0, 1, 2, 0, 5, 3, 0),                nrow = 2, byrow = TRUE, dimnames = list(c(\"A\", \"B\"), NULL))  ## Margalef and Menhinick index richness(trap, method = \"margalef\") # 2.55 1.88 #> [1] 2.551432 1.949356 richness(trap, method = \"menhinick\") # 1.95 1.66 #> [1] 1.876630 1.664101  ## Data from Chao & Chiu 2016 brazil <- matrix(   data = rep(x = c(1:21, 23, 25, 27, 28, 30, 32, 34:37, 41,                    45, 46, 49, 52, 89, 110, 123, 140),              times = c(113, 50, 39, 29, 15, 11, 13, 5, 6, 6, 3, 4,                        3, 5, 2, 5, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1,                        0, 0, 2, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0)),   nrow = 1, byrow = TRUE )  ## Chao1-type estimators (asymptotic species richness) composition(brazil, method = c(\"chao1\"), unbiased = FALSE) # 461.625 #> [1] 461.6254 composition(brazil, method = c(\"ace\"), k = 10) # 445.822 #> [1] 445.8224","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":null,"dir":"Reference","previous_headings":"","what":"Seriograph \u2014 seriograph","title":"Seriograph \u2014 seriograph","text":"seriograph() produces Ford diagram highlighting relationships rows columns. eppm() computes cell numeric matrix positive difference column mean percentage.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Seriograph \u2014 seriograph","text":"","code":"seriograph(object, ...)  eppm(object, ...)  # S4 method for matrix eppm(object)  # S4 method for data.frame eppm(object)  # S4 method for matrix seriograph(   object,   weights = FALSE,   fill = \"darkgrey\",   border = NA,   axes = TRUE,   ... )  # S4 method for data.frame seriograph(   object,   weights = FALSE,   fill = \"darkgrey\",   border = NA,   axes = TRUE,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Seriograph \u2014 seriograph","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. weights logical scalar: row sums displayed? fill color filling bars. border color draw borders. axes logical scalar: axes drawn plot?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Seriograph \u2014 seriograph","text":"seriograph() called side-effects: results graphic displayed (invisibly returns object). eppm() returns numeric matrix.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Seriograph \u2014 seriograph","text":"positive difference column mean percentage (french \"\u00e9cart positif au pourcentage moyen\", EPPM) represents deviation situation statistical independence. independence can interpreted absence relationships types chronological order assemblages, EPPM useful tool explore significance relationship rows columns related seriation (Desachy 2004). seriograph() superimposes frequencies (grey) EPPM values (black) row-column pair Ford diagram.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Seriograph \u2014 seriograph","text":"Desachy, B. (2004). Le s\u00e9riographe EPPM: un outil informatis\u00e9 de s\u00e9riation graphique pour tableaux de comptages. Revue arch\u00e9ologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Seriograph \u2014 seriograph","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seriograph \u2014 seriograph","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Seriograph seriograph(compiegne)  seriograph(compiegne, weights = TRUE)   ## Compute EPPM counts_eppm <- eppm(compiegne) plot_heatmap(counts_eppm, col = khroma::color(\"YlOrBr\")(12))","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Similarity \u2014 similarity","title":"Similarity \u2014 similarity","text":"Similarity","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Similarity \u2014 similarity","text":"","code":"similarity(object, ...)  index_jaccard(x, y, ...)  index_sorenson(x, y, ...)  index_bray(x, y, ...)  index_morisita(x, y, ...)  index_brainerd(x, y, ...)  index_binomial(x, y, ...)  # S4 method for matrix similarity(   object,   method = c(\"brainerd\", \"bray\", \"jaccard\", \"morisita\", \"sorenson\", \"binomial\") )  # S4 method for data.frame similarity(   object,   method = c(\"brainerd\", \"bray\", \"jaccard\", \"morisita\", \"sorenson\", \"binomial\") )  # S4 method for character,character index_jaccard(x, y)  # S4 method for logical,logical index_jaccard(x, y)  # S4 method for numeric,numeric index_jaccard(x, y)  # S4 method for logical,logical index_sorenson(x, y)  # S4 method for numeric,numeric index_sorenson(x, y)  # S4 method for numeric,numeric index_bray(x, y)  # S4 method for numeric,numeric index_morisita(x, y)  # S4 method for numeric,numeric index_brainerd(x, y)  # S4 method for numeric,numeric index_binomial(x, y)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Similarity \u2014 similarity","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. x, y length-\\(p\\) numeric vector count data. method character string specifying method used (see details). unambiguous substring can given.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Similarity \u2014 similarity","text":"similarity() returns stats::dist object. index_*() return numeric vector.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Similarity \u2014 similarity","text":"\\(\\beta\\)-diversity can measured addressing similarity pairs samples/cases (Brainerd-Robinson, Jaccard, Morisita-Horn Sorenson indices). Similarity pairs taxa/types can measured assessing degree co-occurrence (binomial co-occurrence). Jaccard, Morisita-Horn Sorenson indices provide scale similarity \\(0\\)-\\(1\\) \\(1\\) perfect similarity \\(0\\) similarity. Brainerd-Robinson index scaled \\(0\\) \\(200\\). Binomial co-occurrence assessment approximates Z-score. binomial Binomial co-occurrence assessment. assesses degree co-occurrence taxa/types within dataset. strongest associations shown large positive numbers, strongest segregations large negative numbers. brainerd Brainerd-Robinson quantitative index. city-block metric similarity pairs samples/cases. bray Sorenson quantitative index (Bray Curtis modified version Sorenson index). jaccard Jaccard qualitative index. morisita Morisita-Horn quantitative index. sorenson Sorenson qualitative index.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Similarity \u2014 similarity","text":"Brainerd, G. W. (1951). Place Chronological Ordering Archaeological Analysis. American Antiquity, 16(04), 301-313. doi:10.2307/276979 . Bray, J. R. & Curtis, J. T. (1957). Ordination Upland Forest Communities Southern Wisconsin. Ecological Monographs, 27(4), 325-349. doi:10.2307/1942268 . Kintigh, K. (2006). Ceramic Dating Type Associations. J. Hantman R. (eds.), Managing Archaeological Data: Essays Honor Sylvia W. Gaines. Anthropological Research Paper, 57. Tempe, AZ: Arizona State University, p. 17-26. Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . Robinson, W. S. (1951). Method Chronologically Ordering Archaeological Deposits. American Antiquity, 16(04), 293-301. doi:10.2307/276978 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Similarity \u2014 similarity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Similarity \u2014 similarity","text":"","code":"## Data from Huntley 2004, 2008 data(\"pueblo\")  ## Brainerd-Robinson measure (C <- similarity(pueblo, \"brainerd\")) #>             Atsinna   Cienega   Mirabal PdMuertos      Hesh   LowPesc      BoxS #> Cienega   164.36782                                                             #> Mirabal   152.38095 138.09524                                                   #> PdMuertos 179.31034 150.57471 152.38095                                         #> Hesh       82.75862  55.55556  66.66667 103.44828                               #> LowPesc    89.21023  62.00717  73.11828 109.89989 193.54839                     #> BoxS       82.75862  55.55556 114.28571 102.17114 103.70370 103.70370           #> Ojo Bon    27.58621  22.22222  19.04762  20.68966  26.66667  25.80645  22.22222 #> S170       27.58621  22.22222  19.04762  20.68966  26.66667  25.80645  22.22222 #>             Ojo Bon #> Cienega             #> Mirabal             #> PdMuertos           #> Hesh                #> LowPesc             #> BoxS                #> Ojo Bon             #> S170      190.53030 plot_spot(C)   ## Data from Magurran 1988, p. 166 data(\"aves\")  ## Jaccard measure (presence/absence data) similarity(aves, \"jaccard\") # 0.46 #>         unmanaged #> managed 0.4615385  ## Sorenson measure (presence/absence data) similarity(aves, \"sorenson\") # 0.63 #>         unmanaged #> managed 0.6315789  # Jaccard measure (Bray's formula ; count data) similarity(aves, \"bray\") # 0.44 #>         unmanaged #> managed 0.4442754  # Morisita-Horn measure (count data) similarity(aves, \"morisita\") # 0.81 #>         unmanaged #> managed 0.8134497","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"Measure Diversity Comparing Simulated Assemblages","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"","code":"# S4 method for DiversityIndex simulate(   object,   n = 1000,   step = 1,   interval = c(\"percentiles\", \"student\", \"normal\"),   level = 0.8,   progress = getOption(\"tabula.progress\") )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"object DiversityIndex object. n non-negative integer giving number bootstrap replications. step integer giving increment sample size. interval character string giving type confidence interval returned. must one \"percentiles\" (sample quantiles, described Kintigh 1984; default), \"student\" \"normal\". unambiguous substring can given. level length-one numeric vector giving confidence level. progress logical scalar: progress bar displayed?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"Returns DiversityIndex object.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"Baxter, M. J. (2001). Methodological Issues Study Assemblage Diversity. American Antiquity, 66(4), 715-725. doi:10.2307/2694184 . Kintigh, K. W. (1984). Measuring Archaeological Diversity Comparison Simulated Assemblages. American Antiquity, 49(1), 44-54. doi:10.2307/280511 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"","code":"# \\donttest{ ## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = \"shannon\") h_sim <- simulate(h) plot(h_sim)   r <- richness(cantabria, method = \"count\") r_sim <- simulate(r) plot(r_sim)  # }","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/tabula-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in tabula \u2014 tabula-deprecated","title":"Deprecated Functions in tabula \u2014 tabula-deprecated","text":"functions still work removed (defunct) next version.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/tabula-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tabula: Analysis and Visualization of Archaeological Count Data \u2014 tabula-package","title":"tabula: Analysis and Visualization of Archaeological Count Data \u2014 tabula-package","text":"easy way examine archaeological count data. package provides several tests measures diversity: heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). allows easily visualize count data statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) Bertin (1977) diagrams, etc.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/tabula-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tabula: Analysis and Visualization of Archaeological Count Data \u2014 tabula-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne 33607 Pessac cedex France","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Test \u2014 test_diversity","title":"Diversity Test \u2014 test_diversity","text":"Compares Shannon diversity samples.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Test \u2014 test_diversity","text":"","code":"test_diversity(object, ...)  # S4 method for matrix test_diversity(object, adjust = \"holm\", ...)  # S4 method for data.frame test_diversity(object, adjust = \"holm\", ...)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Test \u2014 test_diversity","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. adjust character string specifying method adjusting \\(p\\) values (see stats::p.adjust()).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Test \u2014 test_diversity","text":"numeric  matrix.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diversity Test \u2014 test_diversity","text":"test produces two sided pairwise comparisons: returns matrix adjusted \\(p\\) values.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diversity Test \u2014 test_diversity","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Test \u2014 test_diversity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Test \u2014 test_diversity","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Shannon diversity test test_diversity(cantabria) #>                      Altamira Cueto de la Mina El Juyo El Cierro #> Cueto de la Mina 8.268025e-02               NA      NA        NA #> El Juyo          3.754291e-05       0.06016855      NA        NA #> El Cierro        3.526160e-05       0.05708787       1        NA #> La Paloma        3.255839e-05       0.01313679       1         1","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":null,"dir":"Reference","previous_headings":"","what":"Turnover \u2014 turnover","title":"Turnover \u2014 turnover","text":"Returns degree turnover taxa composition along gradient transect.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turnover \u2014 turnover","text":"","code":"turnover(object, ...)  index_whittaker(x, ...)  index_cody(x, ...)  index_routledge1(x, ...)  index_routledge2(x, ...)  index_routledge3(x, ...)  index_wilson(x, ...)  # S4 method for matrix turnover(   object,   method = c(\"whittaker\", \"cody\", \"routledge1\", \"routledge2\", \"routledge3\", \"wilson\"),   ... )  # S4 method for data.frame turnover(   object,   method = c(\"whittaker\", \"cody\", \"routledge1\", \"routledge2\", \"routledge3\", \"wilson\"),   ... )  # S4 method for matrix index_whittaker(x)  # S4 method for matrix index_cody(x)  # S4 method for matrix index_routledge1(x)  # S4 method for matrix index_routledge2(x)  # S4 method for matrix index_routledge3(x)  # S4 method for matrix index_wilson(x)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turnover \u2014 turnover","text":"object, x \\(m \\times p\\) numeric matrix data.frame count data incidence data. data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. method character string specifying method used (see details). unambiguous substring can given.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turnover \u2014 turnover","text":"numeric vector.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Turnover \u2014 turnover","text":"following methods can used ascertain degree turnover taxa composition along gradient (\\(\\beta\\)-diversity) qualitative (presence/absence) data. assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect. whittaker Whittaker measure. cody Cody measure. routledge1 Routledge first measure. routledge2 Routledge second measure. routledge3 Routledge third measure. exponential form second measure. wilson Wilson measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Turnover \u2014 turnover","text":"Cody, M. L. (1975). Towards theory continental species diversity: Bird distributions Mediterranean habitat gradients. M. L. Cody & J. M. Diamond (Eds.), Ecology Evolution Communities. Cambridge, MA: Harvard University Press, p. 214-257. Routledge, R. D. (1977). Whittaker's Components Diversity. Ecology, 58(5), 1120-1127. doi:10.2307/1936932 . Whittaker, R. H. (1960). Vegetation Siskiyou Mountains, Oregon California. Ecological Monographs, 30(3), 279-338. doi:10.2307/1943563 . Wilson, M. V., & Shmida, . (1984). Measuring Beta Diversity Presence-Absence Data. Journal Ecology, 72(3), 1055-1064. doi:10.2307/2259551 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Turnover \u2014 turnover","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turnover \u2014 turnover","text":"","code":"## Data from Magurran 1988, p. 162 data(\"woodland\")  ## Whittaker's measure turnover(woodland, \"whittaker\") # 1 #> [1] 1  ## Cody's measure turnover(woodland, \"cody\") # 3 #> [1] 3  ## Routledge's measures turnover(woodland, \"routledge1\") # 0.29 #> [1] 0.2857143 turnover(woodland, \"routledge2\") # 0.56 #> [1] 0.5594978 turnover(woodland, \"routledge3\") # 1.75 #> [1] 1.749794  ## Wilson and Shmida's measure turnover(woodland, \"wilson\") # 1 #> [1] 1","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":null,"dir":"Reference","previous_headings":"","what":"Trees Incidences \u2014 woodland","title":"Trees Incidences \u2014 woodland","text":"dataset presence absence trees six (10 x 10 m) quadarts along transect deciduous woodland.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trees Incidences \u2014 woodland","text":"","code":"woodland","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Trees Incidences \u2014 woodland","text":"data.frame 6 rows (quadarts) 6 variables (tree species).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Trees Incidences \u2014 woodland","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/aion/articles/aion.html","id":"calendars","dir":"Articles","previous_headings":"","what":"Calendars","title":"Introduction to aion","text":"aion currently supports Julian Gregorian calendars (common eras latter, e.g.\u00a0Present, Common Era\u2026). calendar can defined using calendar() function: using shortcuts: creating date vectors time series, must specify calendar corresponding data (see ). allows select correct method converting dates rata die. Outputs generated aion expressed rata die default (can modified per-function basis). two exceptions plot() format() functions, default calendar specified package options (see ). can change default calendar used throughout package modifying aion.calendar option, per-function basis.","code":"## Create a calendar object ## (Gregorian Common Era) calendar(\"CE\") #> Common Era (CE): Gregorian years counted forwards from 0. ## Common Era (Gregorian) CE() #> Common Era (CE): Gregorian years counted forwards from 0.  ## Before Present (Gregorian) BP() #> Before Present (BP): Gregorian years counted backwards from 1950. ## Get default calendar getOption(\"aion.calendar\") #> Common Era (CE): Gregorian years counted forwards from 0.  ## Change default calendar to BP options(aion.calendar = BP()) getOption(\"aion.calendar\") #> Before Present (BP): Gregorian years counted backwards from 1950.  ## Set it back to Gregorian Common Era options(aion.calendar = CE()) getOption(\"aion.calendar\") #> Common Era (CE): Gregorian years counted forwards from 0.","package":"aion"},{"path":"https://packages.tesselle.org/aion/articles/aion.html","id":"vectors-of-dates","dir":"Articles","previous_headings":"","what":"Vectors of dates","title":"Introduction to aion","text":"base R, dates represented default number days since 1970-01-01 (Gregorian), negative values earlier dates (see help(Date)). aion uses different approach: allows create date vectors represented rata die (Reingold Dershowitz 2018), .e.\u00a0number days since 01-01-01 (Gregorian)1. makes possible get rid specific calendar make calculations easier. possible convert vector rata die dates (decimal) years calendar. fixed() function allows create vector rata die dates belonging specific calendar: rata die vector can converted dates (years) particular calendar: Rata die can represented (nicely formated) character vectors: rata die vector provides internal time representation aion time-series (want work numeric vectors represent year-based time scales, may interested era package).","code":"## Convert 2000-02-29 (Gregorian) to rata die fixed(2000, 02, 29, calendar = calendar(\"CE\")) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179  ## If days and months are missing, decimal years are expected fixed(2000.161, calendar = calendar(\"CE\")) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 ## Create a vector of 10 years BP (Gregorian) ## (every 20 years starting from 2000 BP) (years <- seq(from = 20000, by = -20, length.out = 10)) #>  [1] 20000 19980 19960 19940 19920 19900 19880 19860 19840 19820  ## Convert years to rata die (rd <- fixed(years, calendar = calendar(\"BP\"))) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -6592992 -6585687 -6578382 -6571077 -6563772 -6556467 -6549162 -6541857 #>  [9] -6534553 -6527248  ## Convert back to Gregorian years as_year(rd, calendar = calendar(\"CE\"))  # Common Era #>  [1] -18050 -18030 -18010 -17990 -17970 -17950 -17930 -17910 -17890 -17870 as_year(rd, calendar = calendar(\"BP\"))  # Before Present #>  [1] 20000 19980 19960 19940 19920 19900 19880 19860 19840 19820 as_year(rd, calendar = calendar(\"b2k\")) # Before 2000 #>  [1] 20050 20030 20010 19990 19970 19950 19930 19910 19890 19870 format(rd) # Default calendar (Gregorian Common Era) #>  [1] \"-18050 CE\" \"-18030 CE\" \"-18010 CE\" \"-17990 CE\" \"-17970 CE\" \"-17950 CE\" #>  [7] \"-17930 CE\" \"-17910 CE\" \"-17890 CE\" \"-17870 CE\" format(rd, format = \"ka\", calendar = calendar(\"BP\")) #>  [1] \"20 ka BP\"    \"19.98 ka BP\" \"19.96 ka BP\" \"19.94 ka BP\" \"19.92 ka BP\" #>  [6] \"19.9 ka BP\"  \"19.88 ka BP\" \"19.86 ka BP\" \"19.84 ka BP\" \"19.82 ka BP\"","package":"aion"},{"path":"https://packages.tesselle.org/aion/articles/aion.html","id":"time-series","dir":"Articles","previous_headings":"","what":"Time series","title":"Introduction to aion","text":"time series sequence observation time value pairs strictly increasing observation times. time series object \\(n \\times m \\times p\\) array, \\(n\\) number observations, \\(m\\) number series \\(p\\) columns third dimension containing extra variables series. can created numeric vector, matrix array. Time series terminal sampling times can retrieved expressed according different calendars (remember outputs expressed rata die default): Plot one time series:   Note aion uses astronomical notation Gregorian years (year 0).","code":"## Set seed for reproductibility set.seed(12345)  ## 6 x 50 observations... obs <- matrix(rnorm(300), nrow = 50, ncol = 6)  ## ...sampled every two years starting from 2000 BP spl <- seq(from = 2000, by = -2, length.out = 50)  ## Create time series (X <- series(object = obs, time = spl, calendar = BP())) #> 50 x 6 x 1 time series observed between -18627 and 17167 r.d. ## Time series duration span(X) # Default: rata die #> [1] 35794 span(X, calendar = CE()) #> [1] 98.99726  ## Time of first observation start(X) # Default: rata die #> [1] -18627 start(X, calendar = CE()) #> [1] -50  ## Time of last observation end(X) # Default: rata die #> [1] 17167 end(X, calendar = CE()) #> [1] 48  ## Sampling times time(X, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 #> [16] 1970 1968 1966 1964 1962 1960 1958 1956 1954 1952 1950 1948 1946 1944 1942 #> [31] 1940 1938 1936 1934 1932 1930 1928 1926 1924 1922 1920 1918 1916 1914 1912 #> [46] 1910 1908 1906 1904 1902 ## Multiple plot plot(X) # Default calendar ## Extract the first series Y <- X[, 1, ]  ## Plot a single series plot(   Y,   calendar = b2k(), # b2k time scale   panel.first = graphics::grid() # Add a grid ) year_axis(side = 3, calendar = CE()) # Add a secondary time axis mtext(format(CE()), side = 3, line = 3) # Add secondary axis title","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/articles/extending.html","id":"calendars-and-dates","dir":"Articles","previous_headings":"","what":"Calendars and Dates","title":"Extending aion","text":"following example used build simple solar calendar 365 days year leap-year rule. ancient Egyptian calendar. find full details calculations detailed explanations Reingold Dershowitz (2018) (p.\u00a029). can define additional calendars creating S4 classes inherit TimeScale class exported aion: calendar defined, need build methods converting rata die calendar: Now can use calendar: definition new calendars, combined Julian Gregorian calendars already included aion, allows build conversion tools:","code":"## Egyptian calendar E <- setClass(   Class = \"EgyptianCalendar\",   prototype = list(     name = \"Egyptian\",     fixed = -272787,     direction = 1L,     year = 365   ),   contains = \"TimeScale\" ) ## Convert Egyptian dates to rata die ## NB: this method MUST return a RataDie object setMethod(   f = \"fixed\",   signature = c(     year = \"numeric\",     month = \"numeric\",     day = \"numeric\",      calendar = \"EgyptianCalendar\"   ),   definition = function(year, month, day, calendar) {     rd <- calendar_fixed(calendar) +        365 * (year - 1) +        30 * (month - 1) +        day - 1          as_fixed(rd)   } )  ## Convert rata die to Egyptian dates ## NB: this method MUST return a data.frame setMethod(   f = \"as_date\",   signature = c(object = \"numeric\", calendar = \"EgyptianCalendar\"),   definition = function(object, calendar) {     day <- object - calendar_fixed(calendar)     year <- day %/% 365 + 1     month <- (day %% 365) %/% 30 + 1     day <- day - 365 * (year - 1) - 30 * (month - 1) + 1          data.frame(year = year, month = month, day = day)   } )  ## Convert rata die to Egyptian years setMethod(   f = \"as_year\",   signature = c(object = \"numeric\", calendar = \"EgyptianCalendar\"),   definition = function(object, calendar, ...) {     (object - calendar_fixed(calendar)) %/% 365 + 1   } ) ## Create a calendar object cal <- E()  ## Convert 161/7/15 in rata die fixed(   year = 161,   month = 7,   day = 15,    calendar = cal ) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] -214193  ## Convert -214193 r.d. to an Egyptian date as_date(-214193, calendar = cal) #>   year month day #> 1  161     7  15 ## Build a conversion function from Gregorian CE years to Egyptian years Gregorian_to_Egyptian <- convert(CE(), E())  ## Convert 2023 (Gregorian) to the Egyptian calendar Gregorian_to_Egyptian(2023) #> [1] 2771","package":"aion"},{"path":"https://packages.tesselle.org/aion/articles/extending.html","id":"time-series","dir":"Articles","previous_headings":"","what":"Time Series","title":"Extending aion","text":"time series object simply \\(n \\times m \\times p\\) array, \\(n\\) number observations, \\(m\\) number series \\(p\\) columns third dimension containing extra variables series. array comes extra time slot store observations times expressed rata die. can create classes inherits TimeSeries class. example, can create class represent results calibration radiocarbon dates (code comes ananke package): methods defined aion can used objects belonging new class (e.g.\u00a0plot()).","code":".CalibratedAges <- setClass(   Class = \"CalibratedAges\",   slots = c(     ages = \"numeric\",     # Stores the radiocarbon ages to be calibrated     errors = \"numeric\",   # Store the standard deviation of the radiocarbon ages     curves = \"character\"  # Store the name of the calibration curve   ),   contains = \"TimeSeries\" )","package":"aion"},{"path":[],"package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Archaeological Time Series","text":"Base R ships lot functionality useful time series, particular stats package. However, features adapted archaeological time series. indeed defined given calendar era, can involve dates far past sampling observation time (cases) constant. aion provides system classes methods represent work time-series. Dates represented rata die (Reingold Dershowitz 2018), .e.\u00a0number days since 01-01-01 (Gregorian), negative values earlier dates. allows represent dates independently calendar: makes calculations comparisons easier. time series created aion, calendar can used printing plotting data (defaults Gregorian Common Era; see vignette(\"aion\")). aion provide tools temporal modeling. Instead, offers simple API can used specialized packages.","code":"To cite aion in publications use:    Frerebeau N, Roe J (2023). _aion: Archaeological Time Series_.   Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.8032278 <https://doi.org/10.5281/zenodo.8032278>,   R package version 1.0.1, <https://packages.tesselle.org/aion/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau and Joe Roe},     title = {{aion: Archaeological Time Series}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.0.1},     url = {https://packages.tesselle.org/aion/},     doi = {10.5281/zenodo.8032278},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"aion"},{"path":"https://packages.tesselle.org/aion/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Archaeological Time Series","text":"can install released version aion CRAN : development version GitHub :","code":"install.packages(\"aion\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/aion\")","package":"aion"},{"path":"https://packages.tesselle.org/aion/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Archaeological Time Series","text":"","code":"## Load package library(aion) ## Set seed for reproductibility set.seed(12345)  ## Create 6 time-series of 50 observations ## Sampled every two years starting from 2000 BP X <- series(   object = matrix(rnorm(300), nrow = 50, ncol = 6),   time = seq(from = 2000, by = -2, length.out = 50),   calendar = calendar(\"BP\") )  ## Plot plot(X) # Default calendar","package":"aion"},{"path":"https://packages.tesselle.org/aion/index.html","id":"related-works","dir":"","previous_headings":"","what":"Related Works","title":"Archaeological Time Series","text":"era: provides consistent representation year-based time scales numeric vector associated era.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Archaeological Time Series","text":"Please note aion project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/paper.html","id":null,"dir":"","previous_headings":"","what":"Summary","title":"Summary","text":"Numerous R packages developped describe, analyze, model temporal data context archaeological studies broadest sense. packages encompass various functionalities, including handling radiocarbon data (e.g., \u2018Bchron\u2019 @R-Bchron \u2018rcarbon\u2019 @R-rcarbon), Optically Stimulated Luminescence dating (\u2018Luminescence\u2019 @R-Luminescence), Bayesian chronological modeling (\u2018ArchaeoPhases\u2019 @R-ArchaeoPhases), using paleoenvironmental proxies (e.g., \u2018shoredate\u2019 @R-shoredate), temporal data (e.g., \u2018kairos\u2019 @R-kairos). multitude packages underscores significance computational approaches archaeology [@schmidt2020]. However, also presents major challenge package employs representation temporal information. Consequently, exchanging data different packages within data workflow becomes even arduous. \u2018aion\u2019 designed provide consistent framework representing archaeological time series.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/paper.html","id":"statement-of-need","dir":"","previous_headings":"","what":"Statement of need","title":"Summary","text":"R ships lot functionality useful time series, particular base stats [@R-stats], zoo [@R-zoo] packages. However, features adapted archaeological time series. Archaeological data typically collected field excavations surveys, resulting irregularly spaced observation times. Although several packages can handle irregular time series, way represent dates means easily used archaeological series. indeed defined given calendar era importantly can involve dates far past. aion provides system classes methods represent work time series. package provide tools temporal analysis modeling. Instead, offers system classes methods represent work archaeological time series. API can extended used specialized packages.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/paper.html","id":"functionality","dir":"","previous_headings":"","what":"Functionality","title":"Summary","text":"base R, dates represented default number days since 1970-01-01 (Gregorian), negative values earlier dates. aion uses different approach: allows create date vectors represented rata die [@reingold2018], .e.\u00a0number days since 01-01-01 (Gregorian). allows represent dates independently calendar makes calculations comparisons easier. aion time series represented S4 class inherits base array. time series object n\u2005\u00d7\u2005m\u2005\u00d7\u2005p array, n number observations, m number series p columns third dimension containing extra variables series. array comes extra time slot store observations times expressed rata die. output produced aion can formatted (virtually) calendar, long calendar defined associated conversion methods available. aion natively supports Julian Gregorian calendars (common eras latter, e.g.\u00a02000, Present, () Common Era\u2026) allows create custom calendars.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":[],"package":"aion"},{"path":[],"package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/AD-class.html","id":null,"dir":"Reference","previous_headings":"","what":"AD \u2014 AD-class","title":"AD \u2014 AD-class","text":"S4 class represent (Gregorian) AD era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/AD-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"AD \u2014 AD-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BC-class.html","id":null,"dir":"Reference","previous_headings":"","what":"BC \u2014 BC-class","title":"BC \u2014 BC-class","text":"S4 class represent (Gregorian) BC era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BC-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BC \u2014 BC-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BCE-class.html","id":null,"dir":"Reference","previous_headings":"","what":"BCE \u2014 BCE-class","title":"BCE \u2014 BCE-class","text":"S4 class represent (Gregorian) BCE era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BCE-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BCE \u2014 BCE-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BP-class.html","id":null,"dir":"Reference","previous_headings":"","what":"BP \u2014 BP-class","title":"BP \u2014 BP-class","text":"S4 class represent (Gregorian) BP era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BP-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BP \u2014 BP-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/CE-class.html","id":null,"dir":"Reference","previous_headings":"","what":"CE \u2014 CE-class","title":"CE \u2014 CE-class","text":"S4 class represent (Gregorian) CE era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/CE-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"CE \u2014 CE-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/GregorianCalendar-class.html","id":null,"dir":"Reference","previous_headings":"","what":"GregorianCalendar \u2014 GregorianCalendar-class","title":"GregorianCalendar \u2014 GregorianCalendar-class","text":"S4 class represent Gregorian calendar.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/GregorianCalendar-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"GregorianCalendar \u2014 GregorianCalendar-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/JulianCalendar-class.html","id":null,"dir":"Reference","previous_headings":"","what":"JulianCalendar \u2014 JulianCalendar-class","title":"JulianCalendar \u2014 JulianCalendar-class","text":"S4 class represent Julian calendar.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/JulianCalendar-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"JulianCalendar \u2014 JulianCalendar-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/RataDie-class.html","id":null,"dir":"Reference","previous_headings":"","what":"RataDie \u2014 RataDie-class","title":"RataDie \u2014 RataDie-class","text":"S4 class represent vector rata die.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/RataDie-class.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RataDie \u2014 RataDie-class","text":"Rata die (fixed date) represented number days since 01-01-01 (Gregorian), negative values earlier dates. intended date integer value, enforced internal representation.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/RataDie-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"RataDie \u2014 RataDie-class","text":".Data numeric vector giving rata die values.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/RataDie-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"RataDie \u2014 RataDie-class","text":"class inherits numeric.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/RataDie-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"RataDie \u2014 RataDie-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeScale-class.html","id":null,"dir":"Reference","previous_headings":"","what":"TimeScale \u2014 TimeScale-class","title":"TimeScale \u2014 TimeScale-class","text":"virtual S4 class represent calendar.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeScale-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"TimeScale \u2014 TimeScale-class","text":"label character string specifying abbreviated label time scale. name character string specifying name time scale. epoch numeric value specifying epoch year years counted (starting date calendar, years). Allows define multiple era calendar. fixed numeric value specifying reference date calendar (rata die). direction integer specifying years counted backwards (-1) forwards (1) epoch. year numeric value giving average length year solar days.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeScale-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"TimeScale \u2014 TimeScale-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeSeries-class.html","id":null,"dir":"Reference","previous_headings":"","what":"TimeSeries \u2014 TimeSeries-class","title":"TimeSeries \u2014 TimeSeries-class","text":"S4 class represent time series.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeSeries-class.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TimeSeries \u2014 TimeSeries-class","text":"time series object \\(n x m x p\\) array, \\(n\\) number observations, \\(m\\) number series \\(p\\) columns third dimension containing extra variables series.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeSeries-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"TimeSeries \u2014 TimeSeries-class","text":".Data \\(n x m x p\\) numeric array giving observed time-series values. .Time length-\\(n\\) RataDie object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeSeries-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"TimeSeries \u2014 TimeSeries-class","text":"class inherits array.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeSeries-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"TimeSeries \u2014 TimeSeries-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/aion-package.html","id":null,"dir":"Reference","previous_headings":"","what":"aion: Archaeological Time Series \u2014 aion-package","title":"aion: Archaeological Time Series \u2014 aion-package","text":"toolkit archaeological time series. package provides system classes methods represent work archaeological time series. Dates represented \"rata die\" can converted (virtually) calendar defined Reingold Dershowitz (2018) doi:10.1017/9781107415058 . packages offers simple API can used specialized packages.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/aion-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"aion: Archaeological Time Series \u2014 aion-package","text":"aion uses following options() configure behaviour: aion.precision: integer indicating number decimal places (defaults NA). aion.calendar: TimeScale object (default calendar printing).","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/aion-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"aion: Archaeological Time Series \u2014 aion-package","text":"Full list authors contributors (alphabetic order): Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":null,"dir":"Reference","previous_headings":"","what":"Arithmetic Operators \u2014 arithmetic","title":"Arithmetic Operators \u2014 arithmetic","text":"Operators performing arithmetic operations.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arithmetic Operators \u2014 arithmetic","text":"","code":"# S4 method for RataDie,RataDie Arith(e1, e2)  # S4 method for numeric,RataDie Arith(e1, e2)  # S4 method for RataDie,numeric Arith(e1, e2)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arithmetic Operators \u2014 arithmetic","text":"e1, e2 RataDie object numeric vector.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Arithmetic Operators \u2014 arithmetic","text":"logical vector.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Arithmetic Operators \u2014 arithmetic","text":"Rata die converted plain numeric vector computation longer makes sense temporal terms.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Arithmetic Operators \u2014 arithmetic","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arithmetic Operators \u2014 arithmetic","text":"","code":"## Vectors of years x <- fixed(c(-350, 31, 1072, 576, 1130), calendar = CE()) y <- fixed(c(1494, 1645, -869, 1440, 1851), calendar = CE())  ## Move forward in time x + y #> Rata die: number of days since 01-01-01 (Gregorian). #> [1]  417109  611418   73414  735599 1088058  ## Move backward in time x - y #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] -673507 -589502  708936 -315569 -263340  ## Not rata die anymore x * y #> [1]  -69907940292    6579840680 -124300159175  110380523760  278630563941","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":null,"dir":"Reference","previous_headings":"","what":"Date Conversion from Rata Die \u2014 as_date","title":"Date Conversion from Rata Die \u2014 as_date","text":"Date Conversion Rata Die","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Date Conversion from Rata Die \u2014 as_date","text":"","code":"as_date(object, calendar)  # S4 method for numeric,GregorianCalendar as_date(object, calendar)  # S4 method for numeric,JulianCalendar as_date(object, calendar)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Date Conversion from Rata Die \u2014 as_date","text":"object RataDie object (see fixed()). calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Date Conversion from Rata Die \u2014 as_date","text":"numeric vector (decimal) years.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Date Conversion from Rata Die \u2014 as_date","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Date Conversion from Rata Die \u2014 as_date","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Date Conversion from Rata Die \u2014 as_date","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a Date to a Decimal of its Year \u2014 as_decimal","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"Converts Date Decimal Year","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"","code":"as_decimal(year, month, day, calendar)  # S4 method for numeric,numeric,numeric,TimeScale as_decimal(year, month, day, calendar)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"year numeric vector years. month day missing, decimal years expected. month numeric vector months. day numeric vector days. calendar TimeScale object specifying calendar year, month day (see calendar()).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"numeric vector (ecimal years.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to Rata Die \u2014 as_fixed","title":"Coerce to Rata Die \u2014 as_fixed","text":"Coerce Rata Die","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to Rata Die \u2014 as_fixed","text":"","code":"as_fixed(from)  # S4 method for numeric as_fixed(from)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to Rata Die \u2014 as_fixed","text":"numeric vector rata die.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to Rata Die \u2014 as_fixed","text":"RataDie object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Coerce to Rata Die \u2014 as_fixed","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to Rata Die \u2014 as_fixed","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to Rata Die \u2014 as_fixed","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":null,"dir":"Reference","previous_headings":"","what":"Year Conversion from Rata Die \u2014 as_year","title":"Year Conversion from Rata Die \u2014 as_year","text":"Year Conversion Rata Die","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Year Conversion from Rata Die \u2014 as_year","text":"","code":"as_year(object, calendar, ...)  # S4 method for numeric,GregorianCalendar as_year(object, calendar, decimal = TRUE)  # S4 method for numeric,JulianCalendar as_year(object, calendar)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Year Conversion from Rata Die \u2014 as_year","text":"object RataDie object (see fixed()). calendar TimeScale object specifying target calendar (see calendar()). ... Currently used. decimal logical scalar: decimal years returned? FALSE, decimal part dropped.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Year Conversion from Rata Die \u2014 as_year","text":"numeric vector (decimal) years.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Year Conversion from Rata Die \u2014 as_year","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Year Conversion from Rata Die \u2014 as_year","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Year Conversion from Rata Die \u2014 as_year","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/b2k-class.html","id":null,"dir":"Reference","previous_headings":"","what":"b2k \u2014 b2k-class","title":"b2k \u2014 b2k-class","text":"S4 class represent (Gregorian) b2k era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/b2k-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"b2k \u2014 b2k-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":null,"dir":"Reference","previous_headings":"","what":"Calendar \u2014 calendar","title":"Calendar \u2014 calendar","text":"Calendar","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calendar \u2014 calendar","text":"","code":"calendar(object)  # S4 method for character calendar(object)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calendar \u2014 calendar","text":"object character string specifying abbreviated label time scale (see details) object extract time scale.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calendar \u2014 calendar","text":"TimeScale object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calendar \u2014 calendar","text":"following time scales available:","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calendar \u2014 calendar","text":"Inspired era::era() Joe Roe.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calendar \u2014 calendar","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calendar \u2014 calendar","text":"","code":"## Define time scales calendar(\"BP\") #> Before Present (BP): Gregorian years counted backwards from 1950. calendar(\"AD\") #> Anno Domini (AD): Gregorian years counted forwards from 0. calendar(\"julian\") #> Julian years counted forwards from 1.  ## Shortcuts BP() #> Before Present (BP): Gregorian years counted backwards from 1950. AD() #> Anno Domini (AD): Gregorian years counted forwards from 0. J() #> Julian years counted forwards from 1.","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":null,"dir":"Reference","previous_headings":"","what":"Calendar Parameters \u2014 calendar_get","title":"Calendar Parameters \u2014 calendar_get","text":"Calendar Parameters","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calendar Parameters \u2014 calendar_get","text":"","code":"calendar_label(object)  calendar_name(object)  calendar_unit(object)  calendar_epoch(object)  calendar_fixed(object)  calendar_direction(object)  # S4 method for TimeScale calendar_label(object)  # S4 method for TimeScale calendar_name(object)  # S4 method for TimeScale calendar_unit(object)  # S4 method for TimeScale calendar_epoch(object)  # S4 method for TimeScale calendar_fixed(object)  # S4 method for TimeScale calendar_direction(object)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calendar Parameters \u2014 calendar_get","text":"object TimeScale object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calendar Parameters \u2014 calendar_get","text":"calendar_label() returns character string specifying abbreviated label time scale. calendar_name() returns character string specifying name time scale. calendar_unit() returns character string specifying name calendar. calendar_fixed() returns length-one numeric vector specifying reference date calendar (rata die). calendar_epoch() returns length-one numeric vector specifying epoch year years counted (starting date calendar, years). calendar_direction() returns length-one integer vector specifying years counted backwards (\\(-1\\)) forwards (\\(1\\)) epoch. sign calendar_direction() relevant.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calendar Parameters \u2014 calendar_get","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calendar Parameters \u2014 calendar_get","text":"","code":"## Define time scales calendar(\"BP\") #> Before Present (BP): Gregorian years counted backwards from 1950. calendar(\"AD\") #> Anno Domini (AD): Gregorian years counted forwards from 0. calendar(\"julian\") #> Julian years counted forwards from 1.  ## Shortcuts BP() #> Before Present (BP): Gregorian years counted backwards from 1950. AD() #> Anno Domini (AD): Gregorian years counted forwards from 0. J() #> Julian years counted forwards from 1.","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":null,"dir":"Reference","previous_headings":"","what":"Calendar Converter \u2014 convert","title":"Calendar Converter \u2014 convert","text":"Interconverts dates variety calendars.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calendar Converter \u2014 convert","text":"","code":"convert(from, to, ...)  # S4 method for character,character convert(from, to)  # S4 method for TimeScale,TimeScale convert(from, to)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calendar Converter \u2014 convert","text":"TimeScale object describing source calendar. TimeScale object describing target calendar. ... Currently used.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calendar Converter \u2014 convert","text":"function called single numeric argument (factional years) converts years one calendar another.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calendar Converter \u2014 convert","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calendar Converter \u2014 convert","text":"","code":"## Define time scales BP <- calendar(\"BP\") AD <- calendar(\"AD\")  ## Make conversion functions BP_to_AD <- convert(BP, AD) AD_to_BP <- convert(AD, BP)  ## Convert years BP_to_AD(0) #> [1] 1950 AD_to_BP(1950) #> [1] 0","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Data Frame \u2014 data.frame","title":"Coerce to a Data Frame \u2014 data.frame","text":"Coerce Data Frame","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Data Frame \u2014 data.frame","text":"","code":"# S4 method for TimeSeries as.data.frame(x, ..., calendar = NULL)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Data Frame \u2014 data.frame","text":"x TimeSeries object. ... parameters passed data.frame(). calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Data Frame \u2014 data.frame","text":"long data.frame following columns: time (decimal) years time series sampled. series name time series. variable name variables. value observed value.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Data Frame \u2014 data.frame","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to a Data Frame \u2014 data.frame","text":"","code":"## Create time-series of 20 observations  ## Univariate ## Sampled every years starting from 1029 BCE (X <- series(rnorm(30), time = 1029:1000, calendar = BCE())) #> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.  ## Terminal and sampling times (returns rata die) start(X) #> [1] -376199 end(X) #> [1] -365607 time(X) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277 #> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990 #> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702 #> [28] -366337 -365972 -365607 span(X) #> [1] 10592  ## Multivariate ## Sampled every century starting from 1000 CE (Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE())) #> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.  ## Terminal and sampling times (returns Gregorian Common Era years) start(Y, calendar = CE()) #> [1] 1000 end(Y, calendar = CE()) #> [1] 1029 time(Y, calendar = CE()) #>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 span(Y, calendar = CE()) #> [1] 29.99726  ## Coerce to data frame df <- as.data.frame(Y, calendar = BP()) head(df) #>   time series variable      value #> 1  950     S1        A  2.0329711 #> 2  949     S1        A  1.5869806 #> 3  948     S1        A  1.0943139 #> 4  947     S1        A -0.6339700 #> 5  946     S1        A -0.9750187 #> 6  945     S1        A  0.7335285","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/dates.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample Data from Reingold and Dershowitz (2018) \u2014 dates","title":"Sample Data from Reingold and Dershowitz (2018) \u2014 dates","text":"dataset 33 dates years -1000 2100 equivalents differents calendars.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/dates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample Data from Reingold and Dershowitz (2018) \u2014 dates","text":"","code":"dates","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/dates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sample Data from Reingold and Dershowitz (2018) \u2014 dates","text":"data.frame 33 rows 14 variables: rata_die Rata die. weekday Week day. jd Julian day. mjd Modified Julian day. unix Unix. gregorian_year, gregorian_month, gregorian_day Gregorian date. julian_year, julian_month, julian_day Julian date. egyptian_year, egyptian_month, egyptian_day Egyptian date.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/dates.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sample Data from Reingold and Dershowitz (2018) \u2014 dates","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Rata Die (Fixed Date) \u2014 fixed","title":"Rata Die (Fixed Date) \u2014 fixed","text":"Rata Die (Fixed Date)","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rata Die (Fixed Date) \u2014 fixed","text":"","code":"fixed(year, month, day, calendar, ...)  # S4 method for numeric,missing,missing,GregorianCalendar fixed(year, calendar, scale = 1)  # S4 method for numeric,numeric,numeric,GregorianCalendar fixed(year, month, day, calendar)  # S4 method for numeric,numeric,numeric,JulianCalendar fixed(year, month, day, calendar)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rata Die (Fixed Date) \u2014 fixed","text":"year numeric vector years. month day missing, decimal years expected. month numeric vector months. day numeric vector days. calendar TimeScale object specifying calendar year, month day (see calendar()). ... Currently used. scale length-one integer vector specifying number years represented one unit. power 10 (.e. 1000 means ka).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rata Die (Fixed Date) \u2014 fixed","text":"RataDie object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rata Die (Fixed Date) \u2014 fixed","text":"Rata die represented number days since 01-01-01 (Gregorian), negative values earlier dates.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rata Die (Fixed Date) \u2014 fixed","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rata Die (Fixed Date) \u2014 fixed","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rata Die (Fixed Date) \u2014 fixed","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":null,"dir":"Reference","previous_headings":"","what":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"Convenient functions conversion rata die given Gregorian era.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"","code":"fixed_from_BP(year, month, day)  fixed_to_BP(object)  fixed_from_BC(year, month, day)  fixed_to_BC(object)  fixed_from_BCE(year, month, day)  fixed_to_BCE(object)  fixed_from_AD(year, month, day)  fixed_to_AD(object)  fixed_from_CE(year, month, day)  fixed_to_CE(object)  fixed_from_b2k(year, month, day)  fixed_to_b2k(object)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"year numeric vector years. month day missing, decimal years expected. month numeric vector months. day numeric vector days. object RataDie object (see fixed()).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"fixed_from_*() returns RataDie object. fixed_to_*() returns numeric vector Gregorian years.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"astronomical notation used Gregorian years (year 0).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":null,"dir":"Reference","previous_headings":"","what":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"Convenient functions conversion rata die.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"","code":"fixed_from_julian(year, month, day)  fixed_to_julian(object)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"year numeric vector years. month day missing, decimal years expected. month numeric vector months. day numeric vector days. object RataDie object (see fixed()).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"fixed_from_julian() returns RataDie object. fixed_to_julian() returns numeric vector Julian years.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":null,"dir":"Reference","previous_headings":"","what":"Date Conversion to Character \u2014 format","title":"Date Conversion to Character \u2014 format","text":"Date Conversion Character","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Date Conversion to Character \u2014 format","text":"","code":"# S4 method for TimeScale format(x)  # S4 method for RataDie format(   x,   format = c(\"a\", \"ka\", \"Ma\", \"Ga\"),   label = TRUE,   calendar = getOption(\"aion.calendar\") )","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Date Conversion to Character \u2014 format","text":"x RataDie object. format character string specifying prefix. one \"\", \"ka\", \"Ma\" \"Ga\". TRUE, good guess appropriate format made. label logical scalar: label calendar displayed? calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Date Conversion to Character \u2014 format","text":"character vector representing date.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Date Conversion to Character \u2014 format","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Date Conversion to Character \u2014 format","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":null,"dir":"Reference","previous_headings":"","what":"Gregorian Calendar \u2014 gregorian","title":"Gregorian Calendar \u2014 gregorian","text":"Gregorian Calendar","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gregorian Calendar \u2014 gregorian","text":"","code":"BP(...)  b2k(...)  BC(...)  BCE(...)  AD(...)  CE(...)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gregorian Calendar \u2014 gregorian","text":"... Currently used.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gregorian Calendar \u2014 gregorian","text":"GregorianCalendar object.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Gregorian Calendar \u2014 gregorian","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gregorian Calendar \u2014 gregorian","text":"","code":"## Define time scales calendar(\"BP\") #> Before Present (BP): Gregorian years counted backwards from 1950. calendar(\"AD\") #> Anno Domini (AD): Gregorian years counted forwards from 0. calendar(\"julian\") #> Julian years counted forwards from 1.  ## Shortcuts BP() #> Before Present (BP): Gregorian years counted backwards from 1950. AD() #> Anno Domini (AD): Gregorian years counted forwards from 0. J() #> Julian years counted forwards from 1.","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":null,"dir":"Reference","previous_headings":"","what":"Heat Map \u2014 image","title":"Heat Map \u2014 image","text":"Heat Map","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heat Map \u2014 image","text":"","code":"# S4 method for TimeSeries image(x, calendar = getOption(\"aion.calendar\"), k = 1, ...)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heat Map \u2014 image","text":"x TimeSeries object. calendar TimeScale object specifying target calendar (see calendar()). k integer specifying slice x along third dimension plotted. ... parameters passed graphics::image().","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heat Map \u2014 image","text":"image() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heat Map \u2014 image","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heat Map \u2014 image","text":"","code":"## Create 6 time-series of 50 observations ## Sampled every two years starting from 2000 BP X <- series(   object = matrix(rnorm(300), nrow = 50, ncol = 6),   time = seq(2000, by = -2, length.out = 50),   calendar = BP() )  ## Image image(X, calendar = CE())","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/is.html","id":null,"dir":"Reference","previous_headings":"","what":"Is an Object a Calendar? \u2014 is","title":"Is an Object a Calendar? \u2014 is","text":"Test inheritance relationships object calendar class.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/is.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is an Object a Calendar? \u2014 is","text":"","code":"is_gregorian(object)  is_julian(object)  # S4 method for ANY is_gregorian(object)  # S4 method for ANY is_julian(object)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/is.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is an Object a Calendar? \u2014 is","text":"object R object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/is.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Is an Object a Calendar? \u2014 is","text":"logical vector.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/is.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Is an Object a Calendar? \u2014 is","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":null,"dir":"Reference","previous_headings":"","what":"Julian Calendar \u2014 julian","title":"Julian Calendar \u2014 julian","text":"Julian Calendar","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Julian Calendar \u2014 julian","text":"","code":"J(...)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Julian Calendar \u2014 julian","text":"... Currently used.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Julian Calendar \u2014 julian","text":"JulianCalendar object.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Julian Calendar \u2014 julian","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Julian Calendar \u2014 julian","text":"","code":"## Define time scales calendar(\"BP\") #> Before Present (BP): Gregorian years counted backwards from 1950. calendar(\"AD\") #> Anno Domini (AD): Gregorian years counted forwards from 0. calendar(\"julian\") #> Julian years counted forwards from 1.  ## Shortcuts BP() #> Before Present (BP): Gregorian years counted backwards from 1950. AD() #> Anno Domini (AD): Gregorian years counted forwards from 0. J() #> Julian years counted forwards from 1.","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Time Series \u2014 plot","title":"Plot Time Series \u2014 plot","text":"Plot Time Series","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Time Series \u2014 plot","text":"","code":"# S4 method for TimeSeries,missing plot(   x,   facet = c(\"multiple\", \"single\"),   calendar = getOption(\"aion.calendar\"),   panel = graphics::lines,   flip = FALSE,   ncol = NULL,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Time Series \u2014 plot","text":"x TimeSeries object. facet character string specifying whether series plotted separately (common time axis) single plot? must one \"multiple\" \"single\". unambiguous substring can given. calendar TimeScale object specifying target calendar (see calendar()). panel function form function(x, y, ...) gives action carried panel display. default graphics::lines(). flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... parameters passed panel (e.g. graphical parameters).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Time Series \u2014 plot","text":"plot() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Time Series \u2014 plot","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Time Series \u2014 plot","text":"","code":"## Create 6 time-series of 50 observations ## Sampled every two years starting from 2000 BP X <- series(   object = matrix(rnorm(300), nrow = 50, ncol = 6),   time = seq(2000, by = -2, length.out = 50),   calendar = BP() )  ## Multiple plot(X) # Default calendar  plot(X, calendar = BP(), flip = TRUE) # BP  plot(X, calendar = b2k(), ncol = 1) # b2k   ## Single plot(X, facet = \"single\") # CE  plot(X, facet = \"single\", calendar = BP()) # BP   ## Create 6 x 3 time-series of 50 observations ## Sampled every two years starting from 2000 BP X <- series(   object = array(rnorm(900), dim = c(50, 6, 3)),   time = seq(2000, by = 2, length.out = 50),   calendar = BP() ) plot(X, calendar = BP(), flip = TRUE) # BP  plot(X, calendar = b2k(), ncol = 1) # b2k   ## Graphical parameters plot(X, lwd = c(1, 2, 3), col = c(\"#004488\", \"#DDAA33\", \"#BB5566\"))  plot(X, type = \"b\", pch = 16, col = c(\"#004488\", \"#DDAA33\", \"#BB5566\"))  plot(X, type = \"p\", pch = c(16, 17, 18), cex = c(1, 2, 3))","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/pretty.html","id":null,"dir":"Reference","previous_headings":"","what":"Pretty Breakpoints \u2014 pretty","title":"Pretty Breakpoints \u2014 pretty","text":"Pretty Breakpoints","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/pretty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pretty Breakpoints \u2014 pretty","text":"","code":"# S4 method for RataDie pretty(x, calendar = getOption(\"aion.calendar\"), ...)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/pretty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pretty Breakpoints \u2014 pretty","text":"x RataDie object. calendar TimeScale object specifying target calendar (see calendar()). ... parameters passed base::pretty().","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/pretty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pretty Breakpoints \u2014 pretty","text":"RataDie object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/pretty.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pretty Breakpoints \u2014 pretty","text":"pretty() computes vector increasing numbers \"pretty\" decimal notation calendar. Pretty breakpoints converted rata die.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Time Series \u2014 series","title":"Create Time Series \u2014 series","text":"Create Time Series","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Time Series \u2014 series","text":"","code":"series(object, time, calendar, ...)  # S4 method for array,RataDie,missing series(object, time, names = NULL)  # S4 method for array,numeric,TimeScale series(object, time, calendar, scale = 1, names = NULL)  # S4 method for matrix,numeric,TimeScale series(object, time, calendar, scale = 1, names = NULL)  # S4 method for matrix,RataDie,missing series(object, time, names = NULL)  # S4 method for numeric,numeric,TimeScale series(object, time, calendar, scale = 1, names = NULL)  # S4 method for numeric,RataDie,missing series(object, time, names = NULL)  # S4 method for data.frame,numeric,TimeScale series(object, time, calendar, scale = 1, names = NULL)  # S4 method for data.frame,RataDie,missing series(object, time, names = NULL)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Time Series \u2014 series","text":"object numeric vector, matrix array observed time-series values. data.frame coerced numeric matrix via data.matrix(). time numeric vector (decimal) years RataDie object (see fixed()). calendar TimeScale object specifying calendar time (see calendar()). missing, time must RataDie object. ... Currently used. names character string specifying names time series. scale length-one numeric vector specifying number years represented one unit. power 10 (.e. 1000 means ka).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Time Series \u2014 series","text":"TimeSeries object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create Time Series \u2014 series","text":"Data sorted chronological order.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create Time Series \u2014 series","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create Time Series \u2014 series","text":"","code":"## Create time-series of 20 observations  ## Univariate ## Sampled every years starting from 1029 BCE (X <- series(rnorm(30), time = 1029:1000, calendar = BCE())) #> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.  ## Terminal and sampling times (returns rata die) start(X) #> [1] -376199 end(X) #> [1] -365607 time(X) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277 #> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990 #> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702 #> [28] -366337 -365972 -365607 span(X) #> [1] 10592  ## Multivariate ## Sampled every century starting from 1000 CE (Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE())) #> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.  ## Terminal and sampling times (returns Gregorian Common Era years) start(Y, calendar = CE()) #> [1] 1000 end(Y, calendar = CE()) #> [1] 1029 time(Y, calendar = CE()) #>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 span(Y, calendar = CE()) #> [1] 29.99726  ## Coerce to data frame df <- as.data.frame(Y, calendar = BP()) head(df) #>   time series variable      value #> 1  950     S1        A  0.6980847 #> 2  949     S1        A -0.9906964 #> 3  948     S1        A  0.2091514 #> 4  947     S1        A  0.7175182 #> 5  946     S1        A -0.1098521 #> 6  945     S1        A  1.8658180","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":null,"dir":"Reference","previous_headings":"","what":"Duration of Time Series \u2014 span","title":"Duration of Time Series \u2014 span","text":"Get duration.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Duration of Time Series \u2014 span","text":"","code":"span(x, ...)  # S4 method for TimeSeries span(x, calendar = NULL)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Duration of Time Series \u2014 span","text":"x TimeSeries object. ... Currently used. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Duration of Time Series \u2014 span","text":"numeric vector.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Duration of Time Series \u2014 span","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Duration of Time Series \u2014 span","text":"","code":"## Create time-series of 20 observations  ## Univariate ## Sampled every years starting from 1029 BCE (X <- series(rnorm(30), time = 1029:1000, calendar = BCE())) #> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.  ## Terminal and sampling times (returns rata die) start(X) #> [1] -376199 end(X) #> [1] -365607 time(X) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277 #> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990 #> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702 #> [28] -366337 -365972 -365607 span(X) #> [1] 10592  ## Multivariate ## Sampled every century starting from 1000 CE (Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE())) #> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.  ## Terminal and sampling times (returns Gregorian Common Era years) start(Y, calendar = CE()) #> [1] 1000 end(Y, calendar = CE()) #> [1] 1029 time(Y, calendar = CE()) #>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 span(Y, calendar = CE()) #> [1] 29.99726  ## Coerce to data frame df <- as.data.frame(Y, calendar = BP()) head(df) #>   time series variable      value #> 1  950     S1        A  0.2281259 #> 2  949     S1        A  1.6220241 #> 3  948     S1        A  1.0429135 #> 4  947     S1        A -0.9253764 #> 5  946     S1        A  0.2305605 #> 6  945     S1        A -1.5364367","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":null,"dir":"Reference","previous_headings":"","what":"Terminal Times of Time Series \u2014 start","title":"Terminal Times of Time Series \u2014 start","text":"Get times first last observations taken.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Terminal Times of Time Series \u2014 start","text":"","code":"# S4 method for TimeSeries start(x, calendar = NULL)  # S4 method for TimeSeries end(x, calendar = NULL)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Terminal Times of Time Series \u2014 start","text":"x TimeSeries object. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Terminal Times of Time Series \u2014 start","text":"numeric vector.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Terminal Times of Time Series \u2014 start","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Terminal Times of Time Series \u2014 start","text":"","code":"## Create time-series of 20 observations  ## Univariate ## Sampled every years starting from 1029 BCE (X <- series(rnorm(30), time = 1029:1000, calendar = BCE())) #> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.  ## Terminal and sampling times (returns rata die) start(X) #> [1] -376199 end(X) #> [1] -365607 time(X) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277 #> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990 #> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702 #> [28] -366337 -365972 -365607 span(X) #> [1] 10592  ## Multivariate ## Sampled every century starting from 1000 CE (Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE())) #> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.  ## Terminal and sampling times (returns Gregorian Common Era years) start(Y, calendar = CE()) #> [1] 1000 end(Y, calendar = CE()) #> [1] 1029 time(Y, calendar = CE()) #>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 span(Y, calendar = CE()) #> [1] 29.99726  ## Coerce to data frame df <- as.data.frame(Y, calendar = BP()) head(df) #>   time series variable       value #> 1  950     S1        A  0.97241451 #> 2  949     S1        A -0.70127022 #> 3  948     S1        A  0.06704523 #> 4  947     S1        A -0.67246065 #> 5  946     S1        A  0.90582968 #> 6  945     S1        A  1.30456740","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object \u2014 subset","title":"Extract or Replace Parts of an Object \u2014 subset","text":"Operators acting objects extract replace parts.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object \u2014 subset","text":"","code":"# S4 method for RataDie [(x, i)  # S4 method for TimeSeries [(x, i, j, k, drop = FALSE)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object \u2014 subset","text":"x object extract element(s) replace element(s). , j, k Indices specifying elements extract replace. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object \u2014 subset","text":"subsetted object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object \u2014 subset","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":null,"dir":"Reference","previous_headings":"","what":"Sampling Times of Time Series \u2014 time","title":"Sampling Times of Time Series \u2014 time","text":"Get sampling times: time() creates vector times time series sampled. frequency() returns mean number samples per unit time.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sampling Times of Time Series \u2014 time","text":"","code":"# S4 method for TimeSeries time(x, calendar = NULL)  # S4 method for TimeSeries frequency(x)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sampling Times of Time Series \u2014 time","text":"x TimeSeries object. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sampling Times of Time Series \u2014 time","text":"numeric vector.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sampling Times of Time Series \u2014 time","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sampling Times of Time Series \u2014 time","text":"","code":"## Create time-series of 20 observations  ## Univariate ## Sampled every years starting from 1029 BCE (X <- series(rnorm(30), time = 1029:1000, calendar = BCE())) #> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.  ## Terminal and sampling times (returns rata die) start(X) #> [1] -376199 end(X) #> [1] -365607 time(X) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277 #> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990 #> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702 #> [28] -366337 -365972 -365607 span(X) #> [1] 10592  ## Multivariate ## Sampled every century starting from 1000 CE (Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE())) #> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.  ## Terminal and sampling times (returns Gregorian Common Era years) start(Y, calendar = CE()) #> [1] 1000 end(Y, calendar = CE()) #> [1] 1029 time(Y, calendar = CE()) #>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 span(Y, calendar = CE()) #> [1] 29.99726  ## Coerce to data frame df <- as.data.frame(Y, calendar = BP()) head(df) #>   time series variable        value #> 1  950     S1        A -0.528586751 #> 2  949     S1        A -0.127997142 #> 3  948     S1        A -0.585114787 #> 4  947     S1        A  0.003627898 #> 5  946     S1        A -0.308915896 #> 6  945     S1        A -1.265319276","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":null,"dir":"Reference","previous_headings":"","what":"Time Series Windows \u2014 window","title":"Time Series Windows \u2014 window","text":"Extracts subset object x observed times start end (expressed rata die).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Time Series Windows \u2014 window","text":"","code":"# S4 method for TimeSeries window(x, start = NULL, end = NULL)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Time Series Windows \u2014 window","text":"x TimeSeries object. start length-one numeric vector specifying start time period interest. end length-one numeric vector specifying end time period interest.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Time Series Windows \u2014 window","text":"TimeSeries object.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Time Series Windows \u2014 window","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Time Series Windows \u2014 window","text":"","code":"## Create 3 time-series of 100 observations ## Sampled every years starting from 1000 CE (x <- series(matrix(rnorm(300), 100, 3), time = 1000:1099, calendar = CE())) #> 100 x 3 x 1 time series observed between 364878 and 401037 r.d.  ## Subset between 1025 and 1050 CE (y <- window(x, start = 374009, end = 383140)) #> 26 x 3 x 1 time series observed between 374009 and 383140 r.d.","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":null,"dir":"Reference","previous_headings":"","what":"Time Series Plotting Functions \u2014 year_axis","title":"Time Series Plotting Functions \u2014 year_axis","text":"Time Series Plotting Functions","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Time Series Plotting Functions \u2014 year_axis","text":"","code":"year_axis(   side,   at = NULL,   format = c(\"a\", \"ka\", \"Ma\", \"Ga\"),   labels = TRUE,   calendar = getOption(\"aion.last_calendar\"),   current_calendar = getOption(\"aion.last_calendar\"),   ... )","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Time Series Plotting Functions \u2014 year_axis","text":"side integer specifying side plot axis drawn . axis placed follows: 1=, 2=left, 3=4=right. numeric vector giving points tick-marks drawn. NULL, tickmark locations computed. format character string specifying prefix. one \"\", \"ka\", \"Ma\" \"Ga\". TRUE, good guess appropriate format made. labels logical scalar specifying whether annotations made tickmarks, vector character strings placed tickpoints. calendar TimeScale object specifying target calendar (see calendar()). current_calendar TimeScale object specifying calendar used last call plot(). ... parameters passed graphics::axis(). (e.g. graphical parameters).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Time Series Plotting Functions \u2014 year_axis","text":"year_axis() called side-effects.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Time Series Plotting Functions \u2014 year_axis","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Time Series Plotting Functions \u2014 year_axis","text":"","code":"## Create a time-series of 300 observations ## Sampled every two years starting from 2000 BP X <- series(   object = rnorm(300),   time = seq(2000, by = -2, length.out = 300),   calendar = BP() )  ## Axis plot(X, axes = FALSE, calendar = BP()) # Remove axes year_axis(side = 1) # Same calendar as last plot year_axis(side = 3, calendar = CE()) # Specific calendar mtext(format(CE()), side = 3, line = 3)   ## Grid plot(X, panel.first = graphics::grid())","package":"aion"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Fabio Crameri's Color Schemes","text":"Crameri (2018) offers carefully chosen schemes, ready mapping data, colors : Perceptually uniform, Perceptually ordered, Color vision deficiency friendly, Readable black white print, Citable & reproducible. scales presented Crameri (2021) implemented , use base R graphics ggplot2.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"broc","dir":"Articles","previous_headings":"Diverging data","what":"broc","title":"Fabio Crameri's Color Schemes","text":"","code":"broc <- color(\"broc\") plot(broc(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"cork","dir":"Articles","previous_headings":"Diverging data","what":"cork","title":"Fabio Crameri's Color Schemes","text":"","code":"cork <- color(\"cork\") plot(cork(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"vik","dir":"Articles","previous_headings":"Diverging data","what":"vik","title":"Fabio Crameri's Color Schemes","text":"","code":"vik <- color(\"vik\") plot(vik(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"lisbon","dir":"Articles","previous_headings":"Diverging data","what":"lisbon","title":"Fabio Crameri's Color Schemes","text":"","code":"lisbon <- color(\"lisbon\") plot(lisbon(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"tofino","dir":"Articles","previous_headings":"Diverging data","what":"tofino","title":"Fabio Crameri's Color Schemes","text":"","code":"tofino <- color(\"tofino\") plot(tofino(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"berlin","dir":"Articles","previous_headings":"Diverging data","what":"berlin","title":"Fabio Crameri's Color Schemes","text":"","code":"berlin <- color(\"berlin\") plot(berlin(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"roma","dir":"Articles","previous_headings":"Diverging data","what":"roma","title":"Fabio Crameri's Color Schemes","text":"","code":"roma <- color(\"roma\") plot(roma(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"bam","dir":"Articles","previous_headings":"Diverging data","what":"bam","title":"Fabio Crameri's Color Schemes","text":"","code":"bam <- color(\"bam\") plot(bam(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"vanimo","dir":"Articles","previous_headings":"Diverging data","what":"vanimo","title":"Fabio Crameri's Color Schemes","text":"","code":"vanimo <- color(\"vanimo\") plot(vanimo(256))","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"batlow","dir":"Articles","previous_headings":"Sequential data","what":"batlow","title":"Fabio Crameri's Color Schemes","text":"","code":"batlow <- color(\"batlow\") plot(batlow(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"batloww","dir":"Articles","previous_headings":"Sequential data","what":"batlowW","title":"Fabio Crameri's Color Schemes","text":"","code":"batlowW <- color(\"batlowW\") plot(batlowW(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"batlowk","dir":"Articles","previous_headings":"Sequential data","what":"batlowK","title":"Fabio Crameri's Color Schemes","text":"","code":"batlowK <- color(\"batlowK\") plot(batlowK(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"devon","dir":"Articles","previous_headings":"Sequential data","what":"devon","title":"Fabio Crameri's Color Schemes","text":"","code":"devon <- color(\"devon\") plot(devon(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"lajolla","dir":"Articles","previous_headings":"Sequential data","what":"lajolla","title":"Fabio Crameri's Color Schemes","text":"","code":"lajolla <- color(\"lajolla\") plot(lajolla(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"bamako","dir":"Articles","previous_headings":"Sequential data","what":"bamako","title":"Fabio Crameri's Color Schemes","text":"","code":"bamako <- color(\"bamako\") plot(bamako(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"davos","dir":"Articles","previous_headings":"Sequential data","what":"davos","title":"Fabio Crameri's Color Schemes","text":"","code":"davos <- color(\"davos\") plot(davos(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"bilbao","dir":"Articles","previous_headings":"Sequential data","what":"bilbao","title":"Fabio Crameri's Color Schemes","text":"","code":"bilbao <- color(\"bilbao\") plot(bilbao(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"nuuk","dir":"Articles","previous_headings":"Sequential data","what":"nuuk","title":"Fabio Crameri's Color Schemes","text":"","code":"nuuk <- color(\"nuuk\") plot(nuuk(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"oslo","dir":"Articles","previous_headings":"Sequential data","what":"oslo","title":"Fabio Crameri's Color Schemes","text":"","code":"oslo <- color(\"oslo\") plot(oslo(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"grayc","dir":"Articles","previous_headings":"Sequential data","what":"grayC","title":"Fabio Crameri's Color Schemes","text":"","code":"grayC <- color(\"grayC\") plot(grayC(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"hawaii","dir":"Articles","previous_headings":"Sequential data","what":"hawaii","title":"Fabio Crameri's Color Schemes","text":"","code":"hawaii <- color(\"hawaii\") plot(hawaii(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"lapaz","dir":"Articles","previous_headings":"Sequential data","what":"lapaz","title":"Fabio Crameri's Color Schemes","text":"","code":"lapaz <- color(\"lapaz\") plot(lapaz(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"tokyo","dir":"Articles","previous_headings":"Sequential data","what":"tokyo","title":"Fabio Crameri's Color Schemes","text":"","code":"tokyo <- color(\"tokyo\") plot(tokyo(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"buda","dir":"Articles","previous_headings":"Sequential data","what":"buda","title":"Fabio Crameri's Color Schemes","text":"","code":"buda <- color(\"buda\") plot(buda(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"acton","dir":"Articles","previous_headings":"Sequential data","what":"acton","title":"Fabio Crameri's Color Schemes","text":"","code":"acton <- color(\"acton\") plot(acton(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"turku","dir":"Articles","previous_headings":"Sequential data","what":"turku","title":"Fabio Crameri's Color Schemes","text":"","code":"turku <- color(\"turku\") plot(turku(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"imola","dir":"Articles","previous_headings":"Sequential data","what":"imola","title":"Fabio Crameri's Color Schemes","text":"","code":"imola <- color(\"imola\") plot(imola(256))","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"oleron","dir":"Articles","previous_headings":"Multi-sequential data","what":"oleron","title":"Fabio Crameri's Color Schemes","text":"","code":"oleron <- color(\"oleron\") plot(oleron(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"bukavu","dir":"Articles","previous_headings":"Multi-sequential data","what":"bukavu","title":"Fabio Crameri's Color Schemes","text":"","code":"bukavu <- color(\"bukavu\") plot(bukavu(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"fes","dir":"Articles","previous_headings":"Multi-sequential data","what":"fes","title":"Fabio Crameri's Color Schemes","text":"","code":"fes <- color(\"fes\") plot(fes(256))","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Paul Tol's Color Schemes","text":"Tol (2021) offers carefully chosen schemes, ready type data, colors : Distinct people, including color-blind readers, Distinct black white, Distinct screen paper, Matching well together. scales presented Paul Tol\u2019s technical note (issue 3.2, 2021-08-18) implemented , use base R graphics ggplot2.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"qualitative-data","dir":"Articles","previous_headings":"","what":"Qualitative data","title":"Paul Tol's Color Schemes","text":"According Paul Tol\u2019s technical note, bright, contrast, vibrant muted color schemes color-blind safe. light color scheme reasonably distinct normal color-blind vision intended fill labelled cells. pale dark schemes distinct either normal color-blind vision used text background highlight cell table. qualitative color schemes must used given (interpolation): colors picked maximum number supported values. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"bright","dir":"Articles","previous_headings":"Qualitative data","what":"bright","title":"Paul Tol's Color Schemes","text":"","code":"bright <- color(\"bright\") plot_scheme(bright(7), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"high-contrast","dir":"Articles","previous_headings":"Qualitative data","what":"high contrast","title":"Paul Tol's Color Schemes","text":"","code":"highcontrast <- color(\"high contrast\") plot_scheme(highcontrast(3), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"vibrant","dir":"Articles","previous_headings":"Qualitative data","what":"vibrant","title":"Paul Tol's Color Schemes","text":"","code":"vibrant <- color(\"vibrant\") plot_scheme(vibrant(7), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"muted","dir":"Articles","previous_headings":"Qualitative data","what":"muted","title":"Paul Tol's Color Schemes","text":"","code":"muted <- color(\"muted\") plot_scheme(muted(9), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"medium-contrast","dir":"Articles","previous_headings":"Qualitative data","what":"medium contrast","title":"Paul Tol's Color Schemes","text":"","code":"mediumcontrast <- color(\"medium contrast\") plot_scheme(mediumcontrast(6), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"pale-and-dark","dir":"Articles","previous_headings":"Qualitative data","what":"pale and dark","title":"Paul Tol's Color Schemes","text":"","code":"pale <- color(\"pale\") plot_scheme(pale(6), colours = TRUE, names = TRUE, size = 0.9) dark <- color(\"dark\") plot_scheme(dark(6), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"light","dir":"Articles","previous_headings":"Qualitative data","what":"light","title":"Paul Tol's Color Schemes","text":"","code":"light <- color(\"light\") plot_scheme(light(9), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"diverging-data","dir":"Articles","previous_headings":"","what":"Diverging data","title":"Paul Tol's Color Schemes","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"sunset","dir":"Articles","previous_headings":"Diverging data","what":"sunset","title":"Paul Tol's Color Schemes","text":"","code":"sunset <- color(\"sunset\") plot_scheme(sunset(9), colours = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"burd","dir":"Articles","previous_headings":"Diverging data","what":"BuRd","title":"Paul Tol's Color Schemes","text":"","code":"BuRd <- color(\"BuRd\") plot_scheme(BuRd(9), colours = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"prgn","dir":"Articles","previous_headings":"Diverging data","what":"PRGn","title":"Paul Tol's Color Schemes","text":"","code":"PRGn <- color(\"PRGn\") plot_scheme(PRGn(9), colours = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"sequential-data","dir":"Articles","previous_headings":"","what":"Sequential data","title":"Paul Tol's Color Schemes","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"ylorbr","dir":"Articles","previous_headings":"Sequential data","what":"YlOrBr","title":"Paul Tol's Color Schemes","text":"","code":"YlOrBr <- color(\"YlOrBr\") plot_scheme(YlOrBr(9), colours = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"iridescent","dir":"Articles","previous_headings":"Sequential data","what":"iridescent","title":"Paul Tol's Color Schemes","text":"","code":"iridescent <- color(\"iridescent\") plot_scheme(iridescent(23), colours = TRUE, size = 0.5)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"rainbow","dir":"Articles","previous_headings":"Sequential data","what":"rainbow","title":"Paul Tol's Color Schemes","text":"general rule, ordered data represented using rainbow scheme. three main arguments use (Tol 2021): spectral order visible light carries inherent magnitude message. bands almost constant hue sharp transitions , can perceived jumps data. Color-blind people difficulty distinguishing colors rainbow. use avoided, Paul Tol\u2019s technical note provides two color schemes reasonably clear color-blind vision. remain color-blind safe, two schemes must comply following conditions: discrete rainbow scheme must interpolated. smooth rainbow scheme used full range (Tol 2021 suggests starting purple).  using smooth rainbow scheme: Start -white instead purple lowest data value occurs often; End red instead brown highest data value occurs often.","code":"discrete_rainbow <- color(\"discrete rainbow\") plot_scheme(discrete_rainbow(14), colours = TRUE, size = 0.7) smooth_rainbow <- color(\"smooth rainbow\")  ## Start at purple instead of off-white plot(smooth_rainbow(256, range = c(0.25, 1))) ## End at red instead of brown plot(smooth_rainbow(256, range = c(0, 0.9)))","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"qualitative-color-schemes","dir":"Articles","previous_headings":"Diagnostic maps","what":"Qualitative color schemes","title":"Paul Tol's Color Schemes","text":"Diagnostic maps bright, vibrant, muted light (top bottom) qualitative color schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"diverging-schemes","dir":"Articles","previous_headings":"Diagnostic maps","what":"Diverging schemes","title":"Paul Tol's Color Schemes","text":"Diagnostic maps sunset, BuRd PRGn (top bottom) diverging color schemes. Diagnostic maps sunset, BuRd PRGn diverging color schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"sequential-schemes","dir":"Articles","previous_headings":"Diagnostic maps","what":"Sequential schemes","title":"Paul Tol's Color Schemes","text":"Diagnostic maps YlOrBr, iridescent, discrete rainbow smooth rainbow (top bottom) sequential color schemes. Diagnostic maps YlOrBr, iridescent smooth rainbow sequential color schemes.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Colour Schemes for Scientific Data Visualization","text":"Color blindness affects large number individuals. communicating scientific results color palettes must therefore carefully chosen accessible readers. R package provides implementation Okabe Ito (2008), Tol (2021) Crameri (2018) color schemes. schemes ready type data (qualitative, diverging sequential), colors distinct people, including color-blind readers. package also provides tools simulate color-blindness test well colors palette identifiable. simulate color-blindness production-ready R figures may also interested colorblindr package. Tol (2021) Crameri (2018) offer carefully chosen schemes, ready type data, colors : Distinct people, including color-blind readers, Distinct black white, Distinct screen paper, Matching well together, Citable reproducible. See vignette(\"tol\") vignette(\"crameri\") complete overview. specific uses, several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) implemented, color schemes may color-blind safe. color schemes implemented use base R graphics ggplot2 ggraph.","code":"To cite khroma in publications use:    Frerebeau N (2023). _khroma: Colour Schemes for Scientific Data   Visualization_. Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.1472077 <https://doi.org/10.5281/zenodo.1472077>,   R package version 1.11.0, <https://packages.tesselle.org/khroma/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{khroma: Colour Schemes for Scientific Data Visualization}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.11.0},     doi = {10.5281/zenodo.1472077},     url = {https://packages.tesselle.org/khroma/},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Colour Schemes for Scientific Data Visualization","text":"can install released version khroma CRAN: development version GitHub :","code":"install.packages(\"khroma\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/khroma\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Colour Schemes for Scientific Data Visualization","text":"","code":"## Install extra packages (if needed) # install.packages(\"ggplot2\"))  ## Load packages library(khroma) ## Get a table of available palettes info() #>            palette        type max missing #> 1             broc   diverging 256    <NA> #> 2             cork   diverging 256    <NA> #> 3              vik   diverging 256    <NA> #> 4           lisbon   diverging 256    <NA> #> 5           tofino   diverging 256    <NA> #> 6           berlin   diverging 256    <NA> #> 7             roma   diverging 256    <NA> #> 8              bam   diverging 256    <NA> #> 9           vanimo   diverging 256    <NA> #> 10          oleron   diverging 256    <NA> #> 11          bukavu   diverging 256    <NA> #> 12             fes   diverging 256    <NA> #> 13           devon  sequential 256    <NA> #> 14         lajolla  sequential 256    <NA> #> 15          bamako  sequential 256    <NA> #> 16           davos  sequential 256    <NA> #> 17          bilbao  sequential 256    <NA> #> 18            nuuk  sequential 256    <NA> #> 19            oslo  sequential 256    <NA> #> 20           grayC  sequential 256    <NA> #> 21          hawaii  sequential 256    <NA> #> 22           lapaz  sequential 256    <NA> #> 23           tokyo  sequential 256    <NA> #> 24            buda  sequential 256    <NA> #> 25           acton  sequential 256    <NA> #> 26           turku  sequential 256    <NA> #> 27           imola  sequential 256    <NA> #> 28          batlow  sequential 256    <NA> #> 29         batlowW  sequential 256    <NA> #> 30         batlowK  sequential 256    <NA> #> 31           brocO  sequential 256    <NA> #> 32           corkO  sequential 256    <NA> #> 33            vikO  sequential 256    <NA> #> 34           romaO  sequential 256    <NA> #> 35            bamO  sequential 256    <NA> #> 36          bright qualitative   7    <NA> #> 37    highcontrast qualitative   3    <NA> #> 38         vibrant qualitative   7    <NA> #> 39           muted qualitative   9 #DDDDDD #> 40  mediumcontrast qualitative   6    <NA> #> 41            pale qualitative   6    <NA> #> 42            dark qualitative   6    <NA> #> 43           light qualitative   9    <NA> #> 44          sunset   diverging  11 #FFFFFF #> 45            BuRd   diverging   9 #FFEE99 #> 46            PRGn   diverging   9 #FFEE99 #> 47          YlOrBr  sequential   9 #888888 #> 48      iridescent  sequential  23 #999999 #> 49 discreterainbow  sequential  23 #777777 #> 50   smoothrainbow  sequential  34 #666666 #> 51        okabeito qualitative   8    <NA> #> 52   okabeitoblack qualitative   8    <NA> #> 53    stratigraphy qualitative 175    <NA> #> 54            soil qualitative  24    <NA> #> 55            land qualitative  14    <NA>","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"color-palettes-and-scales","dir":"","previous_headings":"Usage","what":"Color palettes and scales","title":"Colour Schemes for Scientific Data Visualization","text":"color() returns palette function called single integer argument returns vector colors.","code":"## Paul Tol's bright color scheme bright <- color(\"bright\") bright(7) #>      blue       red     green    yellow      cyan    purple      grey  #> \"#4477AA\" \"#EE6677\" \"#228833\" \"#CCBB44\" \"#66CCEE\" \"#AA3377\" \"#BBBBBB\"  #> attr(,\"missing\") #> [1] NA ## Show the color palette plot_scheme(bright(7), colours = TRUE) data(mpg, package = \"ggplot2\")  ## Use with graphics par(mar = c(5, 4, 1, 1) + 0.1) plot(   x = mpg$displ,   y = mpg$hwy,   pch = 16,   col = color(\"bright\")(7)[as.factor(mpg$class)],   xlab = \"displ\",   ylab = \"hwy\",   panel.first = grid() ) ## Use with ggplot2 ggplot2::ggplot(data = mpg) +   ggplot2::aes(x = displ, y = hwy, color = class) +   ggplot2::geom_point() +   ggplot2::theme_bw() +   scale_color_bright()","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"test-how-well-the-colors-are-identifiable","dir":"","previous_headings":"Usage > Diagnostic tools","what":"Test how well the colors are identifiable","title":"Colour Schemes for Scientific Data Visualization","text":"","code":"## Okabe & Ito's color scheme okabe <- color(\"okabe ito\")  set.seed(12345) plot_map(okabe(8)) ## BuRd sequential color scheme BuRd <- color(\"BuRd\")  plot_tiles(BuRd(128), n = 256)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"simulate-color-blindness","dir":"","previous_headings":"Usage > Diagnostic tools","what":"Simulate color-blindness","title":"Colour Schemes for Scientific Data Visualization","text":"","code":"plot_scheme_colorblind(okabe(8)) ## ggplot2 default color scheme ## (equally spaced hues around the color wheel) x <- scales::hue_pal()(8) plot_scheme_colorblind(x)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Colour Schemes for Scientific Data Visualization","text":"Please note khroma project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":[],"package":"khroma"},{"path":[],"package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":null,"dir":"Reference","previous_headings":"","what":"Color Palette \u2014 colour","title":"Color Palette \u2014 colour","text":"Provides qualitative, diverging sequential color schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color Palette \u2014 colour","text":"","code":"colour(palette, reverse = FALSE, names = TRUE, lang = \"en\", force = FALSE, ...)  color(palette, reverse = FALSE, names = TRUE, lang = \"en\", force = FALSE, ...)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color Palette \u2014 colour","text":"palette character string giving name palette used (see ). reverse logical scalar: resulting vector colors reversed? names logical scalar: names colors kept resulting vector? lang character string specifying language color names. must one \"en\" (English, default) \"fr\" (French). force logical scalar. TRUE, forces color scheme interpolated. used routinely qualitative color schemes, designed used remain color-blind safe. ... arguments passed colorRampPalette.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Color Palette \u2014 colour","text":"palette function following attributes, called single integer argument (number levels) returns (named) vector colors. palette character string giving name color scheme. type character string giving corresponding data type. One \"qualitative\", \"diverging\" \"sequential\". interpolate logical scalar: can color palette interpolated? missing character string giving hexadecimal representation color used NA values. max integer giving maximum number color values. relevant non-interpolated color schemes. color schemes can interpolated (diverging sequential data), color range can limited additional argument. range allows remove fraction color domain (interpolated; see examples).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"paul-tol-s-color-schemes","dir":"Reference","previous_headings":"","what":"Paul Tol's Color Schemes","title":"Color Palette \u2014 colour","text":"following palettes available. maximum number supported colors brackets, value relevant qualitative color schemes (divergent sequential schemes linearly interpolated). Qualitative data bright (7), high contrast (3), vibrant (7), muted (9), medium contrast (6), pale (6), dark (6), light (9). Diverging data sunset (11), BuRd (9), PRGn (9). Sequential data YlOrBr (9), iridescent (23), discrete rainbow (23), smooth rainbow (34).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Color Palette \u2014 colour","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"rainbow-color-scheme","dir":"Reference","previous_headings":"","what":"Rainbow Color Scheme","title":"Color Palette \u2014 colour","text":"general rule, ordered data represented using rainbow scheme. three main arguments use (Tol 2018): spectral order visible light carries inherent magnitude message. bands almost constant hue sharp transitions , can perceived jumps data. Color-blind people difficulty distinguishing colors rainbow. use avoided, Paul Tol's technical note provides two color schemes reasonably clear color-blind vision. remain color-blind safe, two schemes must comply following conditions: discreterainbow scheme must interpolated. smoothrainbow scheme used full range.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"okabe-and-ito-color-scheme","dir":"Reference","previous_headings":"","what":"Okabe and Ito Color Scheme","title":"Color Palette \u2014 colour","text":"following (qualitative) color scheme available: okabeito 8 colors. okabeito black 8 colors, black last.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"scientific-color-schemes","dir":"Reference","previous_headings":"","what":"Scientific Color Schemes","title":"Color Palette \u2014 colour","text":"following (qualitative) color schemes available: stratigraphy International Chronostratigraphic Chart (175 colors). land AVHRR Global Land Cover Classification (14 colors). soil FAO Reference Soil Groups (24 colors).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Color Palette \u2014 colour","text":"Jones, ., Montanarella, L. & Jones, R. (Ed.) (2005). Soil atlas Europe. Luxembourg: European Commission, Office Official Publications European Communities. 128 pp. ISBN: 92-894-8120-X. Okabe, M. & Ito, K. (2008). Color Universal Design (CUD): Make Figures Presentations Friendly Colorblind People. URL: https://jfly.uni-koeln.de/color/. Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf Commission Geological Map World","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Color Palette \u2014 colour","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color Palette \u2014 colour","text":"","code":"## Okabe and Ito colour scheme colour(\"okabe ito\")(8) #>          black         orange       sky blue   bluish green         yellow  #>      \"#000000\"      \"#E69F00\"      \"#56B4E9\"      \"#009E73\"      \"#F0E442\"  #>           blue      vermilion reddish purple  #>      \"#0072B2\"      \"#D55E00\"      \"#CC79A7\"  #> attr(,\"missing\") #> [1] NA plot_scheme(colour(\"okabe ito\")(8))   ## Paul Tol's colour schemes ### Qualitative data plot_scheme(colour(\"bright\")(7))  plot_scheme(colour(\"high contrast\")(3))  plot_scheme(colour(\"vibrant\")(7))  plot_scheme(colour(\"muted\")(9))  plot_scheme(colour(\"medium contrast\")(6))  plot_scheme(colour(\"pale\")(6))  plot_scheme(colour(\"dark\")(6))  plot_scheme(colour(\"light\")(9))  ### Diverging data plot_scheme(colour(\"sunset\")(11))  plot_scheme(colour(\"BuRd\")(9))  plot_scheme(colour(\"PRGn\")(9))  ### Sequential data plot_scheme(colour(\"YlOrBr\")(9))  plot_scheme(colour(\"iridescent\")(23))  plot_scheme(colour(\"discrete rainbow\")(14))  plot_scheme(colour(\"discrete rainbow\")(23))  plot_scheme(colour(\"smooth rainbow\")(34))   ## Scientific colour schemes ### Geologic timescale plot_scheme(colour(\"stratigraphy\")(175))  ### AVHRR global land cover classification plot_scheme(colour(\"land\")(14))  ### FAO soil reference groups plot_scheme(colour(\"soil\")(24))   ## Adjust colour levels PRGn <- colour(\"PRGn\") plot_scheme(PRGn(9, range = c(0.5, 1)))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":null,"dir":"Reference","previous_headings":"","what":"Color Difference \u2014 compare","title":"Color Difference \u2014 compare","text":"Computes CIELAB distance metric.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color Difference \u2014 compare","text":"","code":"compare(x, metric = 2000, diag = FALSE, upper = FALSE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color Difference \u2014 compare","text":"x character vector colors. metric integer value giving year metric recommended CIE. must one \"1976\", \"1994\", \"2000\" (default; see spacesXYZ::DeltaE()). diag logical scalar: diagonal distance matrix printed? upper logical scalar: upper triangle distance matrix printed?","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Color Difference \u2014 compare","text":"distance matrix.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Color Difference \u2014 compare","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color Difference \u2014 compare","text":"","code":"# Trichromat pal <- colour(\"bright\")  (deltaE <- compare(pal(5))) #>            blue      red    green   yellow #> red    42.96901                            #> green  45.17203 68.91205                   #> yellow 58.32662 49.74802 31.70794          #> cyan   27.21922 60.08809 45.20276 45.65689 summary(deltaE) #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>   27.22   43.52   45.43   47.50   56.18   68.91   # Deuteranopia deu <- convert(pal, mode = \"deuteranopia\") compare(deu(5)) #>            blue      red    green   yellow #> red    48.65131                            #> green  47.54984 18.09392                   #> yellow 62.21275 13.46961 26.56583          #> cyan   24.04176 43.39140 51.12708 54.18900  # Protanopia pro <- convert(pal, mode = \"protanopia\") compare(pro(5)) #>            blue      red    green   yellow #> red    33.88826                            #> green  48.47198 14.79836                   #> yellow 58.79437 24.03187 21.17733          #> cyan   24.67298 34.21991 49.39259 49.55829  # Tritanopia tri <- convert(pal, mode = \"tritanopia\") compare(tri(5)) #>             blue       red     green    yellow #> red    50.727249                               #> green   1.346637 49.444911                     #> yellow 43.626450 20.642711 42.597786           #> cyan   24.817186 56.361997 23.961375 38.777287  # Achromatopsia ach <- convert(pal, mode = \"achromatopsia\") compare(ach(5)) #>             blue       red     green    yellow #> red    15.133194                               #> green  38.752027 19.522094                     #> yellow 26.072281  9.633077 10.043244           #> cyan   17.098221 31.695821 56.285350 41.893242","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate Color-Blindness \u2014 convert","title":"Simulate Color-Blindness \u2014 convert","text":"Simulate Color-Blindness","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate Color-Blindness \u2014 convert","text":"","code":"convert(x, mode)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate Color-Blindness \u2014 convert","text":"x palette function called single integer argument (number levels) returns vector colors (see color()). mode character string giving colorblind vision used. must one \"deuteranopia\", \"protanopia\", \"tritanopia\" \"achromatopsia\". unambiguous substring can given.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate Color-Blindness \u2014 convert","text":"palette function returns vector anomalized colors. attributes initial palette function inherited, supplementary attribute \"mode\" giving corresponding color-blind vision.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulate Color-Blindness \u2014 convert","text":"Brettel, H., Vi\u00e9not, F. Mollon, J. D. (1997). Computerized Simulation Color Appearance Dichromats. Journal Optical Society America , 14(10), p. 2647-2655. doi:10.1364/JOSAA.14.002647 . Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf Vi\u00e9not, F., Brettel, H. Mollon, J. D. (1999). Digital Video Colourmaps Checking Legibility Displays Dichromats. Color Research & Application, 24(4), p. 243-52. doi:10.1002/(SICI)1520-6378(199908)24:4<243::AID-COL5>3.0.CO;2-3 .","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Simulate Color-Blindness \u2014 convert","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate Color-Blindness \u2014 convert","text":"","code":"# Trichromat pal <- colour(\"bright\") plot_scheme(pal(7))   # Deuteranopia deu <- convert(pal, mode = \"deuteranopia\") plot_scheme(deu(7))   # Protanopia pro <- convert(pal, mode = \"protanopia\") plot_scheme(pro(7))   # Tritanopia tri <- convert(pal, mode = \"tritanopia\") plot_scheme(tri(7))   # Achromatopsia ach <- convert(pal, mode = \"achromatopsia\") plot_scheme(ach(7))   ## Plot simulated color blindness plot_scheme_colorblind(pal(7))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/info.html","id":null,"dir":"Reference","previous_headings":"","what":"Available Palettes \u2014 info","title":"Available Palettes \u2014 info","text":"Returns information available palettes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Available Palettes \u2014 info","text":"","code":"info()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Available Palettes \u2014 info","text":"data.frame following columns: palette Names palette. type Types palettes: sequential, diverging qualitative. max Maximum number colors contained palette. relevant qualitative palettes. missing hexadecimal color value mapping missing values.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/info.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Available Palettes \u2014 info","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/info.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Available Palettes \u2014 info","text":"","code":"## Get a table of available palettes info() #>            palette        type max missing #> 1             broc   diverging 256    <NA> #> 2             cork   diverging 256    <NA> #> 3              vik   diverging 256    <NA> #> 4           lisbon   diverging 256    <NA> #> 5           tofino   diverging 256    <NA> #> 6           berlin   diverging 256    <NA> #> 7             roma   diverging 256    <NA> #> 8              bam   diverging 256    <NA> #> 9           vanimo   diverging 256    <NA> #> 10          oleron   diverging 256    <NA> #> 11          bukavu   diverging 256    <NA> #> 12             fes   diverging 256    <NA> #> 13           devon  sequential 256    <NA> #> 14         lajolla  sequential 256    <NA> #> 15          bamako  sequential 256    <NA> #> 16           davos  sequential 256    <NA> #> 17          bilbao  sequential 256    <NA> #> 18            nuuk  sequential 256    <NA> #> 19            oslo  sequential 256    <NA> #> 20           grayC  sequential 256    <NA> #> 21          hawaii  sequential 256    <NA> #> 22           lapaz  sequential 256    <NA> #> 23           tokyo  sequential 256    <NA> #> 24            buda  sequential 256    <NA> #> 25           acton  sequential 256    <NA> #> 26           turku  sequential 256    <NA> #> 27           imola  sequential 256    <NA> #> 28          batlow  sequential 256    <NA> #> 29         batlowW  sequential 256    <NA> #> 30         batlowK  sequential 256    <NA> #> 31           brocO  sequential 256    <NA> #> 32           corkO  sequential 256    <NA> #> 33            vikO  sequential 256    <NA> #> 34           romaO  sequential 256    <NA> #> 35            bamO  sequential 256    <NA> #> 36          bright qualitative   7    <NA> #> 37    highcontrast qualitative   3    <NA> #> 38         vibrant qualitative   7    <NA> #> 39           muted qualitative   9 #DDDDDD #> 40  mediumcontrast qualitative   6    <NA> #> 41            pale qualitative   6    <NA> #> 42            dark qualitative   6    <NA> #> 43           light qualitative   9    <NA> #> 44          sunset   diverging  11 #FFFFFF #> 45            BuRd   diverging   9 #FFEE99 #> 46            PRGn   diverging   9 #FFEE99 #> 47          YlOrBr  sequential   9 #888888 #> 48      iridescent  sequential  23 #999999 #> 49 discreterainbow  sequential  23 #777777 #> 50   smoothrainbow  sequential  34 #666666 #> 51        okabeito qualitative   8    <NA> #> 52   okabeitoblack qualitative   8    <NA> #> 53    stratigraphy qualitative 175    <NA> #> 54            soil qualitative  24    <NA> #> 55            land qualitative  14    <NA>","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/khroma-package.html","id":null,"dir":"Reference","previous_headings":"","what":"khroma: Colour Schemes for Scientific Data Visualization \u2014 khroma-package","title":"khroma: Colour Schemes for Scientific Data Visualization \u2014 khroma-package","text":"Color schemes ready type data (qualitative, diverging sequential), colors distinct people, including color-blind readers. package provides implementation Paul Tol (2018) Fabio Crameri (2018) doi:10.5194/gmd-11-2541-2018  color schemes use 'graphics' 'ggplot2'. provides tools simulate color-blindness test well colors palette identifiable. Several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) also implemented.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/khroma-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"khroma: Colour Schemes for Scientific Data Visualization \u2014 khroma-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot.color_scheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Color Scheme \u2014 plot.color_scheme","title":"Plot Color Scheme \u2014 plot.color_scheme","text":"Quickly displays color scheme returned color().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot.color_scheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Color Scheme \u2014 plot.color_scheme","text":"","code":"# S3 method for color_scheme plot(x, ...)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot.color_scheme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Color Scheme \u2014 plot.color_scheme","text":"x character vector colors. ... Currently used.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot.color_scheme.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Color Scheme \u2014 plot.color_scheme","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot.color_scheme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Color Scheme \u2014 plot.color_scheme","text":"","code":"plot(colour(\"bright\")(7))  plot(colour(\"smooth rainbow\")(256))   ## Plot colour schemes plot_scheme(colour(\"bright\")(7))  plot_scheme(colour(\"sunset\")(11))  plot_scheme(colour(\"YlOrBr\")(9))  plot_scheme(colour(\"discrete rainbow\")(14))   ## Plot diagnostic maps plot_map(colour(\"bright\")(7))  plot_map(colour(\"sunset\")(11))  plot_map(colour(\"YlOrBr\")(9))  plot_map(colour(\"discrete rainbow\")(14))   ## Plot diagnostic images plot_tiles(colour(\"discrete rainbow\")(14), n = 256)  plot_tiles(colour(\"discrete rainbow\")(23), n = 256)  plot_tiles(colour(\"smooth rainbow\")(256), n = 256)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic Map \u2014 plot_map","title":"Diagnostic Map \u2014 plot_map","text":"Produces diagnostic map given color scheme.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic Map \u2014 plot_map","text":"","code":"plot_map(x)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic Map \u2014 plot_map","text":"x character vector colors.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_map.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diagnostic Map \u2014 plot_map","text":"N. Frerebeau, V. Arel-Bundock","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic Map \u2014 plot_map","text":"","code":"plot(colour(\"bright\")(7))  plot(colour(\"smooth rainbow\")(256))   ## Plot colour schemes plot_scheme(colour(\"bright\")(7))  plot_scheme(colour(\"sunset\")(11))  plot_scheme(colour(\"YlOrBr\")(9))  plot_scheme(colour(\"discrete rainbow\")(14))   ## Plot diagnostic maps plot_map(colour(\"bright\")(7))  plot_map(colour(\"sunset\")(11))  plot_map(colour(\"YlOrBr\")(9))  plot_map(colour(\"discrete rainbow\")(14))   ## Plot diagnostic images plot_tiles(colour(\"discrete rainbow\")(14), n = 256)  plot_tiles(colour(\"discrete rainbow\")(23), n = 256)  plot_tiles(colour(\"smooth rainbow\")(256), n = 256)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Color Scheme \u2014 plot_scheme","title":"Plot Color Scheme \u2014 plot_scheme","text":"Shows colors plot.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Color Scheme \u2014 plot_scheme","text":"","code":"plot_scheme(x, colours = FALSE, names = FALSE, size = 1)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Color Scheme \u2014 plot_scheme","text":"x character vector colors. colours logical scalar: hexadecimal representation colors displayed? names logical scalar: name colors displayed? size numeric value giving amount plotting text magnified relative default. Works cex parameter graphics::par().","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Color Scheme \u2014 plot_scheme","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Color Scheme \u2014 plot_scheme","text":"","code":"plot(colour(\"bright\")(7))  plot(colour(\"smooth rainbow\")(256))   ## Plot colour schemes plot_scheme(colour(\"bright\")(7))  plot_scheme(colour(\"sunset\")(11))  plot_scheme(colour(\"YlOrBr\")(9))  plot_scheme(colour(\"discrete rainbow\")(14))   ## Plot diagnostic maps plot_map(colour(\"bright\")(7))  plot_map(colour(\"sunset\")(11))  plot_map(colour(\"YlOrBr\")(9))  plot_map(colour(\"discrete rainbow\")(14))   ## Plot diagnostic images plot_tiles(colour(\"discrete rainbow\")(14), n = 256)  plot_tiles(colour(\"discrete rainbow\")(23), n = 256)  plot_tiles(colour(\"smooth rainbow\")(256), n = 256)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme_colourblind.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","title":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","text":"Shows colors plot different types simulated color blindness.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme_colourblind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","text":"","code":"plot_scheme_colourblind(x)  plot_scheme_colorblind(x)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme_colourblind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","text":"x character vector colors.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme_colourblind.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","text":"N. Frerebeau, V. Arel-Bundock","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme_colourblind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","text":"","code":"# Trichromat pal <- colour(\"bright\") plot_scheme(pal(7))   # Deuteranopia deu <- convert(pal, mode = \"deuteranopia\") plot_scheme(deu(7))   # Protanopia pro <- convert(pal, mode = \"protanopia\") plot_scheme(pro(7))   # Tritanopia tri <- convert(pal, mode = \"tritanopia\") plot_scheme(tri(7))   # Achromatopsia ach <- convert(pal, mode = \"achromatopsia\") plot_scheme(ach(7))   ## Plot simulated color blindness plot_scheme_colorblind(pal(7))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_tiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic Map \u2014 plot_tiles","title":"Diagnostic Map \u2014 plot_tiles","text":"Produces diagnostic map given color scheme.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_tiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic Map \u2014 plot_tiles","text":"","code":"plot_tiles(x, n = 512)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_tiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic Map \u2014 plot_tiles","text":"x character vector colors. n integer specifying size grid (defaults \\(512\\)).","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_tiles.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diagnostic Map \u2014 plot_tiles","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_tiles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic Map \u2014 plot_tiles","text":"","code":"plot(colour(\"bright\")(7))  plot(colour(\"smooth rainbow\")(256))   ## Plot colour schemes plot_scheme(colour(\"bright\")(7))  plot_scheme(colour(\"sunset\")(11))  plot_scheme(colour(\"YlOrBr\")(9))  plot_scheme(colour(\"discrete rainbow\")(14))   ## Plot diagnostic maps plot_map(colour(\"bright\")(7))  plot_map(colour(\"sunset\")(11))  plot_map(colour(\"YlOrBr\")(9))  plot_map(colour(\"discrete rainbow\")(14))   ## Plot diagnostic images plot_tiles(colour(\"discrete rainbow\")(14), n = 256)  plot_tiles(colour(\"discrete rainbow\")(23), n = 256)  plot_tiles(colour(\"smooth rainbow\")(256), n = 256)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":null,"dir":"Reference","previous_headings":"","what":"Color Ramp \u2014 ramp","title":"Color Ramp \u2014 ramp","text":"Provides color scheme map numeric vector colors.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color Ramp \u2014 ramp","text":"","code":"ramp(palette, ...)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color Ramp \u2014 ramp","text":"palette character string giving name palette used (see color()). ... arguments passed color().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Color Ramp \u2014 ramp","text":"palette function argument vector values mapped colors. data midpoint can specified additional midpoint argument (see examples).","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Color Ramp \u2014 ramp","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color Ramp \u2014 ramp","text":"","code":"## Visualize a simple DEM model ## Distribution of elevation values elevation <- hist(volcano)   ## Where are breaks? elevation$breaks #>  [1]  90 100 110 120 130 140 150 160 170 180 190 200  ## Build palette function ramp_BuRd <- ramp(palette = \"BuRd\")  (col <- ramp_BuRd(elevation$breaks)) #>  [1] \"#2166AC\" \"#3986BC\" \"#66A9CF\" \"#9CCAE1\" \"#CAE1EE\" \"#E8F0F4\" \"#F9ECE4\" #>  [8] \"#FCD5C0\" \"#F5AE8D\" \"#E37E64\" \"#CC4C43\" \"#B2182B\" #> attr(,\"breaks\") #>  [1]  90 100 110 120 130 140 150 160 170 180 190 200 image(volcano, col = col) legend(\"topright\", legend = attr(col, \"breaks\"), fill = col)   ## Rescale to midpoint (col <- ramp_BuRd(elevation$breaks, midpoint = 160)) #>  [1] \"#2166AC\" \"#347FB9\" \"#4D99C6\" \"#7AB6D5\" \"#A3CDE2\" \"#C7DFED\" \"#E0ECF2\" #>  [8] \"#F7F7F7\" \"#FAE6DA\" \"#FBD2BC\" \"#F6B495\" \"#EB9072\" #> attr(,\"breaks\") #>  [1]  90 100 110 120 130 140 150 160 170 180 190 200 image(volcano, col = col) legend(\"topright\", legend = attr(col, \"breaks\"), fill = col)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"Fabio Crameri's acton Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"","code":"scale_colour_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"Fabio Crameri's bam Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"","code":"scale_colour_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"Fabio Crameri's bamO Cyclic Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"","code":"scale_colour_bamO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_bamO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_bamO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"Fabio Crameri's bamako Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"","code":"scale_colour_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"Fabio Crameri's batlow Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"","code":"scale_colour_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"Fabio Crameri's batlowK Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"","code":"scale_colour_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"Fabio Crameri's batlowW Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"","code":"scale_colour_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"Fabio Crameri's berlin Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"","code":"scale_colour_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"Fabio Crameri's bilbao Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"","code":"scale_colour_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"Fabio Crameri's broc Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"","code":"scale_colour_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"Fabio Crameri's brocO Cyclic Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"","code":"scale_colour_brocO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_brocO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_brocO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"Fabio Crameri's buda Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"","code":"scale_colour_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"Fabio Crameri's bukavu Multi-Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"","code":"scale_colour_bukavu(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_color_bukavu(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_fill_bukavu(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"","code":"data(volcano)  volcan <- data.frame(   x = rep(1:ncol(volcano), each = nrow(volcano)),   y = rep(1:nrow(volcano), times = ncol(volcano)),   z = as.numeric(volcano) )  ggplot2::ggplot(volcan, ggplot2::aes(x, y, fill = z)) +   ggplot2::geom_raster() +   scale_fill_oleron(midpoint = 125)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"Fabio Crameri's cork Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"","code":"scale_colour_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"Fabio Crameri's corkO Cyclic Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"","code":"scale_colour_corkO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_corkO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_corkO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"Provides cyclic color scales Fabio Crameri's Scientific colour.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works . discrete logical scalar: color scheme used discrete scale?","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"Available schemes: brocO corkO vikO romaO bamO","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"Fabio Crameri's davos Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"","code":"scale_colour_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"Fabio Crameri's devon Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"","code":"scale_colour_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"Provides diverging color scales Fabio Crameri's Scientific colour.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works . discrete logical scalar: color scheme used discrete scale?","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"Available schemes: broc cork vik lisbon tofino berlin roma bam vanimo","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"Fabio Crameri's fes Multi-Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"","code":"scale_colour_fes(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_color_fes(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_fill_fes(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"","code":"data(volcano)  volcan <- data.frame(   x = rep(1:ncol(volcano), each = nrow(volcano)),   y = rep(1:nrow(volcano), times = ncol(volcano)),   z = as.numeric(volcano) )  ggplot2::ggplot(volcan, ggplot2::aes(x, y, fill = z)) +   ggplot2::geom_raster() +   scale_fill_oleron(midpoint = 125)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"Fabio Crameri's grayC Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"","code":"scale_colour_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"Fabio Crameri's hawaii Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"","code":"scale_colour_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"Fabio Crameri's imola Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"","code":"scale_colour_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"Fabio Crameri's lajolla Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"","code":"scale_colour_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"Fabio Crameri's lapaz Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"","code":"scale_colour_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"Fabio Crameri's lisbon Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"","code":"scale_colour_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"Provides multi-sequential color scales Fabio Crameri's Scientific colour.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"Available schemes: oleron bukavu fes","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"","code":"data(volcano)  volcan <- data.frame(   x = rep(1:ncol(volcano), each = nrow(volcano)),   y = rep(1:nrow(volcano), times = ncol(volcano)),   z = as.numeric(volcano) )  ggplot2::ggplot(volcan, ggplot2::aes(x, y, fill = z)) +   ggplot2::geom_raster() +   scale_fill_oleron(midpoint = 125)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"Fabio Crameri's nuuk Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"","code":"scale_colour_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"Fabio Crameri's oleron Multi-Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"","code":"scale_colour_oleron(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_color_oleron(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_fill_oleron(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"","code":"data(volcano)  volcan <- data.frame(   x = rep(1:ncol(volcano), each = nrow(volcano)),   y = rep(1:nrow(volcano), times = ncol(volcano)),   z = as.numeric(volcano) )  ggplot2::ggplot(volcan, ggplot2::aes(x, y, fill = z)) +   ggplot2::geom_raster() +   scale_fill_oleron(midpoint = 125)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"Fabio Crameri's oslo Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"","code":"scale_colour_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"Fabio Crameri's roma Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"","code":"scale_colour_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"Fabio Crameri's romaO Cyclic Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"","code":"scale_colour_romaO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_romaO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_romaO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"Provides sequential color scales Fabio Crameri's Scientific colour.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works . discrete logical scalar: color scheme used discrete scale?","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"Available schemes: batlow batlowW batlowK devon lajolla bamako davos bilbao nuuk oslo grayC hawaii lapaz tokyo buda acton turku imola","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"Fabio Crameri's tofino Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"","code":"scale_colour_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"Fabio Crameri's tokyo Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"","code":"scale_colour_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"Fabio Crameri's turku Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"","code":"scale_colour_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"Fabio Crameri's vanimo Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"","code":"scale_colour_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"Fabio Crameri's vik Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"","code":"scale_colour_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"Fabio Crameri's vikO Cyclic Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"","code":"scale_colour_vikO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_vikO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_vikO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":null,"dir":"Reference","previous_headings":"","what":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"Provides AVHRR Global Land Cover classification modified Paul Tol (colorblind safe).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"","code":"scale_colour_land(..., lang = \"en\", aesthetics = \"colour\")  scale_color_land(..., lang = \"en\", aesthetics = \"colour\")  scale_fill_land(..., lang = \"en\", aesthetics = \"fill\")  scale_edge_colour_land(..., lang = \"en\")  scale_edge_color_land(..., lang = \"en\")  scale_edge_fill_land(..., lang = \"en\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"... Arguments passed ggplot2::discrete_scale(). lang character string specifying language color names (see details). must one \"en\" (english, default), \"fr\" (french) NULL. NULL, values matched based color names. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"Values matched based land classification names.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"","code":"library(ggplot2)  land <- data.frame(   name = c(     \"water\", \"evergreen needleleaf forest\", \"deciduous needleleaf forest\",     \"mixed forest\", \"evergreen broadleaf forest\", \"deciduous broadleaf forest\",     \"woodland\", \"wooded grassland\", \"grassland\", \"cropland\", \"closed shrubland\",     \"open shrubland\", \"bare ground\", \"urban and built\"   ) )  ggplot2::ggplot(land) +   ggplot2::geom_rect(ggplot2::aes(xmin = rep(0, 14), xmax = rep(1, 14),                                   ymin = 1:14, ymax = 1:14+1, fill = name)) +   ggplot2::scale_y_reverse() +   scale_fill_land(name = \"land\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"Provides color scale logical values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"","code":"scale_colour_logical(..., aesthetics = \"colour\")  scale_color_logical(..., aesthetics = \"colour\")  scale_fill_logical(..., aesthetics = \"fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"... Arguments passed ggplot2::discrete_scale(). aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"discrete scale.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"","code":"library(ggplot2)  logic <- data.frame(   x = rep(1:5, times = 5),   y = rep(1:5, each = 5),   values = sample(c(TRUE, FALSE), 25, TRUE) )  ggplot2::ggplot(logic, ggplot2::aes(x, y, fill = values)) +   ggplot2::geom_raster() +   scale_fill_logical()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":null,"dir":"Reference","previous_headings":"","what":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"Provides qualitative color scale Okabe Ito 2008.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"","code":"scale_colour_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"colour\" )  scale_color_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"colour\" )  scale_fill_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"fill\" )  scale_edge_colour_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"edge_colour\" )  scale_edge_color_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"edge_colour\" )  scale_edge_fill_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? black_position character string giving position black color. must one \"first\" \"last\". unambiguous substring can given. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"qualitative color scheme used given (interpolation): colors picked maximum number supported values (8).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"Okabe, M. & Ito, K. (2008). Color Universal Design (CUD): Make Figures Presentations Friendly Colorblind People. URL: https://jfly.uni-koeln.de/color/.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_okabeito()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_okabeito(black_position = \"last\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":null,"dir":"Reference","previous_headings":"","what":"Color Scale Builder \u2014 scale_picker","title":"Color Scale Builder \u2014 scale_picker","text":"Builds color scale ggplot2 ggraph.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color Scale Builder \u2014 scale_picker","text":"","code":"scale_colour_picker(..., palette = \"YlOrBr\")  scale_color_picker(..., palette = \"YlOrBr\")  scale_fill_picker(..., palette = \"YlOrBr\")  scale_edge_colour_picker(..., palette = \"YlOrBr\")  scale_edge_color_picker(..., palette = \"YlOrBr\")  scale_edge_fill_picker(..., palette = \"YlOrBr\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color Scale Builder \u2014 scale_picker","text":"... Extra parameters passed color scale function. palette character string giving name color scheme used (see info()).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Color Scale Builder \u2014 scale_picker","text":"discrete continuous scale.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Color Scale Builder \u2014 scale_picker","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color Scale Builder \u2014 scale_picker","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_picker(palette = \"okabeito\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":null,"dir":"Reference","previous_headings":"","what":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"Provides FAO Soil Reference Groups color scheme.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"","code":"scale_colour_soil(..., lang = \"en\", aesthetics = \"colour\")  scale_color_soil(..., lang = \"en\", aesthetics = \"colour\")  scale_fill_soil(..., lang = \"en\", aesthetics = \"fill\")  scale_edge_colour_soil(..., lang = \"en\")  scale_edge_color_soil(..., lang = \"en\")  scale_edge_fill_soil(..., lang = \"en\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"... Arguments passed ggplot2::discrete_scale(). lang character string specifying language color names (see details). must one \"en\" (english, default), \"fr\" (french) NULL. NULL, values matched based color names. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"Values matched based soil names.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"Jones, ., Montanarella, L. & Jones, R. (Ed.) (2005). Soil atlas Europe. Luxembourg: European Commission, Office Official Publications European Communities. 128 pp. ISBN: 92-894-8120-X.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"","code":"library(ggplot2)  soil <- data.frame(   name = c(     \"Acrisol\", \"Albeluvisol\", \"Andosol\", \"Anthrosol\", \"Arenosol\", \"Calcisol\",     \"Cambisol\", \"Chernozem\", \"Cryosol\", \"Fluvisol\", \"Kastanozem\", \"Gleysol\",     \"Gypsisol\", \"Histosol\", \"Leptosol\", \"Luvisol\", \"Phaeozem\", \"Planosol\",     \"Podzol\", \"Regosol\", \"Solonchak\", \"Solonetz\", \"Umbrisol\", \"Vertisol\"   ) )  ggplot2::ggplot(soil) +   ggplot2::geom_rect(ggplot2::aes(xmin = rep(0, 24), xmax = rep(1, 24),                                   ymin = 1:24, ymax = 1:24+1, fill = name)) +   ggplot2::scale_y_reverse() +   scale_fill_soil(name = \"Soil\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":null,"dir":"Reference","previous_headings":"","what":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"Provides geologic timescale color scheme.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"","code":"scale_colour_stratigraphy(..., lang = \"en\", aesthetics = \"colour\")  scale_color_stratigraphy(..., lang = \"en\", aesthetics = \"colour\")  scale_fill_stratigraphy(..., lang = \"en\", aesthetics = \"fill\")  scale_edge_colour_stratigraphy(..., lang = \"en\")  scale_edge_color_stratigraphy(..., lang = \"en\")  scale_edge_fill_stratigraphy(..., lang = \"en\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"... Arguments passed ggplot2::discrete_scale(). lang character string specifying language color names (see details). must one \"en\" (english, default), \"fr\" (french) NULL. NULL, values matched based color names. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"Values matched based geological unit names.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"Commission Geological Map World.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"","code":"library(ggplot2)  strati <- data.frame(   name = c(\"Phanerozoic\", \"Paleozoic\", \"Cambrian\", \"Ordovician\", \"Silurian\",            \"Devonian\", \"Carboniferous\", \"Mesozoic\", \"Triassic\", \"Cretaceous\",            \"Jurassic\", \"Cenozoic\", \"Paleogene\", \"Neogene\", \"Quaternary\"),   type = c(\"Eon\", \"Era\", \"Period\", \"Period\", \"Period\", \"Period\", \"Period\",            \"Era\", \"Period\", \"Period\", \"Period\", \"Era\", \"Period\", \"Period\",            \"Period\"),   start = c(541, 541, 541, 485, 444, 419, 359,             252, 252, 201, 145, 66, 66, 23, 2.6),   end = c(0, 252, 485, 444, 419, 359, 252,           66, 201, 145, 66, 2.6, 23, 2.6, 0) )  ## Keep chronological order in the legend strati$name <- factor(strati$name, levels = rev(unique(strati$name)),                       ordered = TRUE)  ## Workaround: use `limits = force` to remove unused values ggplot2::ggplot(strati) +   ggplot2::geom_rect(ggplot2::aes(xmin = rep(0, 15), xmax = rep(1, 15),                                   ymin = start, ymax = end, fill = name)) +   ggplot2::scale_y_reverse() +   ggplot2::facet_grid(. ~ type) +   scale_fill_stratigraphy(name = \"Stratigraphy\", limits = force)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"Paul Tol's BuRd Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"","code":"scale_colour_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_sunset(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_BuRd(midpoint = 9000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_PRGn(midpoint = 9000, range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"Paul Tol's PRGn Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"","code":"scale_colour_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_sunset(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_BuRd(midpoint = 9000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_PRGn(midpoint = 9000, range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"Paul Tol's YlOrBr Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"","code":"scale_colour_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ). Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_YlOrBr()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_iridescent(reverse = TRUE)   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_smoothrainbow(range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"Paul Tol's bright Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"","code":"scale_colour_bright(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_bright(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_bright(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_bright(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_bright(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_bright(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"Paul Tol's dark Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"","code":"scale_colour_dark(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_dark(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_dark(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_dark(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_dark(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_dark(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"Provides qualitative color scales Paul Tol's Colour Schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"","code":"scale_fill_muted(..., reverse = FALSE, aesthetics = \"fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"discrete scale.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"Paul Tol's discrete rainbow Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"","code":"scale_colour_discreterainbow(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_discreterainbow(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_discreterainbow(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_discreterainbow(   ...,   reverse = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_discreterainbow(   ...,   reverse = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_discreterainbow(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ). Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"rainbow-color-scheme","dir":"Reference","previous_headings":"","what":"Rainbow Color Scheme","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"general rule, ordered data represented using rainbow scheme. three main arguments use (Tol 2018): spectral order visible light carries inherent magnitude message. bands almost constant hue sharp transitions , can perceived jumps data. Color-blind people difficulty distinguishing colors rainbow. use avoided, Paul Tol's technical note provides two color schemes reasonably clear color-blind vision. remain color-blind safe, two schemes must comply following conditions: discreterainbow scheme must interpolated. smoothrainbow scheme used full range.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_YlOrBr()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_iridescent(reverse = TRUE)   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_smoothrainbow(range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"Provides diverging color scales Paul Tol's Colour Schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"continuous scale.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_sunset(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_BuRd(midpoint = 9000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_PRGn(midpoint = 9000, range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"Paul Tol's high contrast Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"","code":"scale_colour_highcontrast(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_highcontrast(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_highcontrast(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_highcontrast(   ...,   reverse = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_highcontrast(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_highcontrast(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"Paul Tol's iridescent Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"","code":"scale_colour_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ). Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_YlOrBr()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_iridescent(reverse = TRUE)   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_smoothrainbow(range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"Paul Tol's light Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"","code":"scale_colour_light(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_light(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_light(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_light(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_light(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_light(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"Paul Tol's medium contrast Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"","code":"scale_colour_mediumcontrast(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_mediumcontrast(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_mediumcontrast(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_mediumcontrast(   ...,   reverse = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_mediumcontrast(   ...,   reverse = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_mediumcontrast(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"Paul Tol's muted Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"","code":"scale_colour_muted(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_muted(..., reverse = FALSE, aesthetics = \"colour\")  scale_edge_colour_muted(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_muted(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_muted(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"Paul Tol's pale Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"","code":"scale_colour_pale(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_pale(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_pale(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_pale(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_pale(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_pale(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"Provides sequential color scales Paul Tol's Colour Schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works . discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ). Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"rainbow-color-scheme","dir":"Reference","previous_headings":"","what":"Rainbow Color Scheme","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"general rule, ordered data represented using rainbow scheme. three main arguments use (Tol 2018): spectral order visible light carries inherent magnitude message. bands almost constant hue sharp transitions , can perceived jumps data. Color-blind people difficulty distinguishing colors rainbow. use avoided, Paul Tol's technical note provides two color schemes reasonably clear color-blind vision. remain color-blind safe, two schemes must comply following conditions: discreterainbow scheme must interpolated. smoothrainbow scheme used full range.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_YlOrBr()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_iridescent(reverse = TRUE)   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_smoothrainbow(range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"Paul Tol's smooth rainbow Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"","code":"scale_colour_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ). Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"rainbow-color-scheme","dir":"Reference","previous_headings":"","what":"Rainbow Color Scheme","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"general rule, ordered data represented using rainbow scheme. three main arguments use (Tol 2018): spectral order visible light carries inherent magnitude message. bands almost constant hue sharp transitions , can perceived jumps data. Color-blind people difficulty distinguishing colors rainbow. use avoided, Paul Tol's technical note provides two color schemes reasonably clear color-blind vision. remain color-blind safe, two schemes must comply following conditions: discreterainbow scheme must interpolated. smoothrainbow scheme used full range.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_YlOrBr()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_iridescent(reverse = TRUE)   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_smoothrainbow(range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"Paul Tol's sunset Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"","code":"scale_colour_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_sunset(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_BuRd(midpoint = 9000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_PRGn(midpoint = 9000, range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"Paul Tol's vibrant Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"","code":"scale_colour_vibrant(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_vibrant(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_vibrant(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_vibrant(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_vibrant(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_vibrant(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Quantitative Chronology in Archaeology","text":"ananke provides functions radiocarbon calibration chronological analysis.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Quantitative Chronology in Archaeology","text":"can install released version ananke CRAN : development version GitHub :","code":"install.packages(\"ananke\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/ananke\")","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Quantitative Chronology in Archaeology","text":"","code":"## Load packages library(ananke) ## Data from Bosch et al. 2015 data(\"ksarakil\")  ## Calibrate multiple dates cal <- c14_calibrate(   ages = ksarakil$date,   errors = ksarakil$error,   names = ksarakil$code,   curves = \"marine13\",   reservoir_offsets = 53,   reservoir_errors = 43,   from = 50000, to = 0 )  ## Plot plot(cal) plot(cal, density = FALSE, level = 0.95, lwd = 5)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Quantitative Chronology in Archaeology","text":"Please note ananke project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":[],"package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedAges-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","title":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","text":"S4 class represent calibrated radiocarbon ages.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedAges-class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","text":"ages numeric vector giving BP ages calibrated. errors numeric vector giving standard deviation ages calibrated. curves character vector specifying calibration curves used.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedAges-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","text":"F14C logical scalar: status integer vector specifying calibration status. must one \"0\" (OK), \"1\" (calibration range) \"2\" (may extend calibration range).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedAges-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","text":"class inherits TimeSeries.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedAges-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedSPD-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Calibrated SPD \u2014 CalibratedSPD-class","title":"Calibrated SPD \u2014 CalibratedSPD-class","text":"S4 class represent summed probability distributions (SPD) radiocarbon dates.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedSPD-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calibrated SPD \u2014 CalibratedSPD-class","text":"class inherits TimeSeries.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedSPD-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calibrated SPD \u2014 CalibratedSPD-class","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":null,"dir":"Reference","previous_headings":"","what":"F14C \u2014 F14C","title":"F14C \u2014 F14C","text":"Converts F14C ratio 14C age.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"F14C \u2014 F14C","text":"","code":"BP14C_to_F14C(ages, errors, ...)  F14C_to_BP14C(ratios, errors, ...)  # S4 method for numeric,numeric BP14C_to_F14C(ages, errors)  # S4 method for numeric,numeric F14C_to_BP14C(ratios, errors)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"F14C \u2014 F14C","text":"ages numeric vector giving radiocarbon ages. errors numeric vector giving standard deviation ages/ratios. ... Currently used. ratios numeric vector giving F14C ratios.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"F14C \u2014 F14C","text":"two-column data.frame.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"F14C \u2014 F14C","text":"Bronk Ramsey, C. (2008). RADIOCARBON DATING: REVOLUTIONS UNDERSTANDING. Archaeometry, 50:249-275. doi:10.1111/j.1475-4754.2008.00394.x .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"F14C \u2014 F14C","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ProxyRecord-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Proxy Record \u2014 ProxyRecord-class","title":"Proxy Record \u2014 ProxyRecord-class","text":"S4 class store proxy records.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ProxyRecord-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Proxy Record \u2014 ProxyRecord-class","text":"class inherits TimeSeries.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ProxyRecord-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Proxy Record \u2014 ProxyRecord-class","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/RECE-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Radiocarbon Event Count Ensemble \u2014 RECE-class","title":"Radiocarbon Event Count Ensemble \u2014 RECE-class","text":"S4 class represent radiocarbon event count ensemble.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/RECE-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Radiocarbon Event Count Ensemble \u2014 RECE-class","text":"class inherits TimeSeries.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/RECE-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Radiocarbon Event Count Ensemble \u2014 RECE-class","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ananke-package.html","id":null,"dir":"Reference","previous_headings":"","what":"ananke: Quantitative Chronology in Archaeology \u2014 ananke-package","title":"ananke: Quantitative Chronology in Archaeology \u2014 ananke-package","text":"package (one paragraph).","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ananke-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"ananke: Quantitative Chronology in Archaeology \u2014 ananke-package","text":"ananke uses following options() configure behavior: ananke.calendar: TimeScale object (default calendar printing). ananke.grid: numeric value specifying number equally spaced points densities estimated (defaults \\(512\\)). power \\(2\\). ananke.progress: logical scalar. progress bars displayed? ananke.verbose: logical scalar. R report extra information progress? Defaults TRUE.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ananke-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"ananke: Quantitative Chronology in Archaeology \u2014 ananke-package","text":"Full list authors contributors (alphabetic order): Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":null,"dir":"Reference","previous_headings":"","what":"14C Calibration \u2014 c14_calibrate","title":"14C Calibration \u2014 c14_calibrate","text":"Calibrates radiocarbon dates.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"14C Calibration \u2014 c14_calibrate","text":"","code":"c14_calibrate(ages, errors, ...)  # S4 method for numeric,numeric c14_calibrate(   ages,   errors,   names = NULL,   curves = \"intcal20\",   reservoir_offsets = 0,   reservoir_errors = 0,   from = 55000,   to = 0,   resolution = 1,   normalize = TRUE,   F14C = FALSE,   drop = TRUE,   eps = 1e-06 )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"14C Calibration \u2014 c14_calibrate","text":"ages numeric vector giving BP ages calibrated. errors numeric vector giving standard deviation ages calibrated. ... Currently used. names character vector specifying names ages (e.g. laboratory codes). curves character vector specifying calibration curve used. Different curves can specified per dated sample. reservoir_offsets numeric vector giving offset values marine reservoir effect (defaults 0; .e. offset). reservoir_errors numeric vector giving offset value errors marine reservoir effect (defaults 0; .e. offset). length-one numeric vector specifying earliest data calibrate , cal. BP years. length-one numeric vector specifying latest data calibrate , cal. BP years. resolution length-one numeric vector specifying temporal resolution (years) calibration. normalize logical scalar: calibration normalized? F14C logical scalar: calibration carried F14C space? drop logical scalar: years zero probability discarded? TRUE (default), results narrower time range. eps length-one numeric value giving cutoff calibration values removed.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"14C Calibration \u2014 c14_calibrate","text":"CalibratedAges object.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"14C Calibration \u2014 c14_calibrate","text":"Adapted Bchron::BchronCalibrate() Andrew Parnell rcarbon::calibrate() Andrew Bevan Enrico Crema.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"14C Calibration \u2014 c14_calibrate","text":"Bronk Ramsey, C. (2008). RADIOCARBON DATING: REVOLUTIONS UNDERSTANDING. Archaeometry, 50:249-275. doi:10.1111/j.1475-4754.2008.00394.x .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"14C Calibration \u2014 c14_calibrate","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"14C Calibration \u2014 c14_calibrate","text":"","code":"## Calibrate a single date cal <- c14_calibrate(300, 20) plot(cal, panel.first = graphics::grid())   ## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") ) plot(cal, calendar = BP(), panel.first = graphics::grid())  plot(cal, interval = FALSE)  plot(cal[, 1, ], col.interval = \"red\")   plot(cal, density = FALSE, level = 0.68, lwd = 5)  plot(cal, density = FALSE, level = 0.95, lwd = 5)   # \\donttest{ ## Out of 14C range? out <- c14_calibrate(130, 20) #> Warning: Date \u201cX1\u201d may extent out of range. plot(out) #> Warning: Date \u201cX1\u201d may extent out of range.  # }","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":null,"dir":"Reference","previous_headings":"","what":"14C Calibration \u2014 c14_combine","title":"14C Calibration \u2014 c14_combine","text":"Combines radiocarbon dates.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"14C Calibration \u2014 c14_combine","text":"","code":"c14_combine(ages, errors, ...)  # S4 method for numeric,numeric c14_combine(ages, errors, groups = NULL)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"14C Calibration \u2014 c14_combine","text":"ages numeric vector giving BP ages calibrated. errors numeric vector giving standard deviation ages calibrated. ... Currently used. groups factor sense .factor(groups) defines groups combine . NULL (default), dates combined. NAs treated isolated dates.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"14C Calibration \u2014 c14_combine","text":"data.frame following columns:","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"14C Calibration \u2014 c14_combine","text":"Ward, G. K. Wilson, S. R. (1978). Procedures Comparing Combining Radiocarbon Age Determinations: Critique. Archaeometry 20(1): 19\u201131. doi:10.1111/j.1475-4754.1978.tb00208.x .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"14C Calibration \u2014 c14_combine","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"14C Calibration \u2014 c14_combine","text":"","code":"## Replicate Ward and Wilson (1978), p. 28 polach1972 <- data.frame(   samples = c(\"ANU-7\", \"ANU-7\", \"ANU-7\", \"W-1571\", \"ANU-5\",               \"C-800\", \"L-698D\", \"FSU-3\", \"Tx-44\"),   ages = c(14550, 15000, 13700, 14650, 11700, 10860, 11840, 11245, 10700),   errors = c(270, 600, 300, 500, 260, 410, 100, 450, 210) )  c14_combine(   ages = polach1972$ages,   errors = polach1972$errors,   groups = polach1972$samples ) #>   groups     ages  errors     chi2         p #> 1 W-1571 14650.00 500.000       NA        NA #> 2  ANU-5 11700.00 260.000       NA        NA #> 3  C-800 10860.00 410.000       NA        NA #> 4 L-698D 11840.00 100.000       NA        NA #> 5  FSU-3 11245.00 450.000       NA        NA #> 6  Tx-44 10700.00 210.000       NA        NA #> 7  ANU-7 14253.17 190.325 6.157902 0.1041756","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":null,"dir":"Reference","previous_headings":"","what":"14C Calibration Curve \u2014 c14_curve","title":"14C Calibration Curve \u2014 c14_curve","text":"14C Calibration Curve","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"14C Calibration Curve \u2014 c14_curve","text":"","code":"c14_curve(object, ...)  # S4 method for character c14_curve(object)  # S4 method for CalibratedAges c14_curve(object)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"14C Calibration Curve \u2014 c14_curve","text":"object character vector naming calibration curves (see details). ... Currently used.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"14C Calibration Curve \u2014 c14_curve","text":"list three-column data.frame: calibrated age BP, uncalibrated age BP standard deviation.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"14C Calibration Curve \u2014 c14_curve","text":"following calibration curves available:","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"14C Calibration Curve \u2014 c14_curve","text":"Heaton, Timothy J, Peter K\u00f6hler, Martin Butzin, Edouard Bard, Ron W Reimer, William E N Austin, Christopher Bronk Ramsey, et al. (2020). Marine20 Marine Radiocarbon Age Calibration Curve (0-55,000 Cal BP). Radiocarbon, 62(4): 779-820. doi:10.1017/RDC.2020.68 . Hogg, Alan G, Timothy J Heaton, Quan Hua, Jonathan G Palmer, Chris SM Turney, John Southon, Alex Bayliss, et al. (2020). SHCal20 Southern Hemisphere Calibration, 0-55,000 Years Cal BP. Radiocarbon, 62(4): 759-78. doi:10.1017/RDC.2020.59 . Hogg, Alan G, Quan Hua, Paul G Blackwell, Mu Niu, Caitlin E Buck, Thomas P Guilderson, Timothy J Heaton, et al. (2013). SHCal13 Southern Hemisphere Calibration, 0-50,000 Years Cal BP. Radiocarbon, 55(4): 1889-1903. doi:10.2458/azu_js_rc.55.16783 . Hua, Quan, Mike Barbetti (2004). Review Tropospheric Bomb 14C Data Carbon Cycle Modeling Age Calibration Purposes. Radiocarbon, 46(3): 1273-1298. doi:10.1017/S0033822200033142 . Hua, Quan, Mike Barbetti, Andrzej Z Rakowski (2013). Atmospheric Radiocarbon Period 1950-2010. Radiocarbon, 55(4): 2059\u20112072. doi:10.2458/azu_js_rc.v55i2.16177 . Hua, Quan, Jocelyn C Turnbull, Guaciara M Santos, Andrzej Z Rakowski, Santiago Ancapich\u00fan, Ricardo De Pol-Holz, Samuel Hammer, et al. (2022). Atmospheric Radiocarbon Period 1950-2019. Radiocarbon, 64(4): 723\u2011745. doi:10.1017/RDC.2021.95 . Hughen, K., S. Lehman, J. Southon, J. Overpeck, O. Marchal, C. Herring, J. Turnbull (2004). 14C Activity Global Carbon Cycle Changes Past 50,000 Years. Science, 303(5655): 202\u2011207. doi:10.1126/science.1090300 . Hughen, Konrad , Mike G L Baillie, Edouard Bard, J Warren Beck, Chanda J H Bertrand, Paul G Blackwell, Caitlin E Buck, et al. (2004). Marine04 Marine Radiocarbon Age Calibration, 0-26 cal kyr BP. Radiocarbon, 46(3): 1059\u20111086. doi:10.1017/S0033822200033002 . Kueppers, Lara M., John Southon, Paul Baer, John Harte (2004). Dead Wood Biomass Turnover Time, Measured Radiocarbon, along Subalpine Elevation Gradient. Oecologia, 141(4): 641\u2011651. doi:10.1007/s00442-004-1689-x . McCormac, F G, G Hogg, P G Blackwell, C E Buck, T F G Higham, P J Reimer (2004). Shcal04 Southern Hemisphere Calibration, 0-11.0 cal kyr BP. Radiocarbon, 46(3): 1087\u20111092. doi:10.1017/S0033822200033014 . Reimer, P J, M G L Baillie, E Bard, Bayliss, J W Beck, P G Blackwell, C Bronk Ramsey, et al. (2009). IntCal09 Marine09 Radiocarbon Age Calibration Curves, 0-50,000 Years cal BP. Radiocarbon, 51(4): 1111\u20111150. doi:10.1017/S0033822200034202 . Reimer, Paula J, William E N Austin, Edouard Bard, Alex Bayliss, Paul G Blackwell, Christopher Bronk Ramsey, Martin Butzin, et al. (2020). IntCal20 Northern Hemisphere Radiocarbon Age Calibration Curve (0-55 cal kBP). Radiocarbon, 62(4): 725\u2011757. doi:10.1017/RDC.2020.41 . Reimer, Paula J, Mike G L Baillie, Edouard Bard, Alex Bayliss, J Warren Beck, Chanda J H Bertrand, Paul G Blackwell, et al. (2004). Intcal04 Terrestrial Radiocarbon Age Calibration, 0-26 cal kyr BP. Radiocarbon, 46(3): 1029\u20111058. doi:10.1017/S0033822200032999 . Reimer, Paula J, Edouard Bard, Alex Bayliss, J Warren Beck, Paul G Blackwell, Christopher Bronk Ramsey, Caitlin E Buck, et al. (2013). IntCal13 Marine13 Radiocarbon Age Calibration Curves 0-50,000 Years cal BP. Radiocarbon, 55(4): 1869\u20111887. doi:10.2458/azu_js_rc.55.16947 . Stuiver, Minze, Paula J. Reimer, Edouard Bard, J. Warren Beck, G. S. Burr, Konrad . Hughen, Bernd Kromer, Gerry McCormac, Johannes Van Der Plicht, Marco Spurk (1998). INTCAL98 Radiocarbon Age Calibration, 24,000-0 cal BP. Radiocarbon, 40(3): 1041\u20111083. doi:10.1017/S0033822200019123 . Stuiver, Minze, Paula J. Reimer, Thomas F. Braziunas. (1998). High-Precision Radiocarbon Age Calibration Terrestrial Marine Samples. Radiocarbon, 40(3): 1127\u20111151. doi:10.1017/S0033822200019172 .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"14C Calibration Curve \u2014 c14_curve","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"14C Calibration Curve \u2014 c14_curve","text":"","code":"## IntCal20 intcal20 <- c14_curve(\"intcal20\") head(intcal20[[1]]) #>   CALBP   AGE ERROR #> 1 55000 50100  1024 #> 2 54980 50081  1018 #> 3 54960 50063  1013 #> 4 54940 50043  1007 #> 5 54920 50027  1003 #> 6 54900 50009   997  ## IntCal intcal <- c14_curve(c(\"intcal09\", \"intcal13\", \"intcal20\")) lapply(X = intcal, FUN = head) #> $intcal09 #>   CALBP   AGE ERROR #> 1 50000 46400   415 #> 2 49900 46316   410 #> 3 49800 46229   403 #> 4 49700 46135   396 #> 5 49600 46036   389 #> 6 49500 45933   380 #>  #> $intcal13 #>   CALBP   AGE ERROR #> 1 50000 46401   274 #> 2 49980 46381   273 #> 3 49960 46362   271 #> 4 49940 46343   270 #> 5 49920 46324   269 #> 6 49900 46305   268 #>  #> $intcal20 #>   CALBP   AGE ERROR #> 1 55000 50100  1024 #> 2 54980 50081  1018 #> 3 54960 50063  1013 #> 4 54940 50043  1007 #> 5 54920 50027  1003 #> 6 54900 50009   997 #>","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":null,"dir":"Reference","previous_headings":"","what":"Radiocarbon Event Count \u2014 c14_ensemble","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"Radiocarbon Event Count","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"","code":"c14_ensemble(object, ...)  # S4 method for CalibratedAges c14_ensemble(   object,   from = NULL,   to = NULL,   by = 10,   n = 100,   calendar = BP(),   progress = getOption(\"ananke.progress\") )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"object CalibratedAges object. ... Currently used. length-one numeric vector specifying earliest data calibrate (cal BP years). length-one numeric vector specifying latest data calibrate (cal BP years). length-one numeric vector specifying temporal resolution (years) calibration. n integer specifying number item choose randomly. calendar TimeScale object specifying calendar (see calendar()). Defaults CE(). progress logical scalar: progress bar displayed?","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"RECE object.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"Carleton, W. C. (2021). Evaluating Bayesian Radiocarbon\u2010dated Event Count (REC) Models Study Long\u2010term Human Environmental Processes. Journal Quaternary Science, 36(1): 110\u201123. doi:10.1002/jqs.3256 .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"Plot Calibrated Radiocarbon Ages","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"","code":"# S4 method for CalibratedAges,missing plot(   x,   calendar = getOption(\"ananke.calendar\"),   density = TRUE,   interval = TRUE,   level = 0.954,   sort = TRUE,   decreasing = TRUE,   main = NULL,   sub = NULL,   axes = TRUE,   frame.plot = FALSE,   ann = graphics::par(\"ann\"),   panel.first = NULL,   panel.last = NULL,   col.density = \"grey\",   col.interval = \"#77AADD\",   ... )  # S4 method for CalibratedSPD,missing plot(   x,   calendar = getOption(\"ananke.calendar\"),   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = FALSE,   panel.first = NULL,   panel.last = NULL,   ... )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"x CalibratedAges CalibratedSPD object. calendar TimeScale object specifying target calendar (see calendar()). density logical scalar: density drawn? interval logical scalar: highest density region drawn? level length-one numeric vector giving confidence level. used interval TRUE. sort logical scalar: data sorted? decreasing logical scalar: sort order decreasing? used sort TRUE. main character string giving main title plot. sub character string giving subtitle plot. axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? ann logical scalar: default annotation (title x y labels) appear plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. col.density, col.interval specification plotting colors. ... graphical parameters may also passed arguments function.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"plot() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"","code":"## Calibrate a single date cal <- c14_calibrate(300, 20) plot(cal, panel.first = graphics::grid())   ## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") ) plot(cal, calendar = BP(), panel.first = graphics::grid())  plot(cal, interval = FALSE)  plot(cal[, 1, ], col.interval = \"red\")   plot(cal, density = FALSE, level = 0.68, lwd = 5)  plot(cal, density = FALSE, level = 0.95, lwd = 5)   # \\donttest{ ## Out of 14C range? out <- c14_calibrate(130, 20) #> Warning: Date \u201cX1\u201d may extent out of range. plot(out) #> Warning: Date \u201cX1\u201d may extent out of range.  # }","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_spd.html","id":null,"dir":"Reference","previous_headings":"","what":"Summed Probability Distributions \u2014 c14_spd","title":"Summed Probability Distributions \u2014 c14_spd","text":"Computes summed probability distributions (SPD) radiocarbon dates.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_spd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summed Probability Distributions \u2014 c14_spd","text":"","code":"c14_spd(object, ...)  # S4 method for CalibratedAges c14_spd(object, normalize_date = FALSE, normalize_spd = FALSE)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_spd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summed Probability Distributions \u2014 c14_spd","text":"object CalibratedAges object. ... Currently used. normalize_date logical scalar: total probability mass calibrated dates normalised (sum unity within time-span analysis)? normalize_spd logical scalar: total probability mass SPD normalised (sum unity)?","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_spd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summed Probability Distributions \u2014 c14_spd","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_spd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summed Probability Distributions \u2014 c14_spd","text":"","code":"## Radiocarbon data from Bosch et al. 2015 data(\"ksarakil\")  ## Calibrate cal <- c14_calibrate(   ages = ksarakil$date,   errors = ksarakil$error,   names = ksarakil$code,   curves = \"marine13\",   reservoir_offsets = 53,   reservoir_errors = 43,   from = 50000, to = 0 ) plot(cal, level = 0.68, flip = TRUE) #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter   ## SPD s <- c14_spd(cal) plot(s)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_uncalibrate.html","id":null,"dir":"Reference","previous_headings":"","what":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","title":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","text":"Uncalibrate Radiocarbon Date","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_uncalibrate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","text":"","code":"c14_uncalibrate(object, ...)  # S4 method for numeric c14_uncalibrate(object, curves = \"intcal20\")  # S4 method for CalibratedAges c14_uncalibrate(object, ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_uncalibrate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","text":"object CalibratedAges object numeric vector calibrated ages (BP). ... Currently used. curves description","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_uncalibrate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_uncalibrate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","text":"","code":"if (FALSE) { ## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  ## Uncalibrate c14_uncalibrate(cal) }","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Data Frame \u2014 data.frame","title":"Coerce to a Data Frame \u2014 data.frame","text":"Coerce Data Frame","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Data Frame \u2014 data.frame","text":"","code":"# S4 method for CalibratedAges as.data.frame(x, ..., calendar = getOption(\"ananke.calendar\"))  # S4 method for RECE as.data.frame(x, ..., calendar = getOption(\"ananke.calendar\"))  # S4 method for ProxyRecord as.data.frame(x, ..., calendar = getOption(\"ananke.calendar\"))","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Data Frame \u2014 data.frame","text":"x object. ... parameters passed data.frame(). calendar TimeScale object specifying target calendar (see calendar()). NULL, rata die returned.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Data Frame \u2014 data.frame","text":"data.frame extra time column.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Data Frame \u2014 data.frame","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to a Data Frame \u2014 data.frame","text":"","code":"## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  head(as.data.frame(cal)) #>    time            X Y #> 1 -3984 1.110191e-06 0 #> 2 -3983 1.434164e-06 0 #> 3 -3982 1.845486e-06 0 #> 4 -3981 2.365558e-06 0 #> 5 -3980 3.020421e-06 0 #> 6 -3979 3.589420e-06 0 head(as.data.frame(cal, calendar = BP())) #>   time            X Y #> 1 5934 1.110191e-06 0 #> 2 5933 1.434164e-06 0 #> 3 5932 1.845486e-06 0 #> 4 5931 2.365558e-06 0 #> 5 5930 3.020421e-06 0 #> 6 5929 3.589420e-06 0 head(as.data.frame(cal, calendar = NULL)) #>       time            X Y #> 1 -1455491 1.110191e-06 0 #> 2 -1455125 1.434164e-06 0 #> 3 -1454760 1.845486e-06 0 #> 4 -1454395 2.365558e-06 0 #> 5 -1454030 3.020421e-06 0 #> 6 -1453664 3.589420e-06 0","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":null,"dir":"Reference","previous_headings":"","what":"Highest Density Regions \u2014 hdr","title":"Highest Density Regions \u2014 hdr","text":"Highest Density Regions","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Highest Density Regions \u2014 hdr","text":"","code":"# S4 method for CalibratedAges,missing interval_hdr(x, level = 0.954, calendar = getOption(\"ananke.calendar\"), ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Highest Density Regions \u2014 hdr","text":"x CalibratedAges object. level length-one numeric vector giving confidence level. calendar TimeScale object specifying target calendar (see calendar()). ... Currently used.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Highest Density Regions \u2014 hdr","text":"Returns list numeric  matrix.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Highest Density Regions \u2014 hdr","text":"Hyndman, R. J. (1996). Computing graphing highest density regions. American Statistician, 50: 120-126. doi:10.2307/2684423 .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Highest Density Regions \u2014 hdr","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Highest Density Regions \u2014 hdr","text":"","code":"## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  ## HDR interval_hdr(cal, level = 0.683) #> $X #>      start   end    p #> [1,] -3911 -3876 0.15 #> [2,] -3802 -3706 0.50 #> [3,] -3669 -3659 0.04 #>  #> $Y #>      start   end    p #> [1,] -3336 -3310 0.11 #> [2,] -3297 -3283 0.06 #> [3,] -3272 -3267 0.02 #> [4,] -3240 -3207 0.14 #> [5,] -3194 -3103 0.37 #>  interval_hdr(cal, level = 0.954) #> $X #>      start   end    p #> [1,] -3944 -3855 0.29 #> [2,] -3844 -3833 0.02 #> [3,] -3818 -3653 0.65 #>  #> $Y #>      start   end    p #> [1,] -3355 -3091 0.94 #> [2,] -3050 -3040 0.01 #>  interval_hdr(cal, level = 0.997) #> $X #>      start   end p #> [1,] -3953 -3647 1 #>  #> $Y #>      start   end p #> [1,] -3366 -3014 1 #>","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ksarakil.html","id":null,"dir":"Reference","previous_headings":"","what":"Ks\u00e2r 'Akil Radiocarbon Dates \u2014 ksarakil","title":"Ks\u00e2r 'Akil Radiocarbon Dates \u2014 ksarakil","text":"Ks\u00e2r 'Akil Radiocarbon Dates","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ksarakil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ks\u00e2r 'Akil Radiocarbon Dates \u2014 ksarakil","text":"","code":"ksarakil","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ksarakil.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Ks\u00e2r 'Akil Radiocarbon Dates \u2014 ksarakil","text":"data.frame 16 rows 5 variables: code Laboratory code. date Radiocarbon date (year BP). error Radiocarbon error (year). layer Stratigraphic layer. phase Chronological phase.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ksarakil.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Ks\u00e2r 'Akil Radiocarbon Dates \u2014 ksarakil","text":"Bosch, M. D., Mannino, M. ., Prendergast, . L., O'Connell, T. C., Demarchi, B., Taylor, S. M., Niven, L., van der Plicht, J. Hublin, J.-J. (2015). New Chronology Ks\u00e2r 'Akil (Lebanon) Supports Levantine Route Modern Human Dispersal Europe. Proceedings National Academy Sciences 112(25): 7683-8. doi:10.1073/pnas.1501529112 .","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Find Labels from Object \u2014 labels","title":"Find Labels from Object \u2014 labels","text":"Find suitable set labels object use printing plotting, example.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find Labels from Object \u2014 labels","text":"","code":"# S4 method for CalibratedAges labels(object, ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find Labels from Object \u2014 labels","text":"object object find labels. ... Currently used.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find Labels from Object \u2014 labels","text":"character vector.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/labels.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Find Labels from Object \u2014 labels","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean \u2014 mean","title":"Mean \u2014 mean","text":"Mean","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean \u2014 mean","text":"","code":"# S4 method for CalibratedAges mean(x, na.rm = FALSE, ..., calendar = getOption(\"ananke.calendar\"))  # S4 method for ProxyRecord mean(x, na.rm = FALSE, ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean \u2014 mean","text":"x CalibratedAges object. na.rm logical scalar: NA values stripped computation proceeds? ... Currently used. calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean \u2014 mean","text":"numeric vector.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean \u2014 mean","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean \u2014 mean","text":"","code":"## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  ## Statistics quantile(cal) #>      0%   25%   50%   75%  100% #> X -3984 -3876 -3778 -3729 -3545 #> Y -3984 -3283 -3211 -3145 -2917 median(cal) #>     X     Y  #> -3778 -3211  mean(cal) #>         X         Y  #> -3792.679 -3211.160   ## Plot plot(cal, calendar = CE())  ## Need to set 'calendar' abline(v = median(cal, calendar = CE()), lty = 2, col = \"blue\") abline(v = mean(cal, calendar = CE()), lty = 2, col = \"red\")","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":null,"dir":"Reference","previous_headings":"","what":"Median \u2014 median","title":"Median \u2014 median","text":"Median","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Median \u2014 median","text":"","code":"# S4 method for CalibratedAges median(x, na.rm = FALSE, ..., calendar = getOption(\"ananke.calendar\"))","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Median \u2014 median","text":"x CalibratedAges object. na.rm logical scalar: NA values stripped computation proceeds? ... Currently used. calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Median \u2014 median","text":"numeric vector.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Median \u2014 median","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Median \u2014 median","text":"","code":"## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  ## Statistics quantile(cal) #>      0%   25%   50%   75%  100% #> X -3984 -3876 -3778 -3729 -3545 #> Y -3984 -3283 -3211 -3145 -2917 median(cal) #>     X     Y  #> -3778 -3211  mean(cal) #>         X         Y  #> -3792.679 -3211.160   ## Plot plot(cal, calendar = CE())  ## Need to set 'calendar' abline(v = median(cal, calendar = CE()), lty = 2, col = \"blue\") abline(v = mean(cal, calendar = CE()), lty = 2, col = \"red\")","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mutators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or Set Parts of an Object \u2014 mutators","title":"Get or Set Parts of an Object \u2014 mutators","text":"Getters setters extract replace parts object.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mutators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or Set Parts of an Object \u2014 mutators","text":"x object get set element(s). value possible value element(s) x.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mutators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get or Set Parts of an Object \u2014 mutators","text":"object sort x new values assigned.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mutators.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get or Set Parts of an Object \u2014 mutators","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"Represents layer-counted proxy records sequences probability distributions absolute, error-free time axes.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"","code":"proxy_ensemble(depth, ...)  # S4 method for numeric proxy_ensemble(   depth,   proxy,   proxy_error,   step,   time,   time_error,   calendar,   from = NULL,   to = NULL,   by = NULL,   n = 30,   progress = getOption(\"ananke.progress\"),   verbose = getOption(\"ananke.verbose\") )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"depth positive numeric vector giving depth proxy values calendar ages measured. must decreasing order (.e. chronological order). ... Currently used. proxy numeric vector giving proxy values. proxy_error numeric vector giving proxy uncertainties. step length-one numeric vector specifying step size (units proxy) proxy records densities estimated. time numeric vector giving calendar ages (years). time_error numeric vector giving calendar age uncertainties (years). calendar TimeScale object specifying calendar time (see calendar()). length-one numeric vector specifying starting value temporal sequence densities estimated (years). length-one numeric vector specifying end value temporal sequence densities estimated (cal BP years). length-one numeric vector specifying increment temporal sequence densities estimated (years). n integer specifying number item choose randomly. progress logical scalar: progress bar displayed? verbose logical scalar: extra information reported?","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"ProxyRecord object.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"Boers, N., Goswami, B. & Ghil, M. (2017). Complete Representation Uncertainties Layer-Counted Paleoclimatic Archives. Climate Past, 13(9): 1169-1180. doi:10.5194/cp-13-1169-2017 .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"","code":"if (FALSE) { ## Get NGRIP records data(\"ngrip2010\", package = \"folio\") ngrip2010 <- subset(ngrip2010, !is.na(MCE)) ngrip2010 <- ngrip2010[nrow(ngrip2010):1, ] # Sort in chronological order  ## Replicate fig. 3d from Boers et al. (2017) ## /!\\ This may take a while... /!\\ ngrip_record <- proxy_ensemble(   depth = ngrip2010$depth,   proxy = ngrip2010$delta,   proxy_error = 0.01,   step = 0.001,   time = ngrip2010$age,   time_error = ngrip2010$MCE,   calendar = b2k(), # /!\\   by = 20,   n = 30 )  plot(ngrip_record) }","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"Plot Layer-Counted Proxy Records Uncertainties","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"","code":"# S4 method for ProxyRecord,missing plot(   x,   calendar = getOption(\"ananke.calendar\"),   iqr = TRUE,   xlab = NULL,   ylab = NULL,   col = grDevices::hcl.colors(12, \"YlOrRd\", rev = TRUE),   col.mean = \"black\",   col.iqr = col.mean,   lty.mean = 1,   lty.iqr = 3,   lwd.mean = 2,   lwd.iqr = lwd.mean,   ... )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"x ProxyRecord object. calendar TimeScale object specifying target calendar (see calendar()). iqr logical scalar: mean IQR displayed? xlab, ylab character string giving label x y axis. col list colors generated grDevices::hcl.colors(). col.mean, col.iqr specification line colors. used iqr TRUE. lty.mean, lty.iqr specification line types. used iqr TRUE. lwd.mean, lwd.iqr specification line widths. used iqr TRUE. ... parameters passed graphics::image().","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"plot() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"","code":"if (FALSE) { ## Get NGRIP records data(\"ngrip2010\", package = \"folio\") ngrip2010 <- subset(ngrip2010, !is.na(MCE)) ngrip2010 <- ngrip2010[nrow(ngrip2010):1, ] # Sort in chronological order  ## Replicate fig. 3d from Boers et al. (2017) ## /!\\ This may take a while... /!\\ ngrip_record <- proxy_ensemble(   depth = ngrip2010$depth,   proxy = ngrip2010$delta,   proxy_error = 0.01,   step = 0.001,   time = ngrip2010$age,   time_error = ngrip2010$MCE,   calendar = b2k(), # /!\\   by = 20,   n = 30 )  plot(ngrip_record) }","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantiles of a Density Estimate \u2014 quantile","title":"Quantiles of a Density Estimate \u2014 quantile","text":"Quantiles Density Estimate","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantiles of a Density Estimate \u2014 quantile","text":"","code":"# S4 method for CalibratedAges quantile(   x,   probs = seq(0, 1, 0.25),   na.rm = FALSE,   ...,   calendar = getOption(\"ananke.calendar\") )  # S4 method for ProxyRecord quantile(x, probs = seq(0, 1, 0.25), na.rm = FALSE, ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantiles of a Density Estimate \u2014 quantile","text":"x CalibratedAges object. probs numeric vector probabilities values \\([0,1]\\). na.rm logical scalar: NA values stripped computation proceeds? ... Currently used. calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantiles of a Density Estimate \u2014 quantile","text":"numeric  matrix containing quantiles.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantiles of a Density Estimate \u2014 quantile","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantiles of a Density Estimate \u2014 quantile","text":"","code":"## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  ## Statistics quantile(cal) #>      0%   25%   50%   75%  100% #> X -3984 -3876 -3778 -3729 -3545 #> Y -3984 -3283 -3211 -3145 -2917 median(cal) #>     X     Y  #> -3778 -3211  mean(cal) #>         X         Y  #> -3792.679 -3211.160   ## Plot plot(cal, calendar = CE())  ## Need to set 'calendar' abline(v = median(cal, calendar = CE()), lty = 2, col = \"blue\") abline(v = mean(cal, calendar = CE()), lty = 2, col = \"red\")","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"Plot Radiocarbon Event Count Ensemble","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"","code":"# S4 method for RECE,missing plot(x, calendar = getOption(\"ananke.calendar\"), ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"x RECE object. calendar TimeScale object specifying target calendar (see calendar()). ... parameters passed graphics::image().","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"image() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"Carleton, W. C. (2021). Evaluating Bayesian Radiocarbon\u2010dated Event Count (REC) Models Study Long\u2010term Human Environmental Processes. Journal Quaternary Science, 36(1): 110\u201123. doi:10.1002/jqs.3256 .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages \u2014 reexports","title":"Objects exported from other packages \u2014 reexports","text":"objects imported packages. Follow links see documentation. aion AD, b2k, BC, BCE, BP, calendar, CE, image, plot, start, start, time, year_axis arkhe interval_hdr","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object \u2014 subset","title":"Extract or Replace Parts of an Object \u2014 subset","text":"Operators acting objects extract replace parts.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object \u2014 subset","text":"","code":"# S4 method for CalibratedAges [(x, i, j, k, drop = FALSE)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object \u2014 subset","text":"x object extract element(s) replace element(s). , j, k Indices specifying elements extract replace. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object \u2014 subset","text":"subsetted object.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object \u2014 subset","text":"N. Frerebeau","code":"","package":"ananke"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Ternary Plots","text":"Ternary plots made simple. isopleuros allows create ternary plot using graphics. provides functions display data ternary space, add tune graphical elements display statistical summaries. also includes common ternary diagrams useful archaeologist (e.g.\u00a0soil texture charts, ceramic phase diagram). isopleuros dependency-free package1 designed simple possible. need finer tuning advanced features, consider Ternary ggtern package.","code":"To cite isopleuros in publications use:    Frerebeau N (2023). _isopleuros: Ternary Plots_. Universit\u00e9 Bordeaux   Montaigne, Pessac, France. R package version 1.0.0,   <https://packages.tesselle.org/isopleuros/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{isopleuros: Ternary Plots}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.0.0},     url = {https://packages.tesselle.org/isopleuros/},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Ternary Plots","text":"can install released version isopleuros CRAN : development version GitHub :","code":"install.packages(\"isopleuros\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/isopleuros\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Ternary Plots","text":"","code":"## Load package library(isopleuros) ## Graphical parameters par(mfrow = c(1, 2), mar = c(0, 0, 0, 0) + 0.1)  ## Set colors col <- c(\"blue\", \"red\")  ## Ternary plot ## (data from Aitchison 1986) ternary_plot(   x = lava,    panel.first = ternary_grid(),   col = col[as.factor(lava$A > 30)], )  ## Split data groups <- split(lava, f = lava$A > 30)  ## Add tolerance ellipses for (i in seq_along(groups)) {   ternary_tolerance(groups[[i]], level = 0.975, lty = 2, border = col[[i]]) }  ## Density contours ternary_plot(lava, panel.first = ternary_grid()) ternary_density(lava, n = 500, nlevels = 10) ## Install extra package (if needed) # install.packages(\"folio\")  ## Data from Barrera and Velde 1989 data(\"verre\", package = \"folio\")  ## Select data coda <- verre[, c(\"Na2O\", \"CaO\", \"K2O\", \"MgO\", \"P2O5\", \"Al2O3\")]  ## Ternary plots with marginal compositions ternary_pairs(coda, col = as.factor(coda$Na2O > 5)) ## Graphical parameters par(mfrow = c(2, 2), mar = c(0, 0, 0, 0) + 0.1)  ## Ceramic phase diagram ternary_plot(NULL, axes = FALSE, ann = FALSE, frame.plot = TRUE) triangle_phase_cas(symbol = TRUE, pch = 16)  ternary_plot(NULL, xlab = \"CaO\", ylab = \"Al2O3\", zlab = \"SiO2\") triangle_phase_ceramic(symbol = TRUE, pch = 16)  ## HYPRES soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_hypres()  ## USDA (1951) soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_usda(symbol = TRUE)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Ternary Plots","text":"Please note isopleuros project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/arctic.html","id":null,"dir":"Reference","previous_headings":"","what":"Arctic Lake Sediments Compositions \u2014 arctic","title":"Arctic Lake Sediments Compositions \u2014 arctic","text":"Sand, silt, clay compositions 39 sediment samples different water depths Arctic lake.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/arctic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arctic Lake Sediments Compositions \u2014 arctic","text":"","code":"arctic","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/arctic.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Arctic Lake Sediments Compositions \u2014 arctic","text":"data.frame 4 variables: sand  silt  clay  depth Water depth (m).","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/arctic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Arctic Lake Sediments Compositions \u2014 arctic","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/boxite.html","id":null,"dir":"Reference","previous_headings":"","what":"Boxite Compositions \u2014 boxite","title":"Boxite Compositions \u2014 boxite","text":"Compositions 25 specimens boxite.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/boxite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boxite Compositions \u2014 boxite","text":"","code":"boxite","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/boxite.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Boxite Compositions \u2014 boxite","text":"data.frame 5 variables: albite. B blandite. C cornite. D daubite. E endite.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/boxite.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Boxite Compositions \u2014 boxite","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":null,"dir":"Reference","previous_headings":"","what":"Cartesian Coordinates \u2014 coordinates_cartesian","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"Computes cartesian coordinates.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"","code":"coordinates_cartesian(x, y, z, ...)  # S4 method for numeric,numeric coordinates_cartesian(x, y)  # S4 method for ANY,missing coordinates_cartesian(x)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"x, y numeric vector giving x y ternary coordinates set points. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"list.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"","code":"## Compositional data coda <- data.frame(   x = c(1, 0, 0, 1/3),   y = c(0, 1, 0, 1/3),   z = c(0, 0, 1, 1/3) )  ## Ternary coordinates (tern <- coordinates_ternary(coda)) #> $x #> [1] 0.0 1.0 0.5 0.5 #>  #> $y #> [1] 0.0000000 0.0000000 0.8660254 0.2886751 #>   ## Cartesian coordinates (cart <- coordinates_cartesian(tern)) #> $x #> [1] 1.0000000 0.0000000 0.0000000 0.3333333 #>  #> $y #> [1] 0.0000000 1.0000000 0.0000000 0.3333333 #>  #> $z #> [1] 0.0000000 0.0000000 1.0000000 0.3333333 #>","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":null,"dir":"Reference","previous_headings":"","what":"Ternary Coordinates \u2014 coordinates_ternary","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"Computes ternary coordinates.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"","code":"coordinates_ternary(x, y, z, ...)  # S4 method for numeric,numeric,numeric coordinates_ternary(x, y, z, missing = getOption(\"isopleuros.missing\"))  # S4 method for ANY,missing,missing coordinates_ternary(x, missing = getOption(\"isopleuros.missing\"))","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"x, y, z numeric vector giving x, y z cartesian coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... Currently used. missing logical scalar: missing values replaced zeros computation proceeds? FALSE (default), incomplete cases removed.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"list.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"","code":"## Compositional data coda <- data.frame(   x = c(1, 0, 0, 1/3),   y = c(0, 1, 0, 1/3),   z = c(0, 0, 1, 1/3) )  ## Ternary coordinates (tern <- coordinates_ternary(coda)) #> $x #> [1] 0.0 1.0 0.5 0.5 #>  #> $y #> [1] 0.0000000 0.0000000 0.8660254 0.2886751 #>   ## Cartesian coordinates (cart <- coordinates_cartesian(tern)) #> $x #> [1] 1.0000000 0.0000000 0.0000000 0.3333333 #>  #> $y #> [1] 0.0000000 1.0000000 0.0000000 0.3333333 #>  #> $z #> [1] 0.0000000 0.0000000 1.0000000 0.3333333 #>","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/isopleuros-package.html","id":null,"dir":"Reference","previous_headings":"","what":"isopleuros: Ternary Plots \u2014 isopleuros-package","title":"isopleuros: Ternary Plots \u2014 isopleuros-package","text":"Ternary plots made simple. package allows create ternary plots using 'graphics'. provides functions display data ternary space, add tune graphical elements display statistical summaries. also includes common ternary diagrams useful archaeologist (e.g. soil texture charts, ceramic phase diagram).","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/isopleuros-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"isopleuros: Ternary Plots \u2014 isopleuros-package","text":"isopleuros uses following options() configure behavior: isopleuros.missing: logical scalar. missing values replaced zeros ternary coordinates computation proceeds? FALSE (default), incomplete cases removed.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/isopleuros-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"isopleuros: Ternary Plots \u2014 isopleuros-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne 33607 Pessac cedex France","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/lava.html","id":null,"dir":"Reference","previous_headings":"","what":"Skye Lavas Compositions \u2014 lava","title":"Skye Lavas Compositions \u2014 lava","text":"AFM compositions 23 aphyric Skye lavas.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/lava.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skye Lavas Compositions \u2014 lava","text":"","code":"lava","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/lava.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Skye Lavas Compositions \u2014 lava","text":"data.frame 3 variables: Na2O + K2O (percent). F Fe2O3 (percent). M MgO (percent).","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/lava.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Skye Lavas Compositions \u2014 lava","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"Draw arrows pairs points.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"","code":"ternary_arrows(x0, y0, z0, ...)  # S4 method for numeric,numeric,numeric ternary_arrows(x0, y0, z0, x1 = x0, y1 = y0, z1 = z0, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"x0, y0, z0 numeric vector giving x, y z ternary coordinates points draw. ... arguments passed graphics::arrows(). x1, y1, z1 numeric vector giving x, y z ternary coordinates points draw.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"ternary_arrows() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"","code":"## Add arrows ternary_plot(NULL, panel.first = ternary_grid()) ternary_arrows(x0 = 40, y0 = 20, z0 = 40,                x1 = 20, y1 = 40, z1 = 40)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an Axis to a Ternary Plot \u2014 ternary_axis","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"Adds axis current plot.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"","code":"ternary_axis(   side,   at = NULL,   labels = TRUE,   tick = TRUE,   font = NA,   lty = \"solid\",   lwd = 1,   lwd.ticks = lwd,   col = NULL,   col.ticks = NULL,   ... )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"side integer specifying side plot axis drawn . axis placed follows: 1=, 2=right 3=left. numeric vector giving points tick-marks drawn. labels logical scalar specifying whether (numerical) annotations made tickmarks, character vector labels placed tickpoints. logical, also supplied length. tick logical scalar: tickmarks axis line drawn? font font text. Defaults par(\"font.axis\"). lty character string numeric value specifying line type axis line tick marks. lwd, lwd.ticks non-negative numeric value specifying line widths axis line tick marks. col, col.ticks Colors axis line tick marks respectively. Defaults par(\"col.axis\"). ... graphical parameters may also passed arguments function, particularly, cex.axis, col.axis font.axis axis annotation.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"ternary_axis() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"","code":"## Add axis ternary_plot(NULL, axes = FALSE) ternary_axis(side = 1, col = \"red\") ternary_axis(side = 2, col = \"blue\") ternary_axis(side = 3, col = \"green\")   ## Add box and grid ternary_plot(NULL, axes = FALSE) ternary_box(lty = \"dashed\", col = \"red\") ternary_grid(lty.primary = \"dotted\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw a Box around a Ternary Plot \u2014 ternary_box","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"Draw Box around Ternary Plot","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"","code":"ternary_box(lty = \"solid\", ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"lty character string numeric value specifying line type box. ... graphical parameters may also passed arguments function, particularly, col lwd.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"ternary_box() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"","code":"## Add axis ternary_plot(NULL, axes = FALSE) ternary_axis(side = 1, col = \"red\") ternary_axis(side = 2, col = \"blue\") ternary_axis(side = 3, col = \"green\")   ## Add box and grid ternary_plot(NULL, axes = FALSE) ternary_box(lty = \"dashed\", col = \"red\") ternary_grid(lty.primary = \"dotted\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":null,"dir":"Reference","previous_headings":"","what":"Contour Lines \u2014 ternary_contour","title":"Contour Lines \u2014 ternary_contour","text":"Computes draws contour lines.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contour Lines \u2014 ternary_contour","text":"","code":"ternary_contour(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_contour(   x,   y,   z,   value,   n = 50,   nlevels = 10,   levels = pretty(range(value, na.rm = TRUE), nlevels),   ilr = TRUE,   linear = TRUE,   extrapolate = FALSE,   palette = function(i) grDevices::hcl.colors(i, \"YlOrRd\", rev = TRUE),   ... )  # S4 method for ANY,missing,missing ternary_contour(   x,   value,   n = 50,   nlevels = 10,   levels = pretty(range(value, na.rm = TRUE), nlevels),   ilr = TRUE,   linear = TRUE,   extrapolate = FALSE,   palette = function(i) grDevices::hcl.colors(i, \"YlOrRd\", rev = TRUE),   ... )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Contour Lines \u2014 ternary_contour","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed ternary_lines(). value numeric vector giving values plotted. n length-one numeric specifying number grid points. nlevels length-one numeric vector specifying number contour levels desired. used levels NULL. levels numeric vector levels draw contour lines. ilr logical scalar: interpolation computed ILR space? FALSE, interpolation computed Cartesian space. linear logical scalar: linear interpolation used? FALSE, spline interpolation used (see akima::interp()). extrapolate logical scalar: extrapolation used outside convex hull determined data points (see akima::interp())? palette color palette function takes single integer argument (number levels) returns vector colors.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Contour Lines \u2014 ternary_contour","text":"ternary_contour() called side-effects. Invisibly returns list elements levels (contour levels) colors (contour colors) can used legend.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Contour Lines \u2014 ternary_contour","text":"Contour computed bivariate interpolation onto grid, isometric log ratio transformation original data.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Contour Lines \u2014 ternary_contour","text":"akima package needs installed machine.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Contour Lines \u2014 ternary_contour","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Contour Lines \u2014 ternary_contour","text":"","code":"## Add density ## Data from Aitchison 1986 ternary_plot(arctic, panel.first = ternary_grid()) levels <- ternary_contour(arctic, value = arctic$depth, n = 100, nlevels = 10)  ## Add a legend legend_image <- grDevices::as.raster(rev(levels$colors)) graphics::rasterImage(legend_image, 0.85, 0.75, 0.9, 1) graphics::text(x = 0.9, y = c(0.75, 1), labels = range(levels$levels), pos = 4)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"Draw lines intersect point.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"","code":"ternary_crosshairs(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_crosshairs(x, y, z, x_mark = TRUE, y_mark = TRUE, z_mark = TRUE, ...)  # S4 method for ANY,missing,missing ternary_crosshairs(x, x_mark = TRUE, y_mark = TRUE, z_mark = TRUE, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... graphical parameters (see graphics::par()) may also supplied arguments, particularly, line type, lty, line width, lwd color, col. Also line characteristics lend, ljoin lmitre. x_mark, y_mark, z_mark logical scalar: x, y z axis component drawn?","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"ternary_crosshairs() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"","code":"## Add cross-hairs ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid()) ternary_crosshairs(lava)   ternary_plot(lava, panel.first = ternary_grid()) ternary_crosshairs(lava, y_mark = FALSE, z_mark = FALSE, col = \"red\")   ternary_plot(lava, panel.first = ternary_grid()) ternary_crosshairs(lava, x_mark = FALSE, z_mark = FALSE, col = \"green\")   ternary_plot(lava, panel.first = ternary_grid()) ternary_crosshairs(lava, x_mark = FALSE, y_mark = FALSE, col = \"blue\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Density Contour Lines \u2014 ternary_density","title":"Density Contour Lines \u2014 ternary_density","text":"Computes draws density contour lines.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Density Contour Lines \u2014 ternary_density","text":"","code":"ternary_density(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_density(   x,   y,   z,   h = NULL,   n = 25,   nlevels = 10,   levels = NULL,   palette = function(i) grDevices::hcl.colors(i, \"YlOrRd\", rev = TRUE),   ... )  # S4 method for ANY,missing,missing ternary_density(   x,   h = NULL,   n = 25,   nlevels = 10,   levels = NULL,   palette = function(i) grDevices::hcl.colors(i, \"YlOrRd\", rev = TRUE),   ... )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Density Contour Lines \u2014 ternary_density","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed ternary_lines(). h length-one numeric vector giving bandwidth. n length-one numeric specifying number grid points. nlevels length-one numeric vector specifying number contour levels desired. used levels NULL. levels numeric vector levels draw contour lines. palette color palette function takes single integer argument (number levels) returns vector colors.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Density Contour Lines \u2014 ternary_density","text":"ternary_density() called side-effects. Invisibly returns list elements levels (contour levels) colors (contour colors) can used legend.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density Contour Lines \u2014 ternary_density","text":"Two-dimensional kernel density estimation axis-aligned bivariate normal kernel. Normal kernel evaluated square grid, isometric log ratio transformation original data.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Density Contour Lines \u2014 ternary_density","text":"Two-dimensional kernel density estimation adapted MASS::kde2d(). must considered experimental subject major changes future release.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Density Contour Lines \u2014 ternary_density","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Density Contour Lines \u2014 ternary_density","text":"","code":"## Add density ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid()) levels <- ternary_density(lava, n = 500, nlevels = 10)  ## Add a legend legend_image <- grDevices::as.raster(rev(levels$colors)) graphics::rasterImage(legend_image, 0.85, 0.75, 0.9, 1) graphics::text(x = 0.9, y = c(0.75, 1), labels = range(levels$levels), pos = 4)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"Computes draws confidence/tolerance ellipse.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"","code":"ternary_ellipse(x, y, z, ...)  ternary_confidence(x, y, z, ...)  ternary_tolerance(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_ellipse(x, y, z, radius = 1, ...)  # S4 method for ANY,missing,missing ternary_ellipse(x, radius = 1, ...)  # S4 method for numeric,numeric,numeric ternary_confidence(x, y, z, level = 0.95, ...)  # S4 method for ANY,missing,missing ternary_confidence(x, level = 0.95, ...)  # S4 method for numeric,numeric,numeric ternary_tolerance(x, y, z, level = 0.95, ...)  # S4 method for ANY,missing,missing ternary_tolerance(x, level = 0.95, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::polygon(). radius numeric vector specifying scaling half-diameters. level numeric vector specifying confidence/tolerance level.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"ternary_ellipse() called side-effects.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"Ellipse coordinates computed isometric log ratio transformation original data.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"","code":"## Ellipses ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid(5, 10)) ternary_tolerance(lava, level = 0.95, border = \"blue\", lty = 2) ternary_confidence(lava, level = 0.95, border = \"red\", lty = 3)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Grid to a Ternary Plot \u2014 ternary_grid","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"Adds triangular grid existing plot.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"","code":"ternary_grid(   primary = NULL,   secondary = NULL,   col.primary = \"darkgray\",   col.secondary = \"lightgray\",   lty.primary = \"dashed\",   lty.secondary = \"dotted\",   lwd.primary = 1,   lwd.secondary = lwd.primary )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"primary integer specifying number cells primary grid x, y z direction. secondary integer specifying number cells secondary grid x, y z direction. col.primary, col.secondary character string specifying color grid lines. lty.primary, lty.secondary character string numeric value specifying line type grid lines. lwd.primary, lwd.secondary non-negative numeric value specifying line width grid lines.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"ternary_grid() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"","code":"## Blank plot ternary_plot(NULL)   ## Compositional data coda <- data.frame(   X = c(20, 60, 20, 1/3),   Y = c(20, 20, 60, 1/3),   Z = c(60, 20, 20, 1/3) )  ## Ternary plot ternary_plot(coda, pch = 16, col = \"red\")   ## Add a grid ternary_plot(coda, panel.first = ternary_grid(5, 10))   ## Zoom ternary_plot(coda, xlim = c(0.5, 1), panel.first = ternary_grid())  ternary_plot(coda, ylim = c(0.5, 1), panel.first = ternary_grid())  ternary_plot(coda, zlim = c(0.5, 1), panel.first = ternary_grid())   ## Color according to a supplementary variable ## Data from Aitchison 1986 col <- grDevices::colorRampPalette(c(\"red\", \"blue\"))(nrow(arctic)) ternary_plot(arctic, panel.first = ternary_grid(), pch = 16, col = col)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":null,"dir":"Reference","previous_headings":"","what":"Convex Hull of a Set of Points \u2014 ternary_hull","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"Computes draws convex hull set points specified.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"","code":"ternary_hull(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_hull(x, y, z, ...)  # S4 method for ANY,missing,missing ternary_hull(x, y, z, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::polygon().","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"ternary_hull() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"","code":"## Convex hull ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid(5, 10)) ternary_hull(lava, border = \"red\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"Add Connected Line Segments Ternary Plot","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"","code":"ternary_lines(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_lines(x, y, z, type = \"l\", ...)  # S4 method for ANY,missing,missing ternary_lines(x, type = \"l\", ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... graphical parameters (see graphics::par()) may also supplied arguments, particularly, line type, lty, line width, lwd, color, col type = \"b\", pch. Also line characteristics lend, ljoin lmitre. type character string indicating type plotting; actually types graphics::plot.default().","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"ternary_lines() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"","code":"## Compositional data coda <- data.frame(   X = c(20, 60, 20, 20),   Y = c(20, 20, 60, 40),   Z = c(60, 20, 20, 40) )  ## Add lines ternary_plot(NULL, panel.first = ternary_grid()) ternary_lines(coda, col = \"red\", lwd = 2)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Mean \u2014 ternary_mean","title":"Compositional Mean \u2014 ternary_mean","text":"Computes draws closed geometric mean set points specified.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compositional Mean \u2014 ternary_mean","text":"","code":"ternary_mean(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_mean(x, y, z, ...)  # S4 method for ANY,missing,missing ternary_mean(x, y, z, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compositional Mean \u2014 ternary_mean","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::points().","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compositional Mean \u2014 ternary_mean","text":"ternary_mean() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Mean \u2014 ternary_mean","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Mean \u2014 ternary_mean","text":"","code":"## Mean ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid()) ternary_mean(lava, pch = 16, col = \"red\") ternary_confidence(lava, level = 0.95, border = \"red\", lty = 1)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":null,"dir":"Reference","previous_headings":"","what":"Ternary Plot Matrices \u2014 ternary_pairs","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"Produces matrix ternary plots.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"","code":"ternary_pairs(x, ...)  # S4 method for matrix ternary_pairs(x, margin = NULL, ...)  # S4 method for data.frame ternary_pairs(x, margin = NULL, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"x matrix data.frame. Columns converted numeric way data.matrix() . ... arguments passed graphics::arrows(). margin character string integer giving index column used third part ternary plots. NULL (default), marginal compositions used (.e. geometric mean non-selected parts).","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"ternary_pairs() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"","code":"## Data from Aitchison 1986 ## Ternary plots with marginal compositions ternary_pairs(boxite)   ## Ternary plots with endite ternary_pairs(boxite, margin = \"E\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis \u2014 ternary_pca","title":"Principal Component Analysis \u2014 ternary_pca","text":"Computes draws principal component.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis \u2014 ternary_pca","text":"","code":"ternary_pca(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_pca(x, y, z, axis = 1, ...)  # S4 method for ANY,missing,missing ternary_pca(x, axis = 1, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis \u2014 ternary_pca","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::lines(). axis integer specifying dimension plotted.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis \u2014 ternary_pca","text":"ternary_pca() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Component Analysis \u2014 ternary_pca","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis \u2014 ternary_pca","text":"","code":"## PCA ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid()) ternary_pca(lava, axis = 1, col = \"red\", lty = 2)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Ternary Plot \u2014 ternary_plot","title":"Ternary Plot \u2014 ternary_plot","text":"Produces ternary plot.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ternary Plot \u2014 ternary_plot","text":"","code":"ternary_plot(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_plot(   x,   y,   z,   xlim = NULL,   ylim = NULL,   zlim = NULL,   xlab = NULL,   ylab = NULL,   zlab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for ANY,missing,missing ternary_plot(   x,   xlim = NULL,   ylim = NULL,   zlim = NULL,   xlab = NULL,   ylab = NULL,   zlab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ternary Plot \u2014 ternary_plot","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... graphical parameters may also passed arguments function. xlim length-two numeric vector giving x limits range \\([0,1]\\). ylim length-two numeric vector giving y limits range \\([0,1]\\). zlim length-two numeric vector giving z limits range \\([0,1]\\). xlab, ylab, zlab character string giving label x, y z axes. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ternary Plot \u2014 ternary_plot","text":"ternary_plot() called side-effects: results graphic displayed.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ternary Plot \u2014 ternary_plot","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ternary Plot \u2014 ternary_plot","text":"","code":"## Blank plot ternary_plot(NULL)   ## Compositional data coda <- data.frame(   X = c(20, 60, 20, 1/3),   Y = c(20, 20, 60, 1/3),   Z = c(60, 20, 20, 1/3) )  ## Ternary plot ternary_plot(coda, pch = 16, col = \"red\")   ## Add a grid ternary_plot(coda, panel.first = ternary_grid(5, 10))   ## Zoom ternary_plot(coda, xlim = c(0.5, 1), panel.first = ternary_grid())  ternary_plot(coda, ylim = c(0.5, 1), panel.first = ternary_grid())  ternary_plot(coda, zlim = c(0.5, 1), panel.first = ternary_grid())   ## Color according to a supplementary variable ## Data from Aitchison 1986 col <- grDevices::colorRampPalette(c(\"red\", \"blue\"))(nrow(arctic)) ternary_plot(arctic, panel.first = ternary_grid(), pch = 16, col = col)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Points to a Ternary Plot \u2014 ternary_points","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"Add Points Ternary Plot","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"","code":"ternary_points(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_points(x, y, z, type = \"p\", ...)  # S4 method for ANY,missing,missing ternary_points(x, type = \"p\", ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... graphical parameters (see graphics::par()) may also supplied arguments, particularly, plotting character, pch, character expansion, cex color, col. type character string indicating type plotting; actually types graphics::plot.default().","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"ternary_points() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"","code":"## Add points ## Data from Aitchison 1986 ternary_plot(NULL, panel.first = ternary_grid()) ternary_points(lava, col = \"red\", pch = 16)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":null,"dir":"Reference","previous_headings":"","what":"Polygon Drawing \u2014 ternary_polygon","title":"Polygon Drawing \u2014 ternary_polygon","text":"Draws polygons whose vertices given x, y z.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polygon Drawing \u2014 ternary_polygon","text":"","code":"ternary_polygon(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_polygon(x, y, z, ...)  # S4 method for ANY,missing,missing ternary_polygon(x, y, z, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polygon Drawing \u2014 ternary_polygon","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::polygon().","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Polygon Drawing \u2014 ternary_polygon","text":"ternary_polygon() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Polygon Drawing \u2014 ternary_polygon","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polygon Drawing \u2014 ternary_polygon","text":"","code":"## Compositional data coda <- data.frame(   X = c(20, 60, 20),   Y = c(20, 20, 60),   Z = c(60, 20, 20) )  ## Add a polygon ternary_plot(NULL, panel.first = ternary_grid()) ternary_polygon(coda, density = 5, border = \"red\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"Draw line segments pairs points.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"","code":"ternary_segments(x0, y0, z0, ...)  # S4 method for numeric,numeric,numeric ternary_segments(x0, y0, z0, x1 = x0, y1 = y0, z1 = z0, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"x0, y0, z0 numeric vector giving x, y z ternary coordinates points draw. ... graphical parameters (see graphics::par()) may also supplied arguments, particularly, line type, lty, line width, lwd color, col. Also line characteristics lend, ljoin lmitre. x1, y1, z1 numeric vector giving x, y z ternary coordinates points draw.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"ternary_segments() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"","code":"## Add segments ternary_plot(NULL, panel.first = ternary_grid()) ternary_segments(x0 = 40, y0 = 20, z0 = 40,                  x1 = 20, y1 = 40, z1 = 40)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Text to a Ternary Plot \u2014 ternary_text","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"Draws strings given vector labels coordinates given x, y z.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"","code":"ternary_text(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_text(x, y, z, labels = seq_along(x), ...)  # S4 method for ANY,missing,missing ternary_text(x, labels = seq_along(x$x), ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::text(). labels character vector expression specifying text written.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"ternary_text() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"","code":"## Compositional data coda <- data.frame(   X = c(20, 60, 20),   Y = c(20, 20, 60),   Z = c(60, 20, 20) )  ## Add text ternary_plot(NULL, panel.first = ternary_grid()) ternary_text(coda, labels = c(\"A\", \"B\", \"C\"), col = \"red\", cex = 2)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":null,"dir":"Reference","previous_headings":"","what":"Ternary Plot Annotation \u2014 ternary_title","title":"Ternary Plot Annotation \u2014 ternary_title","text":"Ternary Plot Annotation","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ternary Plot Annotation \u2014 ternary_title","text":"","code":"ternary_title(   main = NULL,   sub = NULL,   xlab = NULL,   ylab = NULL,   zlab = NULL,   line = NA,   outer = FALSE,   ... )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ternary Plot Annotation \u2014 ternary_title","text":"main character string specifying main title (top). sub character string specifying sub-title (bottom). xlab, ylab, zlab character string giving label x, y z axes. line Specifying value line overrides default placement labels, places many lines outwards plot edge. outer logical scalar: titles placed outer margins plot? ... graphical parameters may also passed arguments function, particularly, font.main, cex.main, col.main font.sub, cex.sub, col.sub title annotation; font.lab, cex.lab col.lab axis label.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ternary Plot Annotation \u2014 ternary_title","text":"ternary_title() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ternary Plot Annotation \u2014 ternary_title","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ternary Plot Annotation \u2014 ternary_title","text":"","code":"## Add title ternary_plot(NULL, main = \"Main title\", sub = \"Subtitle\",              xlab = \"A\", ylab = \"B\", zlab = \"C\")   ternary_plot(NULL, ann = FALSE) ternary_title(main = \"Main title\", sub = \"Subtitle\",               xlab = \"A\", ylab = \"B\", zlab = \"C\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_phase_cas.html","id":null,"dir":"Reference","previous_headings":"","what":"Ceramic Phase Diagram \u2014 triangle_phase_cas","title":"Ceramic Phase Diagram \u2014 triangle_phase_cas","text":"Ceramic Phase Diagram","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_phase_cas.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ceramic Phase Diagram \u2014 triangle_phase_cas","text":"","code":"triangle_phase_cas(labels = TRUE, symbol = FALSE, mol = FALSE, ...)  triangle_phase_ceramic(labels = TRUE, symbol = FALSE, mol = FALSE, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_phase_cas.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ceramic Phase Diagram \u2014 triangle_phase_cas","text":"labels logical scalar: labels displayed? symbol logical scalar: symbol used instead full labels? used labels TRUE. mol logical scalar: molarity used instead molar mass? ... arguments passed graphics::polygon().","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_phase_cas.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ceramic Phase Diagram \u2014 triangle_phase_cas","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_phase_cas.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ceramic Phase Diagram \u2014 triangle_phase_cas","text":"","code":"## Ceramic phase diagram ternary_plot(NULL, xlab = \"CaO\", ylab = \"Al2O3\", zlab = \"SiO2\") triangle_phase_ceramic(symbol = TRUE, mol = TRUE, pch = 16)   ternary_plot(NULL, xlab = \"CaO\", ylab = \"Al2O3\", zlab = \"SiO2\") triangle_phase_ceramic(symbol = TRUE, mol = FALSE, pch = 16)   ## CAS diagram ternary_plot(NULL, axes = FALSE, ann = FALSE, frame.plot = TRUE) triangle_phase_cas(mol = FALSE, pch = 16)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_soil.html","id":null,"dir":"Reference","previous_headings":"","what":"Soil Texture Triangle \u2014 triangle_soil","title":"Soil Texture Triangle \u2014 triangle_soil","text":"Soil Texture Triangle","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_soil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Soil Texture Triangle \u2014 triangle_soil","text":"","code":"triangle_soil_hypres(labels = TRUE, symbol = FALSE, ...)  triangle_soil_folk(labels = TRUE, symbol = FALSE, ...)  triangle_soil_shepard(labels = TRUE, symbol = FALSE, ...)  triangle_soil_usda(labels = TRUE, symbol = FALSE, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_soil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Soil Texture Triangle \u2014 triangle_soil","text":"labels logical scalar: labels displayed? symbol logical scalar: symbol used instead full labels? used labels TRUE. ... arguments passed graphics::polygon().","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_soil.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Soil Texture Triangle \u2014 triangle_soil","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_soil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Soil Texture Triangle \u2014 triangle_soil","text":"","code":"## HYPRES soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_hypres()   ## USDA (1951) soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_usda(symbol = TRUE)   ## Folk (1954) soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_folk(symbol = TRUE)   ## Shepard (1954) soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_shepard()","package":"isopleuros"},{"path":[],"package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Easily Install and Load tesselle Packages","text":"tesselle suite collection packages research teaching archaeology. packages focus quantitative analysis methods developed archaeology. tesselle packages designed work seamlessly together complement general-purpose specialized statistical packages. packages can used explore analyze common data types archaeology: count data, compositional data chronological data. tesselle package designed make easy install load key packages tesselle suite single step.","code":"To cite tesselle in publications use:    Frerebeau N (2023). _tesselle: Easily Install and Load 'tesselle'   Packages_. Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.6500491 <https://doi.org/10.5281/zenodo.6500491>,   R package version 1.3.0, <https://packages.tesselle.org/tesselle/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{tesselle: Easily Install and Load 'tesselle' Packages}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.3.0},     url = {https://packages.tesselle.org/tesselle/},     doi = {10.5281/zenodo.6500491},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Easily Install and Load tesselle Packages","text":"can install released version tesselle CRAN : development version GitHub :","code":"install.packages(\"tesselle\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/tesselle\")","package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Easily Install and Load tesselle Packages","text":"library(tesselle) load core packages: tabula: analysis visualization archaeological count data; kairos: analysis chronological patterns archaeological count data; khroma: colour schemes scientific data visualization.","code":"library(tesselle) #> --- Attaching packages -------------------------------------------- tesselle --- #> * tabula    3.0.0 #> * kairos    2.0.0 #> * khroma    1.11.0","package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Easily Install and Load tesselle Packages","text":"Please note tesselle project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"tesselle"},{"path":[],"package":"tesselle"},{"path":[],"package":"tesselle"},{"path":[],"package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/reference/tesselle-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tesselle: Easily Install and Load 'tesselle' Packages \u2014 tesselle-package","title":"tesselle: Easily Install and Load 'tesselle' Packages \u2014 tesselle-package","text":"Easy install load key packages 'tesselle' suite single step. 'tesselle' suite collection packages research teaching archaeology. packages focus quantitative analysis methods developed archaeology. 'tesselle' packages designed work seamlessly together complement general-purpose specialized statistical packages. packages can used explore analyze common data types archaeology: count data, compositional data chronological data. Learn 'tesselle' https://www.tesselle.org.","code":"","package":"tesselle"},{"path":[],"package":"tesselle"},{"path":[],"package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/reference/tesselle-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tesselle: Easily Install and Load 'tesselle' Packages \u2014 tesselle-package","text":"Full list authors contributors (alphabetic order): Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne 33607 Pessac cedex France","code":"","package":"tesselle"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Datasets for Teaching Archaeology and Paleontology","text":"Datasets teaching quantitative approaches modeling archaeology paleontology. package provides several types data related broad topics (cultural evolution, radiocarbon dating, paleoenvironments, etc.), can used illustrate statistical methods classroom (multivariate data analysis, compositional data analysis, diversity measurement, etc.). package teaching purposes : datasets may outdated. using folio, must cite relevant publications dataset (see documentation).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Datasets for Teaching Archaeology and Paleontology","text":"can install released version folio CRAN : development version GitHub :","code":"install.packages(\"folio\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/folio\")","package":"folio"},{"path":"https://packages.tesselle.org/folio/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Datasets for Teaching Archaeology and Paleontology","text":"","code":"## Arnold and Libby's Curve of Knowns ## Replicate fig. 1 from Arnold and Libby (1949) data(\"arnold1949\")  fit <- lm(activity_found ~ age_expected, data = arnold1949) summary(fit) #>  #> Call: #> lm(formula = activity_found ~ age_expected, data = arnold1949) #>  #> Residuals: #>        1        2        3        4        5        6  #>  0.31341 -0.27618 -0.04573 -0.19344  0.09780  0.10414  #>  #> Coefficients: #>                Estimate Std. Error t value Pr(>|t|)     #> (Intercept)  12.2665215  0.2903726   42.24 1.88e-06 *** #> age_expected -0.0011588  0.0000938  -12.35 0.000247 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Residual standard error: 0.2421 on 4 degrees of freedom #> Multiple R-squared:  0.9745, Adjusted R-squared:  0.9681  #> F-statistic: 152.6 on 1 and 4 DF,  p-value: 0.0002467  plot(   x = arnold1949$age_expected,   y = arnold1949$activity_found,   type = \"p\",   pch = 16,   xlab = \"Historical age (years)\",   ylab = \"Specific activity (C/m/g)\",   main = \"Samples of known ages\" ) segments(   x0 = arnold1949$age_expected,   y0 = arnold1949$activity_found - arnold1949$activity_found_error,   x1 = arnold1949$age_expected,   y1 = arnold1949$activity_found + arnold1949$activity_found_error ) segments(   x0 = arnold1949$age_expected - arnold1949$age_expected_error,   y0 = arnold1949$activity_found,   x1 = arnold1949$age_expected + arnold1949$age_expected_error,   y1 = arnold1949$activity_found ) abline(fit, col = \"red\")","package":"folio"},{"path":"https://packages.tesselle.org/folio/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Datasets for Teaching Archaeology and Paleontology","text":"Please note folio project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":[],"package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/arnold1949.html","id":null,"dir":"Reference","previous_headings":"","what":"Arnold and Libby's Curve of Knowns \u2014 arnold1949","title":"Arnold and Libby's Curve of Knowns \u2014 arnold1949","text":"\"agreement prediction observation seen satisfactory.\"","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/arnold1949.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arnold and Libby's Curve of Knowns \u2014 arnold1949","text":"","code":"arnold1949","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/arnold1949.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Arnold and Libby's Curve of Knowns \u2014 arnold1949","text":"data.frame 6 observations 8 variables: sample Sample name. age_expected Expected age (year BP). age_expected_error Error age_expected (year BP). age_found Measured age (year BP). age_found_error Error age_found (year BP). activity_expected Expected specific activity (cpm/g carbon). activity_found Measured specific activity (cpm/g carbon). activity_found_error Error activity_found (cpm/g carbon).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/arnold1949.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Arnold and Libby's Curve of Knowns \u2014 arnold1949","text":"Arnold, J. R. Libby, W. F. (1949). Age Determinations Radiocarbon Content: Checks Samples Known Age. Science, 110(2869), 678-80. doi:10.1126/science.110.2869.678","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/birds.html","id":null,"dir":"Reference","previous_headings":"","what":"European Birds \u2014 birds","title":"European Birds \u2014 birds","text":"dataset birds species abundance remote European woodlands.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/birds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"European Birds \u2014 birds","text":"","code":"birds","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/birds.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"European Birds \u2014 birds","text":"data.frame 35 rows (species) 3 variables (woodlands).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/birds.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"European Birds \u2014 birds","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/boves.html","id":null,"dir":"Reference","previous_headings":"","what":"Boves Ceramics \u2014 boves","title":"Boves Ceramics \u2014 boves","text":"dataset containing ceramic counts castle site Boves (Somme, France). data grouped eight periods ranging 10th 18th century thirteen ceramic types.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/boves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boves Ceramics \u2014 boves","text":"","code":"boves","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/boves.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Boves Ceramics \u2014 boves","text":"data.frame 8 rows 13 variables (ceramic types):  IIa  IIb  IIc  IIIa  IIIb  IIIc  IVa  IVb  Va  Vb  VI  VII","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/boves.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Boves Ceramics \u2014 boves","text":"Racinet P. (2002). Le site castral et prioral de Boves du Xe au XVIIe si\u00e8cle. Bilan des recherches 1996-2000. Revue arch\u00e9ologique de Picardie. Num\u00e9ro sp\u00e9cial 20, 123 p.","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/chevelon.html","id":null,"dir":"Reference","previous_headings":"","what":"Chevelon Ground Stone \u2014 chevelon","title":"Chevelon Ground Stone \u2014 chevelon","text":"dataset ground stone artifact counts Cholla project (USA).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/chevelon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chevelon Ground Stone \u2014 chevelon","text":"","code":"chevelon","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/chevelon.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Chevelon Ground Stone \u2014 chevelon","text":"data.frame 12 rows 10 variables (ground stone types): BMe Basin metate. SMe Slab metate. TMe Trough metate. IMe Indeterminate metate. UMa Unifacial mano. BMa Bifacial mano. MUHa Modified unifacial handstone. MBHa Modified bifacial handstone. UUHa Unmodified unifacial handstone. UBHa Unmodified bifacial handstone.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/chevelon.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Chevelon Ground Stone \u2014 chevelon","text":"Reid, J. J. (ed.) (1982). Cholla Project Archaeology. Vol. 2. Archaeological Series 161. Tucson: University Arizona. doi:10.6067/XCV8435710","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/compiegne.html","id":null,"dir":"Reference","previous_headings":"","what":"Compi\u00e8gne Ceramics \u2014 compiegne","title":"Compi\u00e8gne Ceramics \u2014 compiegne","text":"dataset containing ceramic counts Place des Hallettes Compi\u00e8gne (Oise, France). data grouped five periods century, ranging 9th 14th century, sixteen ceramic types.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/compiegne.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compi\u00e8gne Ceramics \u2014 compiegne","text":"","code":"compiegne","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/compiegne.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Compi\u00e8gne Ceramics \u2014 compiegne","text":"data.frame 5 rows (chronological periods, numbered oldest recent 1 5) 16 variables (ceramic types): Red white ceramics fine sized inclusions. B Red white ceramics medium sized inclusions. C Dark ceramics fine sized inclusions. D Dark ceramics medium sized inclusions. E Ceramics close groups B D, similarities group F. F Black, red beige ceramics coarse inclusions. G Red polished ceramics fine medium sized inclusions. H Black polished ceramics fine sized inclusions. Black polished ceramics medium sized inclusions. J Polished painted ceramics fine medium sized inclusions. K Painted ceramics, similar group . L Painted ceramics, similar group B. M Painted ceramics coarse inclusions. N Glazed ceramics. O Stamped ceramics. P Coated ceramics.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/compiegne.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Compi\u00e8gne Ceramics \u2014 compiegne","text":"Lacroix, M. C. (1997). La c\u00e9ramique m\u00e9di\u00e9vale du site des Hallettes \u00e0 Compi\u00e8gne (Oise). Revue arch\u00e9ologique de Picardie. Num\u00e9ro sp\u00e9cial, 13(1), 135-168. doi:10.3406/pica.1997.1945","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":null,"dir":"Reference","previous_headings":"","what":"EPICA Dome C \u2014 epica2008","title":"EPICA Dome C \u2014 epica2008","text":"EPICA Dome C 800-ka composite \\(CO_2\\) data.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"EPICA Dome C \u2014 epica2008","text":"","code":"epica2008","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"EPICA Dome C \u2014 epica2008","text":"data.frame 2 variables: age Year BP. CO2 \\(CO_2\\) (ppmv).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"EPICA Dome C \u2014 epica2008","text":"https://www.ncei.noaa.gov/access/paleo-search/study/6091","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"EPICA Dome C \u2014 epica2008","text":"L\u00fcthi, D., Le Floch, M., Bereiter, B., Blunier, T., Barnola, J.-M., Siegenthaler, U., Raynaud, D., Jouzel, J., Fischer, H., Kawamura, K. Stocker, T. F. (2008). High-resolution carbon dioxide concentration record 650,000-800,000 years present. Nature, 453, 379-382. doi:10.1038/nature06949 Monnin, E., Indermuhle, ., Dallenbach, ., Fluckiger, J., Stauffer, B., Stocker, T. F., Raynaud, D. Barnola, J.-M. (2001). Atmospheric CO2 concentrations last glacial termination. Science, 291, 112-114. doi:10.1126/science.291.5501.112 Petit, J. R., Jouzel, J., Raynaud, D., Barkov, N. ., Barnola, J.-M., Basile, ., Benders, M., Chappellaz, J., Davis, M., Delayque, G., Delmotte, M., Kotlyakov, V. M., Legrand, M., Lipenkov, V. Y., Lorius, C., Pepin, L., Ritz, C., Saltzman, E. Stievenard, M. (1999). Climate atmospheric history past 420,000 years Vostok ice core, Antarctica. Nature, 399, 429-436. doi:10.1038/20859 Siegenthaler, U., Stocker, T. F., Monnin, E., Luthi, D., Schwander, J., Stauffer, B., Raynaud, D., Barnola, J.-M., Fischer, H., Masson-Delmotte, V., Jouzel, J. (2005). Stable Carbon Cycle-Climate Relationship Late Pleistocene. Science, 310, 1313-1317. doi:10.1126/science.1120130","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"EPICA Dome C \u2014 epica2008","text":"","code":"plot(   x = epica2008$age / 1000,   y = epica2008$CO2,   type = \"l\",   xlim = c(800, 0),   xlab = \"kilo year BP\",   ylab = expression(\"CO\"[2]~\"(ppmv)\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/folio-package.html","id":null,"dir":"Reference","previous_headings":"","what":"folio: Datasets for Teaching Archaeology and Paleontology \u2014 folio-package","title":"folio: Datasets for Teaching Archaeology and Paleontology \u2014 folio-package","text":"Datasets teaching quantitative approaches modeling archaeology paleontology. package provides several types data related broad topics (cultural evolution, radiocarbon dating, paleoenvironments, etc.), can used illustrate statistical methods classroom (multivariate data analysis, compositional data analysis, diversity measurement, etc.).","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/folio-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"folio: Datasets for Teaching Archaeology and Paleontology \u2014 folio-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal09.html","id":null,"dir":"Reference","previous_headings":"","what":"IntCal09 \u2014 intcal09","title":"IntCal09 \u2014 intcal09","text":"IntCal series radiocarbon calibration curves.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal09.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IntCal09 \u2014 intcal09","text":"","code":"intcal09","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal09.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"IntCal09 \u2014 intcal09","text":"data.frame 5 variables: calBP Calendar (calibrated) age (year BP). age Radiocarbon age (year BP). error Radiocarbon error (year BP). delta Isotopic ratio \\(\\Delta^{14}C\\) (per mil). sigma Error delta (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal09.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"IntCal09 \u2014 intcal09","text":"Reimer, P. J., Baillie, M. G. L., Bard, E., Bayliss, ., Beck, J. W., Blackwell, P. G., Bronk Ramsey, C. et al. (2009). IntCal09 Marine09 Radiocarbon age Calibration Curves, 0-50,000 Years Cal BP. Radiocarbon, 51(4): 1111-50. doi:10.1017/S0033822200034202 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal13.html","id":null,"dir":"Reference","previous_headings":"","what":"IntCal13 \u2014 intcal13","title":"IntCal13 \u2014 intcal13","text":"IntCal series radiocarbon calibration curves.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal13.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IntCal13 \u2014 intcal13","text":"","code":"intcal13","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal13.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"IntCal13 \u2014 intcal13","text":"data.frame 5 variables: calBP Calendar (calibrated) age (year BP). age Radiocarbon age (year BP). error Radiocarbon error (year BP). delta Isotopic ratio \\(\\Delta^{14}C\\) (per mil). sigma Error delta (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal13.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"IntCal13 \u2014 intcal13","text":"Reimer, P. J., Bard, E. Bayliss, ., Beck, J. W., Blackwell, P. G., Bronk Ramsey, C., Buck, C. E. et al. (2013). IntCal13 Marine13 Radiocarbon age Calibration Curves 0-50,000 Years cal BP. Radiocarbon, 55(4): 1869-87. doi:10.2458/azu_js_rc.55.16947 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal20.html","id":null,"dir":"Reference","previous_headings":"","what":"IntCal20 \u2014 intcal20","title":"IntCal20 \u2014 intcal20","text":"IntCal series radiocarbon calibration curves.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal20.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IntCal20 \u2014 intcal20","text":"","code":"intcal20","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal20.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"IntCal20 \u2014 intcal20","text":"data.frame 5 variables: calBP Calendar (calibrated) age (year BP). age Radiocarbon age (year BP). error Radiocarbon error (year BP). delta Isotopic ratio \\(\\Delta^{14}C\\) (per mil). sigma Error delta (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal20.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"IntCal20 \u2014 intcal20","text":"Reimer, P. J., Austin, W. E. N., Bard, E., Bayliss, ., Blackwell, P. G., Bronk Ramsey, C., Butzin, M. et al. (2020). IntCal20 Northern Hemisphere Radiocarbon \u00e2ge Calibration Curve (0-55 Cal KBP). Radiocarbon, 62(4), 725-757. doi:10.1017/RDC.2020.41 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/kommos.html","id":null,"dir":"Reference","previous_headings":"","what":"Transport Jars from Kommos (Crete). \u2014 kommos","title":"Transport Jars from Kommos (Crete). \u2014 kommos","text":"Chemical analysis (neutron activation analysis) 88 Late Bronze Age transport jars found excavations Kommos, Crete.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/kommos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transport Jars from Kommos (Crete). \u2014 kommos","text":"","code":"kommos","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/kommos.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transport Jars from Kommos (Crete). \u2014 kommos","text":"data.frame 22 variables (chemical elements): type CJ: Canaanite jar; EJ: Egyptian jar; TSJ: transport stirrup jar; SNA: short-necked amphora. date Chronology (period). Sm Sm content (ppm). Lu Lu content (ppm). U U content (ppm). Yb Yb content (ppm). content (ppm). Sb Sb content (ppm). Ca Ca content (ppm). Na Na content (ppm). La La content (ppm). Ce Ce content (ppm). Th Th content (ppm). Cr Cr content (ppm). Hf Hf content (ppm). Cs Cs content (ppm). Sc Sc content (ppm). Rb Rb content (ppm). Fe Fe content (ppm). Ta Ta content (ppm). Co Co content (ppm). Eu Eu content (ppm).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/kommos.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Transport Jars from Kommos (Crete). \u2014 kommos","text":"Day, P. M., Quinn, P. S., Rutter, J. B. & Kilikoglou, V. (2011). World Goods: Transport Jars Commodity Exchange Late Bronze Age Harbor Kommos, Crete. Hesperia, 80, 511-558. doi:10.2972/hesperia.80.4.0511","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":null,"dir":"Reference","previous_headings":"","what":"Law Dome Ice Core \u2014 law2006","title":"Law Dome Ice Core \u2014 law2006","text":"Law Dome Ice Core 2000-year \\(CH_4\\), \\(CO_2\\) \\(N_{2}O\\) data.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Law Dome Ice Core \u2014 law2006","text":"","code":"law2006","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Law Dome Ice Core \u2014 law2006","text":"data.frame 2004 observations 8 variables: year Year AD. NOAA04 NOAA04 \\(CH_4\\) scale. CH4_spl \\(CH_4\\) spline (ppb). CH4_grw \\(CH_4\\) growth Rate (ppb/yr). CO2_spl \\(CO_2\\) spline (ppb). CO2_grw \\(CO_2\\) growth Rate (ppb/yr). N2O_spl \\(N_{2}O\\) spline (ppb). N2O_grw \\(N_{2}O\\) growth Rate (ppb/yr).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Law Dome Ice Core \u2014 law2006","text":"https://www.ncei.noaa.gov/access/paleo-search/study/9959","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Law Dome Ice Core \u2014 law2006","text":"MacFarling Meure, C., Etheridge, D., Trudinger, C., Steele, P., Langenfelds, R., van Ommen, T., Smith, . Elkins, J. (2006). Law Dome CO2, CH4 N2O Ice Core Records Extended 2000 years BP. Geophysical Research Letters, 33(14), L14810. doi:10.1029/2006GL026152 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Law Dome Ice Core \u2014 law2006","text":"","code":"plot(   x = law2006$year,   y = law2006$CO2_spl,   type = \"l\",   xlab = \"Year AD\",   ylab = expression(\"CO\"[2]~\"(ppm)\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"global Pliocene-Pleistocene benthic \\(\\delta^{18}O\\) stack.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"","code":"lisiecki2005","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"data.frame 3 variables: age Calendar age (kilo year cal BP). delta Benthic \\(\\delta^{18}O\\) (per mil). error Standard error (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"https://www.ncei.noaa.gov/access/paleo-search/study/5847","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"LR04 stack spans 5.3 Myr average 57 globally distributed benthic \\(\\delta^{18}O\\) records (measure global ice volume deep ocean temperature) collected scientific literature.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"Lisiecki, L. E. Raymo, M. E. (2005). Pliocene-Pleistocene stack 57 globally distributed benthic d18O records. Paleoceanography, 20, PA1003. doi:10.1029/2004PA001071","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"","code":"plot(   x = lisiecki2005$age,   y = lisiecki2005$delta,   type = \"l\",   xlim = c(500, 0),   xlab = \"kilo year BP\",   ylab = expression(delta^{18}*\"O\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":null,"dir":"Reference","previous_headings":"","what":"Medieval Ceramics from the Loire Basin \u2014 loire","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"dataset containing ceramic counts Loire Basin (France).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"","code":"loire","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"data.frame 332 rows (archaeological assemblages) 331 variables. first five columns provide background information, next columns give MNI ceramic types: site Name archaeological site. city City. area Geographical area. lower Lower bound temporal range. upper Upper bound temporal range.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"https://ceramedvaldeloire.huma-num.fr/editions/suppl79racf2022/accueil","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"Husi, P. (dir.). (2022). La c\u00e9ramique m\u00e9di\u00e9vale et moderne du bassin de la Loire moyenne, chrono-typologie et transformation des aires culturelles dans la longue dur\u00e9e (6e-19e s.). Suppl\u00e9ments \u00e0 la revue Arch\u00e9ologique du Centre de la France, 79.","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/merzbach.html","id":null,"dir":"Reference","previous_headings":"","what":"Merzbach Ceramics \u2014 merzbach","title":"Merzbach Ceramics \u2014 merzbach","text":"dataset containing ceramic counts Merzbach assemblage (Germany). data grouped eight phases.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/merzbach.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merzbach Ceramics \u2014 merzbach","text":"","code":"merzbach","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/merzbach.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Merzbach Ceramics \u2014 merzbach","text":"data.frame 8 rows (phases, numbered VII XIV) 36 variables (pottery motifs).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/merzbach.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Merzbach Ceramics \u2014 merzbach","text":"Crema, E. R. (2016). Sample codes data \"Revealing patterns cultural transmission frequency data: equilibrium non-equilibrium assumptions\". Zenodo, v1.0. doi:10.5281/zenodo.187558 .","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/merzbach.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Merzbach Ceramics \u2014 merzbach","text":"Crema, E. R., Kandler, . & Shennan, S. (2016). Revealing Patterns Cultural Transmission Frequency Data: Equilibrium Non-Equilibrium Assumptions. Scientific Reports, 6(1). doi:10.1038/srep39122 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/mississippi.html","id":null,"dir":"Reference","previous_headings":"","what":"Mississippi Ceramics \u2014 mississippi","title":"Mississippi Ceramics \u2014 mississippi","text":"dataset containing ceramic counts Mississippi region.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/mississippi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mississippi Ceramics \u2014 mississippi","text":"","code":"mississippi","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/mississippi.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mississippi Ceramics \u2014 mississippi","text":"data.frame 20 rows 10 variables (ceramic types): ParkinPunctate  BartonKentMPI  Painted  FortuneNoded  RanchIncised  WallsEngraved  WallaceIncised  RhodesIncised  VernonPaulApplique  HullEngraved","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/mississippi.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mississippi Ceramics \u2014 mississippi","text":"Lipo, C. P., Madsen, M. E. & Dunnell, R. C. (2015). Theoretically-Sufficient Computationally-Practical Technique Deterministic Frequency Seriation. PLOS ONE, 10(4), e0124942. doi:10.1371/journal.pone.0124942 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/munsingen.html","id":null,"dir":"Reference","previous_headings":"","what":"M\u00fcnsingen Cemetery \u2014 munsingen","title":"M\u00fcnsingen Cemetery \u2014 munsingen","text":"dataset data set artifact presence/absence Celtic M\u00fcnsingen-Rain cemetery (Switzerland).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/munsingen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"M\u00fcnsingen Cemetery \u2014 munsingen","text":"","code":"munsingen","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/munsingen.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"M\u00fcnsingen Cemetery \u2014 munsingen","text":"data.frame 59 rows (graves) 70 variables (artifacts).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/munsingen.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"M\u00fcnsingen Cemetery \u2014 munsingen","text":"Hodson, F. R. (1968). La Tene Cemetery M\u00fcnsingen-Rain. St\u00e4mpfli, Bern. Kendall, D. G. (1971). Seriation abundance matrices. Hodson, F. R., Kendall, D. G. Tautu, P. (eds), Mathematics Archaeological Historical Sciences. Edinburgh University Press, Edinburgh, 215-232.","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/munsingen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"M\u00fcnsingen Cemetery \u2014 munsingen","text":"","code":"heatmap(   x = as.matrix(munsingen),   Rowv = NA,   Colv = NA,   scale = \"none\",   col = c(\"white\", \"black\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":null,"dir":"Reference","previous_headings":"","what":"NGRIP 50-year Average \u2014 ngrip2004","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"50-year averaged oxygen isotope data North Greenland Ice Core Project (ss09sea time scale).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"","code":"ngrip2004","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"data.frame 2 variables: age Calendar age (years 2000 AD), ss09sea time scale. delta \\(\\delta^{18}O\\) (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"https://www.ncei.noaa.gov/access/paleo-search/study/2481","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"North Greenland Ice Core Project members (2004). High-resolution record Northern Hemisphere climate extending last interglacial period. Nature, 431(7005), 147-151. doi:10.1038/nature02805","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"","code":"plot(   x = ngrip2004$age / 1000,   y = ngrip2004$delta,   type = \"l\",   xlim = c(120, 0),   xlab = \"ss09sea (ka b2k)\",   ylab = expression(delta^{18}*\"O\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":null,"dir":"Reference","previous_headings":"","what":"NGRIP 20-year Average \u2014 ngrip2010","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"20-year averaged oxygen isotope data North Greenland Ice Core Project (GICC05 time scale).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"","code":"ngrip2010","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"data.frame 4 variables: age Calendar age (years 2000 AD), GICC05 time scale (GICC05modelext going beyond 60 ka b2k). depth (meters). delta \\(\\delta^{18}O\\) (per mil). MCE Maximum counting error (years).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"https://www.iceandclimate.nbi.ku.dk/data/","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"Use labels GICC05 (GICC05modelext going beyond 60 ka b2k) graphs.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"Vinther, B. M., Clausen, H. B., Johnsen, S. J., Rasmussen, S. O., Andersen, K. K., Buchardt, S. L., Dahl-Jensen, D., Seierstad, . K., Siggaard-Andersen, M.-L., Steffensen, J. P., Svensson, . M., Olsen, J. & Heinemeier, J. (2006). synchronized dating three Greenland ice cores throughout Holocene. Journal Geophysical Research, 111, D13102. doi:10.1029/2005JD006921 . Rasmussen, S. O., Andersen, K. K., Svensson, . M., Steffensen, J. P., Vinther, B. M., Clausen, H. B., Siggaard-Andersen, M.-L., Johnsen, S. J., Larsen, L. B., Dahl-Jensen, D., Bigler, M., R\u00f6thlisberger, R., Fischer, H., Goto-Azuma, K., Hansson, M. E. & Ruth, U. (2006). new Greenland ice core chronology last glacial termination. Journal Geophysical Research, 111, D06102. doi:10.1029/2005JD006079 . Andersen, K. K., Svensson, ., Johnsen, S. J., Rasmussen, S. O., Bigler, M., R\u00f6thlisberger, R., Ruth, U., Siggaard-Andersen, M.-L., Steffensen, J. P., Dahl-Jensen, D., Vinther, B. M. & Clausen, H.B. (2005). Greenland Ice Core Chronology 2005, 15-42 ka. Part 1: Constructing time scale. Quaternary Science Reviews, 25(23-24):3246-3257. doi:10.1016/j.quascirev.2006.08.002 . Svensson, ., Andersen, K. K., Bigler, M., Clausen, H. B., Dahl-Jensen, D., Davies, S. M., Johnsen, S. J., Muscheler, R., Rasmussen, S. O., R\u00f6thlisberger, R., Seierstad, ., Steffensen, J. P. & Vinther, B. M. (2008). 60,000 year Greenland stratigraphic ice core chronology. Climate Past, 4:47\u201357. doi:10.5194/cp-4-47-2008 . Wolff, E. W., Chappellaz, J., Blunier, T., Rasmussen, S. O. & Svensson, . (2010). Millennial-scale variability last glacial: ice core record. Quaternary Science Reviews, 29:2828-2838. doi:10.1016/j.quascirev.2009.10.013 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"","code":"plot(   x = ngrip2010$age / 1000,   y = ngrip2010$delta,   type = \"l\",   xlim = c(120, 0),   xlab = \"GICC05\",   ylab = expression(delta^{18}*\"O\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/nydal1996.html","id":null,"dir":"Reference","previous_headings":"","what":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","title":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","text":"Corrected \\({}^{14}C\\) measurements air samples collected five Norwegian sites 1962-1993.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/nydal1996.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","text":"","code":"nydal1996","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/nydal1996.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","text":"data.frame 5 variables: site Sampling station. start Beginning date sampling period. end Ending date sampling period. delta Isotopic ratio \\(\\Delta^{14}C\\) (per mil). sigma Error delta (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/nydal1996.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","text":"Nydal, R. L\u00f6vseth, K. (1996). Carbon-14 Measurements Atmospheric CO2 Northern Southern Hemisphere Sites, 1962-1993. ORNL/CDIAC-93; NDP-057. Washington, DC: USDOE Office Energy Research. doi:10.2172/461185","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/nydal1996.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","text":"","code":"plot(   x = nydal1996$start,   y = nydal1996$delta,   type = \"p\",   xlab = \"Date\",   ylab = expression(Delta^{14}*\"C\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":null,"dir":"Reference","previous_headings":"","what":"Mediterranean Shipwrecks \u2014 shipwrecks","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"dataset mediterranean shipwrecks.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"","code":"shipwrecks","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"data.frame 1784 rows 13 variables: name Wreck name. sea Region sea wreck discovered. country Country wreck discovered. region Region wreck discovered. depth_min Minimum depth wreck (m). depth_max Maximum depth wreck (m). depth Depth wreck (m). period Period. dating Dating. date_early Earliest date. date_late Latest date. origin Place origin. destination Place destination.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"Strauss, J. (2013). Shipwrecks Database. Version 1.0. Accessed 2022-08-13. URL: http://oxrep.classics.ox.ac.uk/databases/shipwrecks_database/","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"dataset contains typos needs normalized.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"Parker, . J. (1992). Ancient Shipwrecks Mediterranean Roman Provinces. British Archaeological Reports International Series 580. Oxford. Strauss, E. J. (2007). Roman Cargoes: Underwater Evidence Eastern Mediterranean. Doctoral thesis, University College London. URL: https://discovery.ucl.ac.uk/id/eprint/1349806.","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":null,"dir":"Reference","previous_headings":"","what":"Late Pleistocene Sea Level Stack \u2014 spratt2016","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"Late Pleistocene sea level stack based marine sediment core data (foraminiferal carbonate \\(\\delta^{18}O\\)).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"","code":"spratt2016","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"data.frame 9 variables: age_calkaBP Age (calendar kilo year BP). SeaLev_shortPC1 Sea Level (meters present day), climate reconstructions (scaled first principal component seven sea level reconstructions (0-430 ka)). SeaLev_shortPC1_err_sig Sea Level standard deviation bootstrap (meters), climate reconstructions (scaled first principal component seven sea level reconstructions (0-430 ka)). SeaLev_shortPC1_err_lo Sea Level 95% confidence interval lower bound (meters), climate reconstructions (scaled first principal component seven sea level reconstructions (0-430 ka)). SeaLev_shortPC1_err_up Sea Level 95% confidence interval upper bound (meters), climate reconstructions (scaled first principal component seven sea level reconstructions (0-430 ka)). SeaLev_longPC1 Sea Level (meters present day), climate reconstructions (scaled first principal component five sea level reconstructions (0-798 ka)). SeaLev_longPC1_err_sig Sea Level standard deviation bootstrap (meters), climate reconstructions (scaled first principal component five sea level reconstructions (0-798 ka)). SeaLev_longPC1_err_lo Sea Level 95% confidence interval lower bound (meters), climate reconstructions (scaled first principal component five sea level reconstructions (0-798 ka)). SeaLev_longPC1_err_up Sea Level 95% confidence interval upper bound (meters), climate reconstructions (scaled first principal component five sea level reconstructions (0-798 ka)).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"https://www.ncei.noaa.gov/access/paleo-search/study/19982","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"Spratt, R. M. Lisiecki, L. E. (2016). Late Pleistocene sea level stack. Climate Past, 12, 1079-1092. doi:10.5194/cp-12-1079-2016","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"","code":"plot(   x = spratt2016$age_calkaBP,   y = spratt2016$SeaLev_longPC1,   type = \"l\",   xlim = c(500, 0),   xlab = \"kilo year BP\",   ylab = \"Sea level (meters above present)\" )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":null,"dir":"Reference","previous_headings":"","what":"Chronostratigraphic Chart \u2014 stratigraphy","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"ICS international chronostratigraphic chart (v2022/2).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"","code":"stratigraphy","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"data.frame 5 variables: type Unit type (\"eon\", \"era\", \"period\", \"series\" \"stage\"). Precambrian Hadean informal units. name Unit name. age Numerical age (Ma). error Error numerical age (Ma). parent Parent unit.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"https://stratigraphy.org/ICSchart/ChronostratChart2022-02.pdf","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"Cohen, K. M., Finney, S. C., Gibbard, P. L. Fan, J.-X. (2013). ICS International Chronostratigraphic Chart. Episodes, 36(3): 199-204. doi:10.18814/epiiugs/2013/v36i3/002","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":null,"dir":"Reference","previous_headings":"","what":"French Medieval Glass Composition \u2014 verre","title":"French Medieval Glass Composition \u2014 verre","text":"Chemical analysis (electron probe X-ray micro analysis) 398 medieval glass vessels found France.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"French Medieval Glass Composition \u2014 verre","text":"","code":"verre","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"French Medieval Glass Composition \u2014 verre","text":"data.frame 17 variables: Site CNL: Cour Napol\u00e9on, Louvre; ORL: Orl\u00e9ans; POI: Poitiers; ANG: Angers; OMO: Omonville, Seine Maritime; ROU: Rouen; MEA: Meaux; CHL: Ch\u00e2lons-sur-Marne; PAI: Pairu (Argonne, Ardennes); BER: Bercettes (Argonne, Ardennes); BIN: Binois (Argonne, Ardennes); CHE: Chevrie (Argonne, Ardennes); MIT: Mitte (Argonne, Ardennes); MET: Metz; CHM: Chambaran. Sample Sample code. Type Typology. Age Century. Periode : 9th-12th century; II: 13th-first half 15th century; III: end 15th end 16th century; IV: end 16th end 17th century. Tint B: blue; CL: colourless; CLg colourless (greyish tint); PB: pale blue; PGE: pale greenish; PGE-B: pale green-blue blue-green; PGY-B: pale grey-blue; R: opaque red; W: opaque white; *av: added aventurine spots; *bl: added thread blue blue spots; *r: added thread opaque red opaque red spots; *w: added thread opaque white. Na2O Na2O content (percent). CaO CaO content (percent). K2O K2O content (percent). MgO MgO content (percent). P2O5 P2O5 content (percent). SiO2 SiO2 content (percent). Al2O3 Al2O3 content (percent). FeO FeO content (percent). MnO MnO content (percent). Cl Cl content (percent). Reference Site reference.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"French Medieval Glass Composition \u2014 verre","text":"Barrera J., Velde B. (1989). study french medieval glass composition. Arch\u00e9ologie m\u00e9di\u00e9vale, 19, 81-130. doi:10.3406/arcme.1989.953 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"French Medieval Glass Composition \u2014 verre","text":"","code":"plot(   x = verre$Na2O,   y = verre$CaO / (verre$CaO + verre$K2O),   type = \"p\",   xlab = expression(\"Na\"[2]*\"O (%)\"),   ylab = expression(\"CaO\"/\"(CaO\"+\"K\"[2]*\"O)\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/zuni.html","id":null,"dir":"Reference","previous_headings":"","what":"Zuni Ceramics \u2014 zuni","title":"Zuni Ceramics \u2014 zuni","text":"dataset containing ceramic counts Zuni region American Southwest.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/zuni.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zuni Ceramics \u2014 zuni","text":"","code":"zuni","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/zuni.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Zuni Ceramics \u2014 zuni","text":"data.frame 420 rows (assemblages) 18 variables (ceramic types). numbers brackets correspond date range type (AD years): LINO Lino Gray (575-875). KIAT Kiatuthlanna Black--white (850-910). RED Red Mesa Black--white (900-1030). GALL Gallup Black--white (1025-1150). ESC Escavada Black--white (1050-1150). PUBW Puerco Black--white (1050-1200). RES Reserve Black--white (1071-1115). TULA Tularosa Black--white (1175-1300). PINE Pinedale Black--white (1275-1325). PUBR Puerco Black--red (1050-1200). WING Wingate Black--red (1070-1200). WIPO Wingate Polychrome (1150-1250). SJ St. Johns Black--red/Polychrome (1200-1300). LSJ St. Johns glaze, Techado Polychrome (1275-1300). SPR Springerville Polychrome (1250-1300). PINER Pinedale Black--red/Polychrome (1275-1325). HESH Heshotauthla Polychrome (1285-1400). KWAK Kwakina Polychrome (1285-1400).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/zuni.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Zuni Ceramics \u2014 zuni","text":"Peeples, M. ., & Schachner, G. (2012). Refining correspondence analysis-based ceramic seriation regional data sets. Journal Archaeological Science, 39(8), 2818-2827. doi:10.1016/j.jas.2012.04.040 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"get-started","dir":"Articles","previous_headings":"","what":"Get started","title":"Manual","text":"can install released version nexus CRAN : installed, load package start working: nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e.\u00a0quantitative (nonnegative) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986; Greenacre 2021). assumes keep data tidy: variable must saved column observation (sample) must saved row. class simple use inherits base matrix: CompositionMatrix represents closed composition matrix: row matrix sum 1 (relative changes relevant compositional data analysis). original row sums kept internally, source data can restored: get_totals(x) set_totals(x) <- value allow retrieve modify row sums existing CompositionMatrix.","code":"install.packages(\"nexus\") library(nexus) #>  #> Attaching package: 'nexus' #> The following object is masked from 'package:stats': #>  #>     biplot ## Mineral compositions of rock specimens data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Coerce to count data counts <- as_amounts(coda)  all.equal(hongite, as.data.frame(counts)) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-reference-groups","dir":"Articles","previous_headings":"Get started","what":"Working with (reference) groups","title":"Manual","text":"Provenance studies typically rely two approaches, can used together: Identification groups among artifacts studied, based mineralogical geochemical criteria (clustering). Comparison -called reference groups, .e.\u00a0known geological sources productive contexts (classification). nexus allows specify whether observation belongs specific group (). coercing data.frame CompositionMatrix object, attempt made automatically detect groups mapping column names. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used groups argument as_composition(). get_groups(x) set_groups(x) <- value allow retrieve set groups existing CompositionMatrix (NA can used specify sample belong group): groups defined, can used methods (e.g.\u00a0plotting).","code":"## Create a data.frame X <- data.frame(   groups = c(\"A\", \"A\", \"B\", \"A\", \"B\", \"C\", \"C\", \"C\", \"B\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) has_groups(Y) #> [1] TRUE ## Set groups (NA means no group) set_groups(Y) <- c(\"X\", \"X\", \"Y\", \"X\", \"Y\", NA, NA, NA, \"Y\")  ## Retrieve groups get_groups(Y) #> [1] \"X\" \"X\" \"Y\" \"X\" \"Y\" NA  NA  NA  \"Y\"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-repeated-measurements","dir":"Articles","previous_headings":"Get started","what":"Working with repeated measurements","title":"Manual","text":"situations, measurements may repeated (e.g.\u00a0multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. coercing data.frame CompositionMatrix object, attempt made automatically detect samples mapping column names. matching column found, row names used default. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used samples argument as_composition(). get_samples(x) set_samples(x) <- value allow retrieve set sample names existing CompositionMatrix (missing values allowed): Note presence repeated measurements may affect calculations (read documentation carefully).","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) has_replicates(Y) #> [1] TRUE ## Set sample names set_samples(Y) <- c(\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\")  ## Retrieve groups get_samples(Y) #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\"","package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"nexus-","dir":"","previous_headings":"","what":"Sourcing Archaeological Materials by Chemical Composition","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Exploration analysis compositional data framework Aitchison (1986). package provides tools chemical fingerprinting source tracking ancient materials. Initial development progress.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Sourcing Archaeological Materials by Chemical Composition","text":"can install released version nexus CRAN : development version GitHub :","code":"install.packages(\"nexus\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/nexus\")","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Sourcing Archaeological Materials by Chemical Composition","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e.\u00a0quantitative (nonnegative) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986). assumes keep data tidy: variable must saved column observation (sample) must saved row. new classes simple use inherit base matrix: CompositionMatrix class special slots (see vignette(\"manual\")): samples repeated measurements/observation, groups group data site/area. coercing data.frame CompositionMatrix object, attempt made automatically assign values slots mapping column names. behavior can disabled setting options(nexus.autodetect = FALSE).","code":"## Load the package library(nexus) #>  #> Attachement du package : 'nexus' #> L'objet suivant est masqu\u00e9 depuis 'package:stats': #>  #>     biplot ## Mineral compositions of rock specimens data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   groups = c(\"X\", \"X\", \"X\", \"X\", \"X\", \"X\", \"Y\", \"Y\", \"Y\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Get samples get_samples(Y) #> [1] \"A\" \"A\" \"A\" \"B\" \"B\" \"B\" \"C\" \"C\" \"C\"  ## Get groups get_groups(Y) #> [1] \"X\" \"X\" \"X\" \"X\" \"X\" \"X\" \"Y\" \"Y\" \"Y\"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Please note nexus project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Matrix \u2014 CompositionMatrix-class","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"S4 class represent compositional data.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"totals numeric vector store absolute row sums (closure compositions). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names ().","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"class inherits matrix.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## codaccess dim(coda) # Get the matrix dimensions #> [1] 25  5 row(coda) # Get the row indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,]    1    1    1    1    1 #>  [2,]    2    2    2    2    2 #>  [3,]    3    3    3    3    3 #>  [4,]    4    4    4    4    4 #>  [5,]    5    5    5    5    5 #>  [6,]    6    6    6    6    6 #>  [7,]    7    7    7    7    7 #>  [8,]    8    8    8    8    8 #>  [9,]    9    9    9    9    9 #> [10,]   10   10   10   10   10 #> [11,]   11   11   11   11   11 #> [12,]   12   12   12   12   12 #> [13,]   13   13   13   13   13 #> [14,]   14   14   14   14   14 #> [15,]   15   15   15   15   15 #> [16,]   16   16   16   16   16 #> [17,]   17   17   17   17   17 #> [18,]   18   18   18   18   18 #> [19,]   19   19   19   19   19 #> [20,]   20   20   20   20   20 #> [21,]   21   21   21   21   21 #> [22,]   22   22   22   22   22 #> [23,]   23   23   23   23   23 #> [24,]   24   24   24   24   24 #> [25,]   25   25   25   25   25 col(coda, as.factor = TRUE) # Get the column indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,] A    B    C    D    E    #>  [2,] A    B    C    D    E    #>  [3,] A    B    C    D    E    #>  [4,] A    B    C    D    E    #>  [5,] A    B    C    D    E    #>  [6,] A    B    C    D    E    #>  [7,] A    B    C    D    E    #>  [8,] A    B    C    D    E    #>  [9,] A    B    C    D    E    #> [10,] A    B    C    D    E    #> [11,] A    B    C    D    E    #> [12,] A    B    C    D    E    #> [13,] A    B    C    D    E    #> [14,] A    B    C    D    E    #> [15,] A    B    C    D    E    #> [16,] A    B    C    D    E    #> [17,] A    B    C    D    E    #> [18,] A    B    C    D    E    #> [19,] A    B    C    D    E    #> [20,] A    B    C    D    E    #> [21,] A    B    C    D    E    #> [22,] A    B    C    D    E    #> [23,] A    B    C    D    E    #> [24,] A    B    C    D    E    #> [25,] A    B    C    D    E    #> Levels: A B C D E nrow(coda) # Get the number of rows #> [1] 25 ncol(coda) # Get the number of columns #> [1] 5 dimnames(coda) # Get the dimension names #> [[1]] #>  [1] \"H1\"  \"H2\"  \"H3\"  \"H4\"  \"H5\"  \"H6\"  \"H7\"  \"H8\"  \"H9\"  \"H10\" \"H11\" \"H12\" #> [13] \"H13\" \"H14\" \"H15\" \"H16\" \"H17\" \"H18\" \"H19\" \"H20\" \"H21\" \"H22\" \"H23\" \"H24\" #> [25] \"H25\" #>  #> [[2]] #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" #>  rownames(coda) <- LETTERS[1:25] # Set the row names rownames(coda) # Get the rownames #>  [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\" \"J\" \"K\" \"L\" \"M\" \"N\" \"O\" \"P\" \"Q\" \"R\" \"S\" #> [20] \"T\" \"U\" \"V\" \"W\" \"X\" \"Y\" colnames(coda) <- letters[21:25] # Set the column names colnames(coda) # Get the column names #> [1] \"u\" \"v\" \"w\" \"x\" \"y\"  ## Subset coda[[1, 1]] # Get the first value #> [1] 0.488 coda[1] # Get the first value #> [1] 0.488 coda[, ] # Get all values #> <CompositionMatrix: 25 x 5> #>           u         v          w          x          y #> A 0.4880000 0.3170000 0.03800000 0.06400000 0.09300000 #> B 0.4820000 0.2380000 0.09000000 0.09200000 0.09800000 #> C 0.3700000 0.0910000 0.34200000 0.09500000 0.10200000 #> D 0.5090000 0.2380000 0.07200000 0.10100000 0.08000000 #> E 0.4420000 0.3830000 0.02900000 0.07700000 0.06900000 #> F 0.5230000 0.2620000 0.04200000 0.12500000 0.04800000 #> G 0.4460000 0.3300000 0.04600000 0.12200000 0.05600000 #> H 0.3460000 0.0520000 0.42900000 0.09600000 0.07700000 #> I 0.4120000 0.1170000 0.26700000 0.09600000 0.10800000 #> J 0.4260000 0.4660000 0.00700000 0.05600000 0.04500000 #> K 0.4990000 0.1950000 0.11400000 0.09500000 0.09700000 #> L 0.4520000 0.3730000 0.02700000 0.05500000 0.09300000 #> M 0.3270000 0.0850000 0.38900000 0.08000000 0.11900000 #> N 0.4140000 0.1290000 0.23400000 0.15800000 0.06500000 #> O 0.4620000 0.1750000 0.15800000 0.08300000 0.12200000 #> P 0.3230000 0.0730000 0.40900000 0.12900000 0.06600000 #> Q 0.4320000 0.4430000 0.01000000 0.07800000 0.03700000 #> R 0.4954955 0.3233233 0.03103103 0.08708709 0.06306306 #> S 0.4230000 0.1580000 0.20400000 0.08300000 0.13200000 #> T 0.4460000 0.1150000 0.23800000 0.11600000 0.08500000 #> U 0.4580000 0.1660000 0.16800000 0.12000000 0.08800000 #> V 0.4990000 0.2500000 0.06800000 0.10900000 0.07400000 #> W 0.4860000 0.3400000 0.02500000 0.09400000 0.05500000 #> X 0.4550000 0.1660000 0.17600000 0.09600000 0.10700000 #> Y 0.4590000 0.2490000 0.09700000 0.09800000 0.09700000 coda[1, , drop = FALSE] # Get the first row #> <CompositionMatrix: 1 x 5> #>       u     v     w     x     y #> A 0.488 0.317 0.038 0.064 0.093 coda[, 1:3] # Get the first three column #> <CompositionMatrix: 25 x 3> #>           u         v          w #> A 0.4880000 0.3170000 0.03800000 #> B 0.4820000 0.2380000 0.09000000 #> C 0.3700000 0.0910000 0.34200000 #> D 0.5090000 0.2380000 0.07200000 #> E 0.4420000 0.3830000 0.02900000 #> F 0.5230000 0.2620000 0.04200000 #> G 0.4460000 0.3300000 0.04600000 #> H 0.3460000 0.0520000 0.42900000 #> I 0.4120000 0.1170000 0.26700000 #> J 0.4260000 0.4660000 0.00700000 #> K 0.4990000 0.1950000 0.11400000 #> L 0.4520000 0.3730000 0.02700000 #> M 0.3270000 0.0850000 0.38900000 #> N 0.4140000 0.1290000 0.23400000 #> O 0.4620000 0.1750000 0.15800000 #> P 0.3230000 0.0730000 0.40900000 #> Q 0.4320000 0.4430000 0.01000000 #> R 0.4954955 0.3233233 0.03103103 #> S 0.4230000 0.1580000 0.20400000 #> T 0.4460000 0.1150000 0.23800000 #> U 0.4580000 0.1660000 0.16800000 #> V 0.4990000 0.2500000 0.06800000 #> W 0.4860000 0.3400000 0.02500000 #> X 0.4550000 0.1660000 0.17600000 #> Y 0.4590000 0.2490000 0.09700000","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-Ratio Matrix \u2014 LogRatio-class","title":"Log-Ratio Matrix \u2014 LogRatio-class","text":"S4 classes represent log-ratio data transformations.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Log-Ratio Matrix \u2014 LogRatio-class","text":"totals numeric vector store absolute row sums (closure compositions). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). parts character vector store part names. ratio character vector store ratio names. order integer vector store original ordering columns. base numeric matrix store basis transformation. weights numeric vector store weights assigned respective log-ratios.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Log-Ratio Matrix \u2014 LogRatio-class","text":"classes inherit matrix.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Log-Ratio Matrix \u2014 LogRatio-class","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical Matrix \u2014 LogicalMatrix-class","title":"Logical Matrix \u2014 LogicalMatrix-class","text":"S4 classes represent \\(m \\times p\\) logical matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logical Matrix \u2014 LogicalMatrix-class","text":"class inherits matrix.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Logical Matrix \u2014 LogicalMatrix-class","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Numeric Matrix \u2014 NumericMatrix-class","title":"Numeric Matrix \u2014 NumericMatrix-class","text":"S4 classes represent \\(m \\times p\\) numeric matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Numeric Matrix \u2014 NumericMatrix-class","text":"class inherits matrix.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Numeric Matrix \u2014 NumericMatrix-class","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Outliers \u2014 OutlierIndex-class","title":"Outliers \u2014 OutlierIndex-class","text":"S4 class store result outlier detection.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Outliers \u2014 OutlierIndex-class","text":"samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). distances numeric vector giving squared Mahalanobis distance. limit numeric value giving cut-value used outlier detection. robust logical scalar: robust estimators used?","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Outliers \u2014 OutlierIndex-class","text":"classes inherit logical.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outliers \u2014 OutlierIndex-class","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Summary Statistics of Data Subsets \u2014 aggregate","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"Splits data subsets, computes summary statistics , returns result.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"","code":"# S4 method for CompositionMatrix aggregate(x, by, FUN, ...)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"x CompositionMatrix object. character string specifying grouping element. must one \"samples\" \"groups\". unambiguous substring can given. FUN function compute summary statistics. ... arguments passed FUN.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"matrix.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   groups = c(\"X\", \"X\", \"X\", \"X\", \"X\", \"X\", \"Y\", \"Y\", \"Y\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Compositional mean by sample aggregate(Y, by = \"samples\", FUN = mean) #> Warning: No observations are repeated. #>          Ca        Fe         Na #> A 0.4468157 0.4539051 0.09927924 #> B 0.4746506 0.4742013 0.05114805 #> C 0.3067545 0.6238351 0.06941036  ## Compositional mean by group aggregate(Y, by = \"groups\", FUN = mean) #>          Ca        Fe         Na #> X 0.4625002 0.4659342 0.07156562 #> Y 0.3067545 0.6238351 0.06941036","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to Amounts \u2014 as_amounts","title":"Coerce to Amounts \u2014 as_amounts","text":"Coerce Amounts","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to Amounts \u2014 as_amounts","text":"","code":"as_amounts(from, ...)  # S4 method for CompositionMatrix as_amounts(from)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to Amounts \u2014 as_amounts","text":"CompositionMatrix object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to Amounts \u2014 as_amounts","text":"numeric  matrix.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to Amounts \u2014 as_amounts","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to Amounts \u2014 as_amounts","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies ## (use get_totals() to retrieve these values) ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to an S3 data.frame X <- data.frame(B) head(X) #>           col1       col2       col3       col4      col5 samples #> row1 0.2179487 0.01282051 0.08974359 0.09615385 0.5833333    row1 #> row2 0.3892216 0.02994012 0.13173653 0.07784431 0.3712575    row2 #> row3 0.2554348 0.03260870 0.19293478 0.27173913 0.2472826    row3 #> row4 0.1983914 0.10187668 0.25201072 0.19839142 0.2493298    row4 #> row5 0.3318182 0.10454545 0.40454545 0.03636364 0.1227273    row5 #> row6 0.2474227 0.05841924 0.22680412 0.27835052 0.1890034    row6","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Closed Compositional Matrix \u2014 as_composition","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"Coerces object CompositionMatrix object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"","code":"as_composition(from, ...)  # S4 method for matrix as_composition(from)  # S4 method for data.frame as_composition(from, samples = NULL, groups = NULL)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"matrix data.frame coerced. ... Currently used. samples integer giving index column used sample identification: allows identify replicated measurements. NULL (default), row names used sample IDs. groups integer giving index column used group samples. NULL (default), grouping stored.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"CompositionMatrix object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"CompositionMatrix class special slots (see vignette(\"manual\")): samples repeated measurements/observation, groups group data site/area. coercing data.frame CompositionMatrix object, attempt made automatically assign values slots mapping column names (case insensitive, plural insensitive). behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying columns used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"non-numeric variable removed.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies ## (use get_totals() to retrieve these values) ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to an S3 data.frame X <- data.frame(B) head(X) #>            col1      col2      col3       col4       col5 samples #> row1 0.06250000 0.2416667 0.3000000 0.33750000 0.05833333    row1 #> row2 0.19217082 0.1530249 0.2526690 0.12811388 0.27402135    row2 #> row3 0.28630705 0.1203320 0.3734440 0.02489627 0.19502075    row3 #> row4 0.25885559 0.1771117 0.1035422 0.19618529 0.26430518    row4 #> row5 0.05666667 0.2033333 0.2400000 0.16666667 0.33333333    row5 #> row6 0.27483444 0.1788079 0.2880795 0.22847682 0.02980132    row6","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph of Log-ratios \u2014 as_graph","title":"Graph of Log-ratios \u2014 as_graph","text":"Produces graph log-ratios.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph of Log-ratios \u2014 as_graph","text":"","code":"as_graph(object, ...)  # S4 method for LR as_graph(object)  # S4 method for ALR as_graph(object)  # S4 method for ILR as_graph(object)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph of Log-ratios \u2014 as_graph","text":"object LogRatio object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph of Log-ratios \u2014 as_graph","text":"igraph graph object.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Graph of Log-ratios \u2014 as_graph","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph of Log-ratios \u2014 as_graph","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda) lr_graph <- as_graph(lr) plot(lr_graph)   ## Additive log-ratio alr <- transform_alr(coda) alr_graph <- as_graph(alr) plot(alr_graph)   ## Isometric log-ratio ilr <- transform_ilr(coda) ilr_graph <- as_graph(ilr) plot(ilr_graph)   plr <- transform_plr(coda) plr_graph <- as_graph(plr) plot(plr_graph)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":null,"dir":"Reference","previous_headings":"","what":"Closure Operation \u2014 closure","title":"Closure Operation \u2014 closure","text":"Closes compositions sum 1.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Closure Operation \u2014 closure","text":"","code":"closure(object, ...)  # S4 method for numeric closure(object, total = 1)  # S4 method for matrix closure(object, total = 1)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Closure Operation \u2014 closure","text":"object numeric vector matrix. ... Currently used. total numeric vector specifying total amount compositions closed (defaults 1).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Closure Operation \u2014 closure","text":"numeric vector matrix (object).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"missing-values-policy","dir":"Reference","previous_headings":"","what":"Missing Values Policy","title":"Closure Operation \u2014 closure","text":"Missing values omitted calculations.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Closure Operation \u2014 closure","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Closure Operation \u2014 closure","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies ## (use get_totals() to retrieve these values) ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to an S3 data.frame X <- data.frame(B) head(X) #>            col1       col2       col3       col4       col5 samples #> row1 0.24137931 0.31034483 0.28017241 0.13362069 0.03448276    row1 #> row2 0.01069519 0.06951872 0.52406417 0.10695187 0.28877005    row2 #> row3 0.20503597 0.12589928 0.33812950 0.10791367 0.22302158    row3 #> row4 0.35684647 0.12863071 0.15767635 0.24896266 0.10788382    row4 #> row5 0.27727273 0.15454545 0.08636364 0.39545455 0.08636364    row5 #> row6 0.12236287 0.39662447 0.16033755 0.08860759 0.23206751    row6","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance and Covariance \u2014 covariance","title":"Variance and Covariance \u2014 covariance","text":"var() computes log-ratio variance matrix. cov() computes log-ratio covariance matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance and Covariance \u2014 covariance","text":"","code":"# S4 method for CompositionMatrix var(x)  # S4 method for CompositionMatrix cov(x)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance and Covariance \u2014 covariance","text":"x CompositionMatrix object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variance and Covariance \u2014 covariance","text":"matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variance and Covariance \u2014 covariance","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variance and Covariance \u2014 covariance","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variance and Covariance \u2014 covariance","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Variance matrix var(coda) #>            A         B         C          D         E #> A 0.00000000 0.2592742 1.5328586 0.08281464 0.1385604 #> B 0.25927416 0.0000000 3.0006804 0.54727146 0.6490135 #> C 1.53285862 3.0006804 0.0000000 1.11145146 0.9476375 #> D 0.08281464 0.5472715 1.1114515 0.00000000 0.1870579 #> E 0.13856035 0.6490135 0.9476375 0.18705791 0.0000000  ## Covariance matrix cov(coda) #>             A_B        A_C         A_D         A_E        B_C         B_D #> A_B  0.25927416 -0.6042738 -0.10259133 -0.12558948 -0.8635479 -0.36186549 #> A_C -0.60427379  1.5328586  0.25211090  0.36189072  2.1371324  0.85638469 #> A_D -0.10259133  0.2521109  0.08281464  0.01715854  0.3547022  0.18540597 #> A_E -0.12558948  0.3618907  0.01715854  0.13856035  0.4874802  0.14274802 #> B_C -0.86354795  2.1371324  0.35470223  0.48748020  3.0006804  1.21825018 #> B_D -0.36186549  0.8563847  0.18540597  0.14274802  1.2182502  0.54727146 #> B_E -0.38486364  0.9661645  0.11974987  0.26414983  1.3510281  0.50461351 #> C_D  0.50168246 -1.2807477 -0.16929626 -0.34473217 -1.7824302 -0.67097871 #> C_E  0.47868431 -1.1709679 -0.23495236 -0.22333036 -1.6496522 -0.71363666 #> D_E -0.02299815  0.1097798 -0.06565610  0.12140181  0.1327780 -0.04265795 #>            B_E        C_D        C_E         D_E #> A_B -0.3848636  0.5016825  0.4786843 -0.02299815 #> A_C  0.9661645 -1.2807477 -1.1709679  0.10977982 #> A_D  0.1197499 -0.1692963 -0.2349524 -0.06565610 #> A_E  0.2641498 -0.3447322 -0.2233304  0.12140181 #> B_C  1.3510281 -1.7824302 -1.6496522  0.13277797 #> B_D  0.5046135 -0.6709787 -0.7136367 -0.04265795 #> B_E  0.6490135 -0.8464146 -0.7020147  0.14439996 #> C_D -0.8464146  1.1114515  0.9360155 -0.17543592 #> C_E -0.7020147  0.9360155  0.9476375  0.01162200 #> D_E  0.1444000 -0.1754359  0.0116220  0.18705791  ## Variation matrix variation(coda) #>           A         B           C          D         E #> A 0.0000000 0.2592742  1.53285862 0.08281464 0.1385604 #> B 0.8011244 0.0000000  3.00068035 0.54727146 0.6490135 #> C 1.5999805 0.7988561  0.00000000 1.11145146 0.9476375 #> D 1.5483008 0.7471764 -0.05167973 0.00000000 0.1870579 #> E 1.7152103 0.9140859  0.11522977 0.16690950 0.0000000","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Distances \u2014 dist","title":"Distances \u2014 dist","text":"Computes log-ratio variance matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distances \u2014 dist","text":"","code":"# S4 method for CompositionMatrix dist(x, method = \"euclidean\", diag = FALSE, upper = FALSE, p = 2)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distances \u2014 dist","text":"x CompositionMatrix object. method character string specifying distance measure used. See stats::dist() available distances. diag logical scalar indicating whether diagonal distance matrix printed. upper logical scalar indicating whether upper triangle distance matrix printed. p integer giving power Minkowski distance.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distances \u2014 dist","text":"stats::dist object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distances \u2014 dist","text":"Distances computed CLR-transformed data.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Distances \u2014 dist","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distances \u2014 dist","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distances \u2014 dist","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Distance dist(coda) #>            H1        H2        H3        H4        H5        H6        H7 #> H2  0.8767510                                                             #> H3  2.5214253 1.6650912                                                   #> H4  0.7906400 0.2952543 1.8639740                                         #> H5  0.4739924 1.1608245 2.8213666 0.9854538                               #> H6  0.9678208 1.0017438 2.4503854 0.7106924 0.8126273                     #> H7  0.8391283 0.9264506 2.4466687 0.6726300 0.6518013 0.3068355           #> H8  3.0681017 2.2077617 0.5985190 2.3755249 3.3531743 2.9031508 2.9350491 #> H9  2.1567755 1.3025730 0.3646671 1.5092037 2.4608605 2.1188602 2.1067706 #> H10 1.5855253 2.3852360 4.0452164 2.1890800 1.2401119 1.7847939 1.7285362 #> H11 1.1710927 0.3101166 1.3704961 0.5142905 1.4599034 1.1921282 1.1626052 #> H12 0.3727262 1.2405255 2.8797113 1.1509884 0.4539174 1.1923398 1.0473042 #> H13 2.6796901 1.8462061 0.2974720 2.0700018 3.0032107 2.6883648 2.6669260 #> H14 2.1787003 1.3234987 0.8580849 1.4065875 2.3581311 1.8190796 1.8478752 #> H15 1.4784947 0.6708213 1.0579099 0.9297665 1.8185451 1.6194568 1.5714340 #> H16 2.9044778 2.0288430 0.6027605 2.1709606 3.1392211 2.6453694 2.6681226 #> H17 1.4546884 2.1462022 3.7770078 1.9169098 1.0304796 1.4107166 1.3729948 #> H18 0.5245452 1.0606339 2.7007677 0.8376898 0.2629770 0.5769217 0.4884938 #> H19 1.7602135 0.9534581 0.8105519 1.2096096 2.0997516 1.8869609 1.8364379 #> H20 2.1115841 1.2415737 0.5413915 1.3933788 2.3711124 1.9319745 1.9510350 #> H21 1.6327549 0.7593779 0.9906382 0.9018811 1.8714132 1.4647356 1.4581381 #> H22 0.8008035 0.4134000 1.9478272 0.1333961 0.9287606 0.5909027 0.5497058 #> H23 0.7451072 1.2918095 2.9137094 1.0529779 0.3583952 0.6293206 0.5735426 #> H24 1.6082346 0.7503990 0.9163484 0.9700216 1.9090190 1.6170556 1.5859866 #> H25 0.9383107 0.1051583 1.6279995 0.3274461 1.1953636 1.0126284 0.9224570 #>            H8        H9       H10       H11       H12       H13       H14 #> H2                                                                        #> H3                                                                        #> H4                                                                        #> H5                                                                        #> H6                                                                        #> H7                                                                        #> H8                                                                        #> H9  0.9397076                                                             #> H10 4.5619484 3.6846865                                                   #> H11 1.9033794 1.0096405 2.6762596                                         #> H12 3.4332992 2.5152272 1.2980229 1.5387862                               #> H13 0.6382499 0.5712106 4.2301042 1.5639306 3.0279063                     #> H14 1.1707267 0.7298713 3.5473128 1.0636854 2.5411784 1.1433041           #> H15 1.6287975 0.6973619 3.0370608 0.4288520 1.8301814 1.2017611 1.0344674 #> H16 0.4545771 0.8638763 4.3488475 1.7374933 3.2690987 0.7689998 0.8360769 #> H17 4.2728994 3.4243251 0.4863777 2.4251205 1.2552674 3.9824638 3.2094091 #> H18 3.2105210 2.3441270 1.3561461 1.3391357 0.6366131 2.8999267 2.1988548 #> H19 1.3980195 0.4714590 3.3222469 0.7032641 2.1054467 0.9228103 0.9859377 #> H20 0.9877633 0.3329260 3.5815737 0.9408789 2.4792771 0.8218637 0.4308744 #> H21 1.4914563 0.6632621 3.0879216 0.4819969 1.9980537 1.2277365 0.5920798 #> H22 2.4502129 1.5980522 2.1227178 0.6233978 1.1448814 2.1615401 1.4406247 #> H23 3.4120401 2.5610218 1.1797726 1.5632549 0.7625165 3.1207729 2.3708636 #> H24 1.4738079 0.5530808 3.1343759 0.4647774 1.9682225 1.1021347 0.8059578 #> H25 2.1729555 1.2681367 2.4277793 0.3059376 1.2955966 1.8116276 1.2646069 #>           H15       H16       H17       H18       H19       H20       H21 #> H2                                                                        #> H3                                                                        #> H4                                                                        #> H5                                                                        #> H6                                                                        #> H7                                                                        #> H8                                                                        #> H9                                                                        #> H10                                                                       #> H11                                                                       #> H12                                                                       #> H13                                                                       #> H14                                                                       #> H15                                                                       #> H16 1.5257009                                                             #> H17 2.8148034 4.0297987                                                   #> H18 1.7276112 2.9936000 1.0890523                                         #> H19 0.2925366 1.3251713 3.0989710 2.0135135                               #> H20 0.7661338 0.8061360 3.2888972 2.2273992 0.6511045                     #> H21 0.4989043 1.2756316 2.7966378 1.7339926 0.5870924 0.5064689           #> H22 1.0426202 2.2267808 1.8298329 0.7673337 1.3160904 1.4643450 0.9696446 #> H23 1.9607003 3.1805225 0.8658073 0.2491895 2.2446978 2.4281713 1.9360069 #> H24 0.2290380 1.3313017 2.8816469 1.7984553 0.3052918 0.5584031 0.3003119 #> H25 0.6589617 1.9776437 2.1771982 1.0988769 0.9297336 1.2023185 0.7102152 #>           H22       H23       H24 #> H2                                #> H3                                #> H4                                #> H5                                #> H6                                #> H7                                #> H8                                #> H9                                #> H10                               #> H11                               #> H12                               #> H13                               #> H14                               #> H15                               #> H16                               #> H17                               #> H18                               #> H19                               #> H20                               #> H21                               #> H22                               #> H23 0.9671322                     #> H24 1.0663845 2.0204099           #> H25 0.4247642 1.3238750 0.7183694  ## Mahalanobis distance mahalanobis(coda) #>         H1         H2         H3         H4         H5         H6         H7  #>  1.9709079  0.8438243  0.9421982  2.8803179 59.3339067  3.2346153 97.1211672  #>         H8         H9        H10        H11        H12        H13        H14  #> 49.1635974  0.5901016 67.4288815  0.9004588  2.5335404  2.0322110 46.8670451  #>        H15        H16        H17        H18        H19        H20        H21  #>  1.0886501 48.2413850 19.9301611  1.0113684  1.0481611  2.0022595  2.3207166  #>        H22        H23        H24        H25  #>  1.9723197 24.6876086 13.4500205 19.1349096","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Deal With Groups \u2014 groups","title":"Deal With Groups \u2014 groups","text":"Retrieves defines groups observations belong.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deal With Groups \u2014 groups","text":"","code":"has_groups(x)  get_groups(x)  set_groups(x) <- value  # S4 method for CompositionMatrix has_groups(x)  # S4 method for LogRatio has_groups(x)  # S4 method for OutlierIndex has_groups(x)  # S4 method for CompositionMatrix get_groups(x)  # S4 method for LogRatio get_groups(x)  # S4 method for OutlierIndex get_groups(x)  # S4 method for CompositionMatrix set_groups(x) <- value","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deal With Groups \u2014 groups","text":"x object get set groups. value possible value groups x.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Deal With Groups \u2014 groups","text":"set_groups() returns object sort x new group names assigned. get_groups() returns group names x. has_groups() returns logical scalar.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Deal With Groups \u2014 groups","text":"See vignette(\"manual\").","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Deal With Groups \u2014 groups","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":null,"dir":"Reference","previous_headings":"","what":"Hongite Mineralogy \u2014 hongite","title":"Hongite Mineralogy \u2014 hongite","text":"Mineral compositions 25 rock specimens hongite type.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hongite Mineralogy \u2014 hongite","text":"","code":"hongite","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hongite Mineralogy \u2014 hongite","text":"data.frame 5 variables (minerals): Albite (percent). B Blandite (percent). C Cornite (percent). D Daubite (percent). E Endite (percent).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hongite Mineralogy \u2014 hongite","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/lia_age.html","id":null,"dir":"Reference","previous_headings":"","what":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"Compute geological model age (T) U/Pb (mu) Th/U (kappa) ratios lead isotopic measurements.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/lia_age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"","code":"lia_age(x, y, z, ...)  # S4 method for numeric,numeric,numeric lia_age(   x,   y,   z,   t0 = 3.8,   x_star = 18.75,   y_star = 15.63,   z_star = 38.86,   mu = 9.66,   kappa = 3.9,   th232 = 0.049475,   u238 = 0.155125,   u235 = 0.98485,   u238_235 = 137.79,   tolerance = sqrt(.Machine$double.eps) )  # S4 method for list,missing,missing lia_age(   x,   t0 = 3.8,   x_star = 18.75,   y_star = 15.63,   z_star = 38.86,   mu = 9.66,   kappa = 3.9,   th232 = 0.049475,   u238 = 0.155125,   u235 = 0.98485,   u238_235 = 137.79,   tolerance = sqrt(.Machine$double.eps) )","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/lia_age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"x numeric vector 206Pb/204Pb ratios. y z missing, must list (data.frame) numeric components (columns) x, y z. y numeric vector 207Pb/204Pb ratios.  missing, attempt made interpret x suitable way. z numeric vector 208Pb/204Pb ratios.  missing, attempt made interpret x suitable way. ... Currently used. t0 numeric value giving time second stage reference model. x_star numeric value giving 206Pb/204Pb ratio \\(t = 0\\). y_star numeric value giving 207Pb/204Pb ratio \\(t = 0\\). z_star numeric value giving 208Pb/204Pb ratio \\(t = 0\\). mu numeric value giving 238U/204Pb ratio reference model. kappa numeric value giving 232Th/238U ratio reference model. th232 numeric value giving decay constants 232Th. u238 numeric value giving decay constants 238U. u235 numeric value giving decay constants 235U. u238_235 numeric value giving 238U/235U ratio. tolerance numeric value specifying tolerance (stopping criteria Newton\u2013Raphson method).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/lia_age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"four columns data.frame: age Geological model age (Ma). mu 238U/204Pb ratio. kappa 232Th/238U ratio. f","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/lia_age.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"Reference values Albarede & Juteau (1984).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/lia_age.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"Albar\u00e8de, F., Desaulty, .-M. & Blichert-Toft, J. (2012). Geological Perspective Use Pb Isotopes Archaeometry. Archaeometry, 54: 853-867. doi:10.1111/j.1475-4754.2011.00653.x . Albar\u00e8de, F. & Juteau, M. (1984). Unscrambling Lead Model Ages. Geochimica et Cosmochimica Acta, 48(1): 207-12. doi:10.1016/0016-7037(84)90364-8 .","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/lia_age.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"N. Frerebeau, F. Albarede (original Matlab code)","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/lia_age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"","code":"Pb <- data.frame(   x = c(18.23247, 18.22936, 18.23102), # Pb206/Pb204   y = c(15.65199, 15.65216, 15.65097), # Pb207/Pb204   z = c(38.5167, 38.51516, 38.51601)   # Pb208/Pb204 )  lia_age(Pb) #>        age       mu    kappa            f #> 1 428.3959 9.859142 4.074891 1.004859e-14 #> 2 430.9796 9.860659 4.076292 1.004859e-14 #> 3 427.6003 9.855397 4.075178 1.004859e-14","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":null,"dir":"Reference","previous_headings":"","what":"Mahalanobis Distance \u2014 mahalanobis","title":"Mahalanobis Distance \u2014 mahalanobis","text":"Computes squared Mahalanobis distance rows x.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mahalanobis Distance \u2014 mahalanobis","text":"","code":"# S4 method for CompositionMatrix mahalanobis(x, center, cov, robust = TRUE, ...)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mahalanobis Distance \u2014 mahalanobis","text":"x CompositionMatrix object. center numeric vector giving mean vector distribution. missing, estimated x. cov numeric matrix giving covariance distribution. missing, estimated x. robust logical scalar: robust location scatter estimation used (see robustbase::covMcd())? ... Extra parameters passed robustbase::covMcd(). used robust TRUE.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mahalanobis Distance \u2014 mahalanobis","text":"numeric vector.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mahalanobis Distance \u2014 mahalanobis","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Mean \u2014 mean","title":"Compositional Mean \u2014 mean","text":"Compositional Mean","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compositional Mean \u2014 mean","text":"","code":"# S4 method for CompositionMatrix mean(x, ...)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compositional Mean \u2014 mean","text":"x CompositionMatrix object. ... argument passed methods.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compositional Mean \u2014 mean","text":"numeric vector.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compositional Mean \u2014 mean","text":"Closed vector columns geometric means.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compositional Mean \u2014 mean","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 .","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Mean \u2014 mean","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Mean \u2014 mean","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Mean mean(coda) #>          A          B          C          D          E  #> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":null,"dir":"Reference","previous_headings":"","what":"nexus: Sourcing Archaeological Materials by Chemical Composition \u2014 nexus-package","title":"nexus: Sourcing Archaeological Materials by Chemical Composition \u2014 nexus-package","text":"Exploration analysis compositional data framework Aitchison (1986) <isbn: 9780412280603>. package provides tools chemical fingerprinting source tracking ancient materials.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"nexus: Sourcing Archaeological Materials by Chemical Composition \u2014 nexus-package","text":"nexus uses following options() configure behavior: nexus.autodetect: logical scalar. Try automatically assign values corresponding slot CompositionMatrix object coercing data.frame? Defaults TRUE. nexus.verbose: logical scalar. R report extra information progress? Defaults TRUE.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"nexus: Sourcing Archaeological Materials by Chemical Composition \u2014 nexus-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Detection \u2014 outliers","title":"Outlier Detection \u2014 outliers","text":"Outlier Detection","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Detection \u2014 outliers","text":"","code":"outliers(object, ...)  # S4 method for CompositionMatrix outliers(   object,   center = NULL,   cov = NULL,   robust = TRUE,   alpha = 0.5,   level = 0.975 )","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Detection \u2014 outliers","text":"object CompositionMatrix. ... Currently used. center numeric vector giving mean vector distribution. missing, estimated x. cov numeric matrix giving covariance distribution. missing, estimated x. robust logical scalar: robust location scatter estimation used (see robustbase::covMcd())? alpha length-one numeric vector controlling size subsets determinant minimized (see robustbase::covMcd()). used robust TRUE. level length-one numeric vector giving significance level. level used cut-value outlier detection: observations larger (squared) Mahalanobis distance considered potential outliers.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Detection \u2014 outliers","text":"OutlierIndex object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Outlier Detection \u2014 outliers","text":"outlier can defined large Mahalanobis distance observations. way, certain proportion observations can identified, e.g. top 2% values (.e. values 0.98th percentile Chi-2 distribution). one hand, Mahalanobis distance likely strongly affected presence outliers. Rousseeuw van Zomeren (1990) thus recommend using robust methods (excessively affected presence outliers). hand, choice threshold classifying observation outlier discussed. apparent reason particular threshold applicable data sets (Filzmoser, Garrett, Reimann 2005).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Outlier Detection \u2014 outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 . Rousseeuw, P. J. & van Zomeren, B. C. (1990). Unmasking Multivariate Outliers Leverage Points. Journal American Statistical Association, 85(411): 633-639. doi:10.1080/01621459.1990.10474920 . Santos, F. (2020). Modern methods old data: overview robust methods outliers detection applications osteology. Journal Archaeological Science: Reports, 32, 102423. doi:10.1016/j.jasrep.2020.102423 .","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outlier Detection \u2014 outliers","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outlier Detection \u2014 outliers","text":"","code":"## Coerce to chemical data data(\"hongite\") coda <- as_composition(hongite)  ## Detect outliers out <- outliers(coda)  ## Plot plot(out, qq = TRUE)  plot(out, qq = FALSE)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Components Analysis \u2014 pca","title":"Principal Components Analysis \u2014 pca","text":"Computes principal components analysis based singular value decomposition.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Components Analysis \u2014 pca","text":"","code":"# S4 method for CompositionMatrix pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )  # S4 method for LogRatio pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Components Analysis \u2014 pca","text":"object LogRatio object. center logical scalar: variables shifted zero centered? scale logical scalar: variables scaled unit variance? rank integer value specifying maximal number components kept results. NULL (default), \\(p - 1\\) components returned. sup_row numeric logical vector specifying indices supplementary rows (individuals). sup_col numeric logical vector specifying indices supplementary columns (variables). weight_row numeric vector specifying active row (individual) weights. NULL (default), uniform weights used. Row weights internally normalized sum 1 weight_col numeric vector specifying active column (variable) weights. NULL (default), uniform weights (1) used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Components Analysis \u2014 pca","text":"dimensio::PCA object.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Components Analysis \u2014 pca","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Components Analysis \u2014 pca","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Centered log-ratio clr <- transform_clr(coda)  ## PCA X <- pca(clr)  ## Plot plot_individuals(X)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Compositional Data \u2014 plot_coda","title":"Plot Compositional Data \u2014 plot_coda","text":"Displays compositional bar chart.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Compositional Data \u2014 plot_coda","text":"","code":"# S4 method for CompositionMatrix autoplot(object, ..., order = NULL, decreasing = FALSE, facet = TRUE)  # S4 method for CompositionMatrix,missing plot(x, order = NULL, decreasing = FALSE, facet = TRUE, ...)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Compositional Data \u2014 plot_coda","text":"... Currently used. order integer vector giving index column used ordering data. decreasing logical scalar: sort order increasing decreasing? facet logical scalar: matrix panels defined groups drawn? used x groups defined. x, object CompositionMatrix object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Compositional Data \u2014 plot_coda","text":"autoplot() returns ggplot object. plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Compositional Data \u2014 plot_coda","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Compositional Data \u2014 plot_coda","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Bar plot plot(coda, order = 2)   ## Use ceramic types for grouping data(\"kommos\", package = \"folio\") kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) #> 1 qualitative variable was removed: date.  plot(coda, order = 1)   ## Log ratio clr <- transform_clr(coda) plot(clr, facet = FALSE)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Log-Ratios \u2014 plot_logratio","title":"Plot Log-Ratios \u2014 plot_logratio","text":"Displays compositional bar chart.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Log-Ratios \u2014 plot_logratio","text":"","code":"# S4 method for LogRatio autoplot(object, ..., order = FALSE, decreasing = FALSE, facet = TRUE)  # S4 method for LogRatio,missing plot(x, order = FALSE, decreasing = FALSE, facet = TRUE, ...)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Log-Ratios \u2014 plot_logratio","text":"... Currently used. order logical scalar: ratio ordered? decreasing logical scalar: sort order increasing decreasing? facet logical scalar: matrix panels defined groups drawn? used x groups defined. x, object CompositionMatrix object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Log-Ratios \u2014 plot_logratio","text":"autoplot() returns ggplot object. plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Log-Ratios \u2014 plot_logratio","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Log-Ratios \u2014 plot_logratio","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Bar plot plot(coda, order = 2)   ## Use ceramic types for grouping data(\"kommos\", package = \"folio\") kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) #> 1 qualitative variable was removed: date.  plot(coda, order = 1)   ## Log ratio clr <- transform_clr(coda) plot(clr, facet = FALSE)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Outliers \u2014 plot_outliers","title":"Plot Outliers \u2014 plot_outliers","text":"Plot Outliers","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Outliers \u2014 plot_outliers","text":"","code":"# S4 method for OutlierIndex autoplot(object, ..., qq = TRUE, limit = !qq)  # S4 method for OutlierIndex,missing plot(x, qq = TRUE, limit = !qq, ...)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Outliers \u2014 plot_outliers","text":"... Currently used. qq logical scalar: quantile-quantile plot produced? limit logical scalar: cut-value outlier detection displayed? x, object OutlierIndex object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Outliers \u2014 plot_outliers","text":"autoplot() returns ggplot object. plot() qqplot() called side-effects: result graphic displayed (invisibly return x).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot Outliers \u2014 plot_outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 .","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Outliers \u2014 plot_outliers","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Outliers \u2014 plot_outliers","text":"","code":"## Coerce to chemical data data(\"hongite\") coda <- as_composition(hongite)  ## Detect outliers out <- outliers(coda)  ## Plot plot(out, qq = TRUE)  plot(out, qq = FALSE)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages \u2014 reexports","title":"Objects exported from other packages \u2014 reexports","text":"objects imported packages. Follow links see documentation. arkhe remove_NA, remove_zero, replace_NA, replace_zero dimensio biplot, plot_contributions, plot_cos2, plot_individuals, plot_variables, plot_variance","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Deal With Samples \u2014 samples","title":"Deal With Samples \u2014 samples","text":"Retrieves defines sample names.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deal With Samples \u2014 samples","text":"","code":"has_replicates(x)  get_samples(x)  set_samples(x) <- value  # S4 method for CompositionMatrix has_replicates(x)  # S4 method for LogRatio has_replicates(x)  # S4 method for OutlierIndex has_replicates(x)  # S4 method for CompositionMatrix get_samples(x)  # S4 method for LogRatio get_samples(x)  # S4 method for OutlierIndex get_samples(x)  # S4 method for CompositionMatrix set_samples(x) <- value","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deal With Samples \u2014 samples","text":"x object get set samples. value possible value samples x.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Deal With Samples \u2014 samples","text":"set_samples() returns object sort x new sample names assigned. get_samples() returns sample names x. has_replicates() returns logical scalar.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Deal With Samples \u2014 samples","text":"situations, measurements may repeated (e.g. multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. See vignette(\"manual\").","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Deal With Samples \u2014 samples","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object \u2014 subset","title":"Extract or Replace Parts of an Object \u2014 subset","text":"Operators acting objects extract replace parts.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object \u2014 subset","text":"","code":"# S4 method for CompositionMatrix [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix [(x, i, j, ...) <- value  # S4 method for CompositionMatrix [[(x, i, j, ...) <- value","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object \u2014 subset","text":"x object extract element(s) replace element(s). , j Indices specifying elements extract replace. Indices numeric, integer character vectors empty (missing) NULL. Numeric values coerced integer .integer() (hence truncated towards zero). Character vectors matched name elements. empty index (comma separated blank) indicates entries dimension selected. ... Currently used. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement. value possible value element(s) x.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object \u2014 subset","text":"subsetted object sort x.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object \u2014 subset","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or Replace Parts of an Object \u2014 subset","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## codaccess dim(coda) # Get the matrix dimensions #> [1] 25  5 row(coda) # Get the row indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,]    1    1    1    1    1 #>  [2,]    2    2    2    2    2 #>  [3,]    3    3    3    3    3 #>  [4,]    4    4    4    4    4 #>  [5,]    5    5    5    5    5 #>  [6,]    6    6    6    6    6 #>  [7,]    7    7    7    7    7 #>  [8,]    8    8    8    8    8 #>  [9,]    9    9    9    9    9 #> [10,]   10   10   10   10   10 #> [11,]   11   11   11   11   11 #> [12,]   12   12   12   12   12 #> [13,]   13   13   13   13   13 #> [14,]   14   14   14   14   14 #> [15,]   15   15   15   15   15 #> [16,]   16   16   16   16   16 #> [17,]   17   17   17   17   17 #> [18,]   18   18   18   18   18 #> [19,]   19   19   19   19   19 #> [20,]   20   20   20   20   20 #> [21,]   21   21   21   21   21 #> [22,]   22   22   22   22   22 #> [23,]   23   23   23   23   23 #> [24,]   24   24   24   24   24 #> [25,]   25   25   25   25   25 col(coda, as.factor = TRUE) # Get the column indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,] A    B    C    D    E    #>  [2,] A    B    C    D    E    #>  [3,] A    B    C    D    E    #>  [4,] A    B    C    D    E    #>  [5,] A    B    C    D    E    #>  [6,] A    B    C    D    E    #>  [7,] A    B    C    D    E    #>  [8,] A    B    C    D    E    #>  [9,] A    B    C    D    E    #> [10,] A    B    C    D    E    #> [11,] A    B    C    D    E    #> [12,] A    B    C    D    E    #> [13,] A    B    C    D    E    #> [14,] A    B    C    D    E    #> [15,] A    B    C    D    E    #> [16,] A    B    C    D    E    #> [17,] A    B    C    D    E    #> [18,] A    B    C    D    E    #> [19,] A    B    C    D    E    #> [20,] A    B    C    D    E    #> [21,] A    B    C    D    E    #> [22,] A    B    C    D    E    #> [23,] A    B    C    D    E    #> [24,] A    B    C    D    E    #> [25,] A    B    C    D    E    #> Levels: A B C D E nrow(coda) # Get the number of rows #> [1] 25 ncol(coda) # Get the number of columns #> [1] 5 dimnames(coda) # Get the dimension names #> [[1]] #>  [1] \"H1\"  \"H2\"  \"H3\"  \"H4\"  \"H5\"  \"H6\"  \"H7\"  \"H8\"  \"H9\"  \"H10\" \"H11\" \"H12\" #> [13] \"H13\" \"H14\" \"H15\" \"H16\" \"H17\" \"H18\" \"H19\" \"H20\" \"H21\" \"H22\" \"H23\" \"H24\" #> [25] \"H25\" #>  #> [[2]] #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" #>  rownames(coda) <- LETTERS[1:25] # Set the row names rownames(coda) # Get the rownames #>  [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\" \"J\" \"K\" \"L\" \"M\" \"N\" \"O\" \"P\" \"Q\" \"R\" \"S\" #> [20] \"T\" \"U\" \"V\" \"W\" \"X\" \"Y\" colnames(coda) <- letters[21:25] # Set the column names colnames(coda) # Get the column names #> [1] \"u\" \"v\" \"w\" \"x\" \"y\"  ## Subset coda[[1, 1]] # Get the first value #> [1] 0.488 coda[1] # Get the first value #> [1] 0.488 coda[, ] # Get all values #> <CompositionMatrix: 25 x 5> #>           u         v          w          x          y #> A 0.4880000 0.3170000 0.03800000 0.06400000 0.09300000 #> B 0.4820000 0.2380000 0.09000000 0.09200000 0.09800000 #> C 0.3700000 0.0910000 0.34200000 0.09500000 0.10200000 #> D 0.5090000 0.2380000 0.07200000 0.10100000 0.08000000 #> E 0.4420000 0.3830000 0.02900000 0.07700000 0.06900000 #> F 0.5230000 0.2620000 0.04200000 0.12500000 0.04800000 #> G 0.4460000 0.3300000 0.04600000 0.12200000 0.05600000 #> H 0.3460000 0.0520000 0.42900000 0.09600000 0.07700000 #> I 0.4120000 0.1170000 0.26700000 0.09600000 0.10800000 #> J 0.4260000 0.4660000 0.00700000 0.05600000 0.04500000 #> K 0.4990000 0.1950000 0.11400000 0.09500000 0.09700000 #> L 0.4520000 0.3730000 0.02700000 0.05500000 0.09300000 #> M 0.3270000 0.0850000 0.38900000 0.08000000 0.11900000 #> N 0.4140000 0.1290000 0.23400000 0.15800000 0.06500000 #> O 0.4620000 0.1750000 0.15800000 0.08300000 0.12200000 #> P 0.3230000 0.0730000 0.40900000 0.12900000 0.06600000 #> Q 0.4320000 0.4430000 0.01000000 0.07800000 0.03700000 #> R 0.4954955 0.3233233 0.03103103 0.08708709 0.06306306 #> S 0.4230000 0.1580000 0.20400000 0.08300000 0.13200000 #> T 0.4460000 0.1150000 0.23800000 0.11600000 0.08500000 #> U 0.4580000 0.1660000 0.16800000 0.12000000 0.08800000 #> V 0.4990000 0.2500000 0.06800000 0.10900000 0.07400000 #> W 0.4860000 0.3400000 0.02500000 0.09400000 0.05500000 #> X 0.4550000 0.1660000 0.17600000 0.09600000 0.10700000 #> Y 0.4590000 0.2490000 0.09700000 0.09800000 0.09700000 coda[1, , drop = FALSE] # Get the first row #> <CompositionMatrix: 1 x 5> #>       u     v     w     x     y #> A 0.488 0.317 0.038 0.064 0.093 coda[, 1:3] # Get the first three column #> <CompositionMatrix: 25 x 3> #>           u         v          w #> A 0.4880000 0.3170000 0.03800000 #> B 0.4820000 0.2380000 0.09000000 #> C 0.3700000 0.0910000 0.34200000 #> D 0.5090000 0.2380000 0.07200000 #> E 0.4420000 0.3830000 0.02900000 #> F 0.5230000 0.2620000 0.04200000 #> G 0.4460000 0.3300000 0.04600000 #> H 0.3460000 0.0520000 0.42900000 #> I 0.4120000 0.1170000 0.26700000 #> J 0.4260000 0.4660000 0.00700000 #> K 0.4990000 0.1950000 0.11400000 #> L 0.4520000 0.3730000 0.02700000 #> M 0.3270000 0.0850000 0.38900000 #> N 0.4140000 0.1290000 0.23400000 #> O 0.4620000 0.1750000 0.15800000 #> P 0.3230000 0.0730000 0.40900000 #> Q 0.4320000 0.4430000 0.01000000 #> R 0.4954955 0.3233233 0.03103103 #> S 0.4230000 0.1580000 0.20400000 #> T 0.4460000 0.1150000 0.23800000 #> U 0.4580000 0.1660000 0.16800000 #> V 0.4990000 0.2500000 0.06800000 #> W 0.4860000 0.3400000 0.02500000 #> X 0.4550000 0.1660000 0.17600000 #> Y 0.4590000 0.2490000 0.09700000","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":null,"dir":"Reference","previous_headings":"","what":"Row Sums \u2014 totals","title":"Row Sums \u2014 totals","text":"Retrieves defines row sums (closure).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row Sums \u2014 totals","text":"","code":"get_totals(x)  set_totals(x) <- value  # S4 method for CompositionMatrix get_totals(x)  # S4 method for CompositionMatrix set_totals(x) <- value","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row Sums \u2014 totals","text":"x object get set totals. value possible value totals x.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row Sums \u2014 totals","text":"set_totals() returns object sort x new row sums assigned. get_totals() returns row sums x.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Row Sums \u2014 totals","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":null,"dir":"Reference","previous_headings":"","what":"Additive Log-Ratios (ALR) \u2014 transform_alr","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"Computes ALR transformation.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"","code":"transform_alr(object, ...)  # S4 method for CompositionMatrix transform_alr(object, j = ncol(object))","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"object CompositionMatrix object. ... Currently used. j integer giving index rationing part (denominator).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"ALR object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered Log-Ratios (CLR) \u2014 transform_clr","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"Computes CLR transformation.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"","code":"transform_clr(object, ...)  # S4 method for CompositionMatrix transform_clr(object, weights = FALSE)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"object CompositionMatrix object. ... Currently used. weights logical scalar: sould varying weight used. FALSE (default), equally-weighted parts used. Alternatively, positive numeric vector weights can specified.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"CLR object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":null,"dir":"Reference","previous_headings":"","what":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"Computes ILR transformations.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"","code":"transform_ilr(object, ...)  # S4 method for CompositionMatrix transform_ilr(object)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"object CompositionMatrix object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"ILR object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"ILR transformation provides coordinates composition respect given orthonormal basis. transform_ilr() uses orthonormal basis (Helmert matrix) originally defined Egozcue et al. (2003).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barcel\u00f3-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse Log-Ratio Transformation \u2014 transform_inverse","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"Computes inverse log-ratio transformations.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"","code":"transform_inverse(object, ...)  # S4 method for CLR transform_inverse(object)  # S4 method for ALR transform_inverse(object)  # S4 method for ILR transform_inverse(object)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"object CompositionMatrix object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"CompositionMatrix object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barcel\u00f3-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Fi\u0161erov\u00e1, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455\u2011468. doi:10.1007/s11004-011-9333-x . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise Log-Ratios (LR) \u2014 transform_lr","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"Computes pairwise log-ratio transformation.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"","code":"transform_lr(object, ...)  # S4 method for CompositionMatrix transform_lr(object)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"object CompositionMatrix object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"LR object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot Log-Ratios (PLR) \u2014 transform_plr","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"Computes PLR transformations.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"","code":"transform_plr(object, ...)  # S4 method for CompositionMatrix transform_plr(object, pivot = 1)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"object CompositionMatrix object. ... Currently used. pivot integer giving index pivotal variable.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"PLR object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"Fi\u0161erov\u00e1, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455\u2011468. doi:10.1007/s11004-011-9333-x . Hron, K., Filzmoser, P., de Caritat, P., Fi\u0161erov\u00e1, E. & Gardlo, . (2017). Weighted Pivot Coordinates Compositional Data Application Geochemical Mapping. Mathematical Geosciences, 49(6), 797-814. doi:10.1007/s11004-017-9684-z . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":null,"dir":"Reference","previous_headings":"","what":"Variation Matrix \u2014 variation","title":"Variation Matrix \u2014 variation","text":"Computes compositional variation array.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variation Matrix \u2014 variation","text":"","code":"variation(object, ...)  # S4 method for CompositionMatrix variation(object)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variation Matrix \u2014 variation","text":"object CompositionMatrix object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variation Matrix \u2014 variation","text":"matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variation Matrix \u2014 variation","text":"compositional variation array square matrix upper triangular part displays log-ratio variances lower triangular part displays log-ratio means.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variation Matrix \u2014 variation","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 .","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variation Matrix \u2014 variation","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variation Matrix \u2014 variation","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Variance matrix var(coda) #>            A         B         C          D         E #> A 0.00000000 0.2592742 1.5328586 0.08281464 0.1385604 #> B 0.25927416 0.0000000 3.0006804 0.54727146 0.6490135 #> C 1.53285862 3.0006804 0.0000000 1.11145146 0.9476375 #> D 0.08281464 0.5472715 1.1114515 0.00000000 0.1870579 #> E 0.13856035 0.6490135 0.9476375 0.18705791 0.0000000  ## Covariance matrix cov(coda) #>             A_B        A_C         A_D         A_E        B_C         B_D #> A_B  0.25927416 -0.6042738 -0.10259133 -0.12558948 -0.8635479 -0.36186549 #> A_C -0.60427379  1.5328586  0.25211090  0.36189072  2.1371324  0.85638469 #> A_D -0.10259133  0.2521109  0.08281464  0.01715854  0.3547022  0.18540597 #> A_E -0.12558948  0.3618907  0.01715854  0.13856035  0.4874802  0.14274802 #> B_C -0.86354795  2.1371324  0.35470223  0.48748020  3.0006804  1.21825018 #> B_D -0.36186549  0.8563847  0.18540597  0.14274802  1.2182502  0.54727146 #> B_E -0.38486364  0.9661645  0.11974987  0.26414983  1.3510281  0.50461351 #> C_D  0.50168246 -1.2807477 -0.16929626 -0.34473217 -1.7824302 -0.67097871 #> C_E  0.47868431 -1.1709679 -0.23495236 -0.22333036 -1.6496522 -0.71363666 #> D_E -0.02299815  0.1097798 -0.06565610  0.12140181  0.1327780 -0.04265795 #>            B_E        C_D        C_E         D_E #> A_B -0.3848636  0.5016825  0.4786843 -0.02299815 #> A_C  0.9661645 -1.2807477 -1.1709679  0.10977982 #> A_D  0.1197499 -0.1692963 -0.2349524 -0.06565610 #> A_E  0.2641498 -0.3447322 -0.2233304  0.12140181 #> B_C  1.3510281 -1.7824302 -1.6496522  0.13277797 #> B_D  0.5046135 -0.6709787 -0.7136367 -0.04265795 #> B_E  0.6490135 -0.8464146 -0.7020147  0.14439996 #> C_D -0.8464146  1.1114515  0.9360155 -0.17543592 #> C_E -0.7020147  0.9360155  0.9476375  0.01162200 #> D_E  0.1444000 -0.1754359  0.0116220  0.18705791  ## Variation matrix variation(coda) #>           A         B           C          D         E #> A 0.0000000 0.2592742  1.53285862 0.08281464 0.1385604 #> B 0.8011244 0.0000000  3.00068035 0.54727146 0.6490135 #> C 1.5999805 0.7988561  0.00000000 1.11145146 0.9476375 #> D 1.5483008 0.7471764 -0.05167973 0.00000000 0.1870579 #> E 1.7152103 0.9140859  0.11522977 0.16690950 0.0000000","package":"nexus"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Pre-Processing XY Data from Experimental Methods","text":"alkahest lightweight, dependency-free toolbox pre-processing XY data experimental methods (.e.\u00a0signal can measured along continuous variable). provides methods baseline estimation correction, smoothing, normalization, integration peaks detection.","code":"To cite alkahest in publications use:    Frerebeau N (2023). _alkahest: Pre-Processing XY Data from   Experimental Methods_. Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.7081524 <https://doi.org/10.5281/zenodo.7081524>,   R package version 1.1.1, <https://packages.tesselle.org/alkahest/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{alkahest: Pre-Processing XY Data from Experimental Methods}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.1.1},     url = {https://packages.tesselle.org/alkahest/},     doi = {10.5281/zenodo.7081524},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Pre-Processing XY Data from Experimental Methods","text":"can install released version alkahest CRAN : development version GitHub :","code":"install.packages(\"alkahest\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/alkahest\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Pre-Processing XY Data from Experimental Methods","text":"alkahest expects input data simplest form (two-column matrix data frame, two-element list two numeric vectors).","code":"## Load the package library(alkahest) ## X-ray diffraction data(\"XRD\")  ## 4S Peak Filling baseline baseline <- baseline_peakfilling(XRD, n = 10, m = 5, by = 10, sparse = TRUE)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\") ## Correct baseline XRD <- signal_drift(XRD, lag = baseline, subtract = TRUE)  ## Find peaks peaks <- peaks_find(XRD, SNR = 3, m = 11)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(peaks, type = \"p\", pch = 16, col = \"red\") ## Simulate data set.seed(12345) x <- seq(-4, 4, length = 100) y <- dnorm(x) z <- y + rnorm(100, mean = 0, sd = 0.01) # Add some noise  ## Plot raw data plot(x, z, type = \"l\", xlab = \"\", ylab = \"\", main = \"Raw data\") lines(x, y, type = \"l\", lty = 2, col = \"red\")  ## Savitzky\u2013Golay filter smooth <- smooth_savitzky(x, z, m = 21, p = 2) plot(smooth, type = \"l\", xlab = \"\", ylab = \"\", main = \"Savitzky\u2013Golay filter\") lines(x, y, type = \"l\", lty = 2, col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Pre-Processing XY Data from Experimental Methods","text":"Please note alkahest project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":[],"package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/BEGe.html","id":null,"dir":"Reference","previous_headings":"","what":"Gamma-Ray Spectrometry \u2014 BEGe","title":"Gamma-Ray Spectrometry \u2014 BEGe","text":"Gamma-Ray Spectrometry","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/BEGe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gamma-Ray Spectrometry \u2014 BEGe","text":"","code":"BEGe","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/BEGe.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Gamma-Ray Spectrometry \u2014 BEGe","text":"data.frame 8192 rows (channels) 2 variables. energy (keV) count","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/BEGe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gamma-Ray Spectrometry \u2014 BEGe","text":"","code":"data(\"BEGe\") plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/LaBr.html","id":null,"dir":"Reference","previous_headings":"","what":"Gamma-Ray Spectrometry \u2014 LaBr","title":"Gamma-Ray Spectrometry \u2014 LaBr","text":"Gamma-Ray Spectrometry","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/LaBr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gamma-Ray Spectrometry \u2014 LaBr","text":"","code":"LaBr","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/LaBr.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Gamma-Ray Spectrometry \u2014 LaBr","text":"data.frame 1024 rows (channels) 2 variables. energy (keV) count","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/LaBr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gamma-Ray Spectrometry \u2014 LaBr","text":"","code":"data(\"LaBr\") plot(LaBr, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/XRD.html","id":null,"dir":"Reference","previous_headings":"","what":"Powder X-ray Diffraction \u2014 XRD","title":"Powder X-ray Diffraction \u2014 XRD","text":"Powder X-ray Diffraction","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/XRD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Powder X-ray Diffraction \u2014 XRD","text":"","code":"XRD","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/XRD.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Powder X-ray Diffraction \u2014 XRD","text":"data.frame 2989 rows 2 variables. theta  count","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/XRD.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Powder X-ray Diffraction \u2014 XRD","text":"","code":"data(\"XRD\") plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/alkahest-package.html","id":null,"dir":"Reference","previous_headings":"","what":"alkahest: Pre-Processing XY Data from Experimental Methods \u2014 alkahest-package","title":"alkahest: Pre-Processing XY Data from Experimental Methods \u2014 alkahest-package","text":"lightweight, dependency-free toolbox pre-processing XY data experimental methods (.e. signal can measured along continuous variable). package provides methods baseline estimation correction, smoothing, normalization, integration peaks detection. Baseline correction methods includes polynomial fitting described Lieber Mahadevan-Jansen (2003) doi:10.1366/000370203322554518 , Rolling Ball algorithm Kneen Annegarn (1996) doi:10.1016/0168-583X(95)00908-6 , SNIP algorithm Ryan et al. (1988) doi:10.1016/0168-583X(88)90063-8 , 4S Peak Filling Liland (2015) doi:10.1016/j.mex.2015.02.009  .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/alkahest-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"alkahest: Pre-Processing XY Data from Experimental Methods \u2014 alkahest-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne 33607 Pessac cedex France","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear Baseline Estimation \u2014 baseline_linear","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"Linear Baseline Estimation","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"","code":"baseline_linear(x, y, ...)  # S4 method for numeric,numeric baseline_linear(x, y, points = range(x))  # S4 method for ANY,missing baseline_linear(x, points = range(x))","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. points numeric vector specifying data points used fitting process (x unit).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Linear baseline baseline <- baseline_linear(XRD, points = c(25, 34))  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")   ## Correct baseline XRD$count <- XRD$count - baseline$y  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":null,"dir":"Reference","previous_headings":"","what":"4S Peak Filling \u2014 baseline_peakfilling","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"Baseline estimation iterative mean suppression.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"","code":"baseline_peakfilling(x, y, ...)  # S4 method for numeric,numeric baseline_peakfilling(x, y, n, m, by = 10, lambda = 1600, d = 2, sparse = FALSE)  # S4 method for ANY,missing baseline_peakfilling(x, n, m, by = 10, lambda = 1600, d = 2, sparse = FALSE)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. n integer value giving number iterations. m odd integer giving half window size. length-one numeric vector givging umber buckets divide x . lambda integer giving smoothing parameter. larger lambda , smoother curve (see smooth_whittaker()). d integer specifying order penalty (see smooth_whittaker()). sparse logical scalar: sparse matrices used computation (see smooth_whittaker())? TRUE, Matrix required.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"Liland, K. H. (2015). 4S Peak Filling - baseline estimation iterative mean suppression. MethodsX, 2, 135-140. doi:10.1016/j.mex.2015.02.009 .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"","code":"## X-ray diffraction data(\"XRD\")  ## 4S Peak Filling baseline baseline <- baseline_peakfilling(XRD, n = 10, m = 5, by = 10, sparse = TRUE)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Polynomial Baseline Estimation \u2014 baseline_polynomial","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"Polynomial Baseline Estimation","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"","code":"baseline_polynomial(x, y, ...)  # S4 method for numeric,numeric baseline_polynomial(x, y, d = 3, tolerance = 0.001, stop = 100)  # S4 method for ANY,missing baseline_polynomial(x, d = 3, tolerance = 0.001, stop = 100)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. d integer giving degree polynomial. Must less number unique points. tolerance numeric scalar giving tolerance difference iterations. stop integer giving stopping rule (.e. maximum number iterations).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"Lieber, C. . Mahadevan-Jansen, . (2003). Automated Method Subtraction Fluorescence Biological Raman Spectra. Applied Spectroscopy, 57(11): 1363-67. doi:10.1366/000370203322554518 .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Subset from 20 to 70 degrees XRD <- signal_select(XRD, from = 20, to = 70)  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Polynomial baseline baseline <- baseline_polynomial(XRD, d = 4, tolerance = 0.02, stop = 1000)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":null,"dir":"Reference","previous_headings":"","what":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"Rolling Ball Baseline Estimation","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"","code":"baseline_rollingball(x, y, ...)  # S4 method for numeric,numeric baseline_rollingball(x, y, m, s)  # S4 method for ANY,missing baseline_rollingball(x, m, s)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. m odd integer giving window size (.e. number adjacent points used; see window_sliding()) minimization/maximization. s odd integer giving window size (.e. number adjacent points used; see window_sliding()) smoothing.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, progressively wider/narrower windows used ends data series.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"Kneen, M. . Annegarn, H. J. (1996). Algorithm Fitting XRF, SEM PIXE X-Ray Spectra Backgrounds. Nuclear Instruments Methods Physics Research Section B: Beam Interactions Materials Atoms, 109/110: 209-213. doi:10.1016/0168-583X(95)00908-6 .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Subset from 20 to 70 degrees XRD <- signal_select(XRD, from = 20, to = 70)  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Rolling Ball baseline baseline <- baseline_rollingball(XRD, m = 201, s = 151)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":null,"dir":"Reference","previous_headings":"","what":"Rubberband Baseline Estimation \u2014 baseline_rubberband","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"Rubberband Baseline Estimation","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"","code":"baseline_rubberband(x, y, ...)  # S4 method for numeric,numeric baseline_rubberband(x, y, noise = 0, spline = TRUE, ...)  # S4 method for ANY,missing baseline_rubberband(x, noise = 0, spline = TRUE, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra arguments passed stats::smooth.spline(). noise length-one numeric vector giving noise level. used method \"rubberband\". spline logical scalar: spline interpolation support points used instead linear interpolation? used method \"rubberband\".","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"convex envelope spectrum determined baseline estimated part convex envelope lying spectrum. Note rubber band enter concave regions () spectrum.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"baseline_rubberband() slightly modified C. Beleites' hyperSpec::spc.rubberband().","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")  ## Rubberband baseline baseline <- baseline_rubberband(BEGe)  plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":null,"dir":"Reference","previous_headings":"","what":"SNIP Baseline Estimation \u2014 baseline_snip","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"Sensitive Nonlinear Iterative Peak clipping algorithm.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"","code":"baseline_snip(x, y, ...)  # S4 method for numeric,numeric baseline_snip(x, y, LLS = FALSE, decreasing = FALSE, n = 100)  # S4 method for ANY,missing baseline_snip(x, LLS = FALSE, decreasing = FALSE, n = 100)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. LLS logical scalar: LLS operator applied x employing SNIP algorithm? used method \"SNIP\". decreasing logical scalar: decreasing clipping window used? n integer value giving number iterations.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"Morh\u00e1\u010d, M., Kliman, J., Matou\u0161ek, V., Veselsk\u00fd, M. & Turzo, . (1997). Background elimination methods multidimensional gamma-ray spectra. Nuclear Instruments Methods Physics Research Section : Accelerators, Spectrometers, Detectors Associated Equipment, 401(1), p. 113-132. doi:10.1016/S0168-9002(97)01023-1 Morh\u00e1\u010d, M. & Matou\u0161ek, V. (2008). Peak Clipping Algorithms Background Estimation Spectroscopic Data. Applied Spectroscopy, 62(1), p. 91-106. doi:10.1366/000370208783412762 Ryan, C. G., Clayton, E., Griffin, W. L., Sie, S. H. & Cousens, D. R. (1988). SNIP, statistics-sensitive background treatment quantitative analysis PIXE spectra geoscience applications. Nuclear Instruments Methods Physics Research Section B: Beam Interactions Materials Atoms, 34(3), p. 396-402. doi:10.1016/0168-583X(88)90063-8","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")  ## SNIP baseline baseline <- baseline_snip(BEGe, LLS = FALSE, decreasing = FALSE, n = 100)  plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectangle Rule \u2014 integrate_rectangle","title":"Rectangle Rule \u2014 integrate_rectangle","text":"Approximates definite integral using rectangle rule.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectangle Rule \u2014 integrate_rectangle","text":"","code":"integrate_rectangle(x, y, ...)  # S4 method for numeric,numeric integrate_rectangle(x, y, right = FALSE)  # S4 method for ANY,missing integrate_rectangle(x, right = FALSE)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectangle Rule \u2014 integrate_rectangle","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. right logical scalar: right rule used instead left rule?","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectangle Rule \u2014 integrate_rectangle","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rectangle Rule \u2014 integrate_rectangle","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectangle Rule \u2014 integrate_rectangle","text":"","code":"## Calculate the area under the sine curve from 0 to pi # integrate(f = function(x) x^3, lower = 0, upper = 2) x <- seq(0, 2, len = 101) y <- x^3  plot(x, y, type = \"l\")   integrate_rectangle(x, y, right = FALSE) # 3.9204 #> [1] 3.9204 integrate_rectangle(x, y, right = TRUE) # 4.0804 #> [1] 4.0804 integrate_trapezoid(x, y) # 4.0004 #> [1] 4.0004","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Trapezoidal Rule \u2014 integrate_trapezoid","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"Approximates definite integral using trapezoidal rule.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"","code":"integrate_trapezoid(x, y, ...)  # S4 method for numeric,numeric integrate_trapezoid(x, y)  # S4 method for ANY,missing integrate_trapezoid(x)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"","code":"## Calculate the area under the sine curve from 0 to pi # integrate(f = function(x) x^3, lower = 0, upper = 2) x <- seq(0, 2, len = 101) y <- x^3  plot(x, y, type = \"l\")   integrate_rectangle(x, y, right = FALSE) # 3.9204 #> [1] 3.9204 integrate_rectangle(x, y, right = TRUE) # 4.0804 #> [1] 4.0804 integrate_trapezoid(x, y) # 4.0004 #> [1] 4.0004","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":null,"dir":"Reference","previous_headings":"","what":"Strip XRD ka2 \u2014 ka2_strip_penalized","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"Strip XRD ka2","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"","code":"ka2_strip_penalized(x, y, ...)  # S4 method for numeric,numeric ka2_strip_penalized(   x,   y,   lambda,   wave = c(1.5406, 1.54443),   tau = 0.5,   nseg = 1,   progress = interactive() )  # S4 method for ANY,missing ka2_strip_penalized(   x,   lambda,   wave = c(1.5406, 1.54443),   tau = 0.5,   nseg = 1,   progress = interactive() )","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. lambda integer giving smoothing parameter. larger lambda , smoother curve. wave length-two numeric vector giving characteristic wavelengths anode material (defaults copper). tau length-one numeric vector giving ratio \\(\\alpha\\)1 \\(\\alpha\\)2 line intensities (defaults 1/2). nseg length-one numeric vector specifying number equally sized segments B-spline basis matrix computation. progress logical scalar: progress bar displayed?","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"Matrix required.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"de Rooi, J. J., van der Pers, N. M., Hendrikx, R. W. ., Delhez, R., B\u00f6ttger . J. Eilers, P. H. C. (2014). Smoothing X-ray diffraction data Ka2 elimination using penalized likelihood composite link model. Journal Applied Crystallography, 47: 852-860. doi:10.1107/S1600576714005809","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"J. J. de Rooi et al. (original R code).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"","code":"if (FALSE) { ## X-ray diffraction data(\"XRD\")  ## Subset from 20 to 40 degrees XRD <- signal_select(XRD, from = 20, to = 40)  ## Plot diffractogram plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Penalized likelihood smoothing lambda <- seq(from = 1, to = 8, length.out = 40) lambda <- 10^lambda  likelihood <- smooth_likelihood(XRD, lambda = lambda, d = 3) lines(likelihood, col = \"red\")  ## Strip ka2 ka2 <- ka2_strip_penalized(XRD, lambda = lambda, tau = 0.5, nseg = 1) lines(ka2, col = \"blue\") }","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":null,"dir":"Reference","previous_headings":"","what":"Find Peaks \u2014 peaks_find","title":"Find Peaks \u2014 peaks_find","text":"Finds local maxima sequential data.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find Peaks \u2014 peaks_find","text":"","code":"peaks_find(x, y, ...)  # S4 method for numeric,numeric peaks_find(x, y, method = \"MAD\", SNR = 2, m = NULL, ...)  # S4 method for ANY,missing peaks_find(x, method = \"MAD\", SNR = 2, m = NULL, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find Peaks \u2014 peaks_find","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra parameters passed internal methods. method character string specifying method used background noise estimation (see ). SNR integer giving signal--noise-ratio peak detection (see ). m odd integer giving window size (.e. number adjacent points used). NULL, 5% data points used half window size.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find Peaks \u2014 peaks_find","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Find Peaks \u2014 peaks_find","text":"local maximum highest one given window higher \\(SNR \\times noise\\) recognized peak. following methods available noise estimation: MAD Median Absolute Deviation. Note improve peak detection, may helpful smooth data remove baseline beforehand.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Find Peaks \u2014 peaks_find","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, progressively wider/narrower windows used ends data series. Adapted Stasia Grinberg's findPeaks function.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Find Peaks \u2014 peaks_find","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find Peaks \u2014 peaks_find","text":"","code":"## X-ray diffraction data(\"XRD\")  ## 4S Peak Filling baseline baseline <- baseline_peakfilling(XRD, n = 10, m = 5, by = 10, sparse = TRUE)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")   ## Correct baseline XRD <- signal_drift(XRD, lag = baseline, subtract = TRUE)  ## Find peaks peaks <- peaks_find(XRD, SNR = 3, m = 11)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(peaks, type = \"p\", pch = 16, col = \"red\") abline(h = attr(peaks, \"noise\"), lty = 2) # noise threshold   ## Half-Width at Half-Maximum x <- seq(-4, 4, length = 1000) y <- dnorm(x)  peaks_fwhm(x, y, center = 0) # Expected: 2 * sqrt(2 * log(2)) #> [1] 2.354354","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":null,"dir":"Reference","previous_headings":"","what":"Half-Width at Half-Maximum \u2014 peaks_fwhm","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"Estimates Half-Width Half-Maximum (FWHM) given peak.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"","code":"peaks_fwhm(x, y, ...)  # S4 method for numeric,numeric peaks_fwhm(x, y, center)  # S4 method for ANY,missing peaks_fwhm(x, center)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. center numeric value giving peak position x units.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"numeric value.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"tries get smallest possible estimate.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"","code":"## X-ray diffraction data(\"XRD\")  ## 4S Peak Filling baseline baseline <- baseline_peakfilling(XRD, n = 10, m = 5, by = 10, sparse = TRUE)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")   ## Correct baseline XRD <- signal_drift(XRD, lag = baseline, subtract = TRUE)  ## Find peaks peaks <- peaks_find(XRD, SNR = 3, m = 11)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(peaks, type = \"p\", pch = 16, col = \"red\") abline(h = attr(peaks, \"noise\"), lty = 2) # noise threshold   ## Half-Width at Half-Maximum x <- seq(-4, 4, length = 1000) y <- dnorm(x)  peaks_fwhm(x, y, center = 0) # Expected: 2 * sqrt(2 * log(2)) #> [1] 2.354354","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_negative.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace Negative Values \u2014 replace_negative","title":"Replace Negative Values \u2014 replace_negative","text":"Replace Negative Values","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_negative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace Negative Values \u2014 replace_negative","text":"","code":"replace_negative(x, y, ...)  # S4 method for numeric,numeric replace_negative(x, y, value = 0)  # S4 method for ANY,missing replace_negative(x, value = 0)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_negative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace Negative Values \u2014 replace_negative","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra parameters passed threshold. value numeric value replace negative values.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_negative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace Negative Values \u2014 replace_negative","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_negative.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Replace Negative Values \u2014 replace_negative","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace Values Below a Given Threshold \u2014 replace_threshold","title":"Replace Values Below a Given Threshold \u2014 replace_threshold","text":"Replace Values Given Threshold","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace Values Below a Given Threshold \u2014 replace_threshold","text":"","code":"replace_threshold(x, y, threshold, ...)  # S4 method for numeric,numeric,`function` replace_threshold(x, y, threshold, value = 0, ...)  # S4 method for ANY,missing,`function` replace_threshold(x, threshold, value = 0, ...)  # S4 method for numeric,numeric,numeric replace_threshold(x, y, threshold, value = 0, ...)  # S4 method for ANY,missing,numeric replace_threshold(x, threshold, value = 0, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace Values Below a Given Threshold \u2014 replace_threshold","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). threshold numeric value function takes numeric vector argument returns single numeric value. ... Extra parameters passed threshold. value numeric value replace values threshold.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace Values Below a Given Threshold \u2014 replace_threshold","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_threshold.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Replace Values Below a Given Threshold \u2014 replace_threshold","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":null,"dir":"Reference","previous_headings":"","what":"Bin \u2014 resample_bin","title":"Bin \u2014 resample_bin","text":"Averages x values applies function corresponding y values.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bin \u2014 resample_bin","text":"","code":"resample_bin(x, y, ...)  # S4 method for numeric,numeric resample_bin(x, y, by, f = mean, ...)  # S4 method for ANY,missing resample_bin(x, y, by, f = sum)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bin \u2014 resample_bin","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra parameters passed f. integer specifying binning ratio (.e. number points grouped together; see window_tumbling()). f function takes numeric vector intensities argument returns single numeric vector. Used estimate local representative value bin (defaults sum(); see examples).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bin \u2014 resample_bin","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bin \u2014 resample_bin","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bin \u2014 resample_bin","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")   ## Bin by 3 XRD_bin_mean <- resample_bin(XRD, by = 3, f = mean) XRD_bin_min <- resample_bin(XRD, by = 3, f = min)  plot(XRD, type = \"l\", xlim = c(25, 35),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_bin_mean, type = \"l\", col = \"red\") lines(XRD_bin_min, type = \"l\", col = \"green\")   ## Downsample by 10 XRD_down <- resample_down(XRD, by = 10)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_down, type = \"l\", col = \"red\")   ## Linearly interpolate XRD_approx <- resample_interpolate(XRD, from = 20, to = 40, by = 0.02)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_approx, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":null,"dir":"Reference","previous_headings":"","what":"Downsample \u2014 resample_down","title":"Downsample \u2014 resample_down","text":"Downsample","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downsample \u2014 resample_down","text":"","code":"resample_down(x, y, ...)  # S4 method for numeric,numeric resample_down(x, y, by)  # S4 method for ANY,missing resample_down(x, y, by)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downsample \u2014 resample_down","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. integer specifying downsampling factor.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downsample \u2014 resample_down","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Downsample \u2014 resample_down","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downsample \u2014 resample_down","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")   ## Bin by 3 XRD_bin_mean <- resample_bin(XRD, by = 3, f = mean) XRD_bin_min <- resample_bin(XRD, by = 3, f = min)  plot(XRD, type = \"l\", xlim = c(25, 35),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_bin_mean, type = \"l\", col = \"red\") lines(XRD_bin_min, type = \"l\", col = \"green\")   ## Downsample by 10 XRD_down <- resample_down(XRD, by = 10)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_down, type = \"l\", col = \"red\")   ## Linearly interpolate XRD_approx <- resample_interpolate(XRD, from = 20, to = 40, by = 0.02)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_approx, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Linearly Interpolate \u2014 resample_interpolate","title":"Linearly Interpolate \u2014 resample_interpolate","text":"Linearly Interpolate","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linearly Interpolate \u2014 resample_interpolate","text":"","code":"resample_interpolate(x, y, ...)  # S4 method for numeric,numeric resample_interpolate(x, y, from, to, by, ...)  # S4 method for ANY,missing resample_interpolate(x, y, from, to, by, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linearly Interpolate \u2014 resample_interpolate","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra arguments passed stats::approx(). length-one numeric vector giving starting value sequence interpolation take place. length-one numeric vector giving end value sequence interpolation take place. length-one numeric vector specifying increment sequence.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linearly Interpolate \u2014 resample_interpolate","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Linearly Interpolate \u2014 resample_interpolate","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linearly Interpolate \u2014 resample_interpolate","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")   ## Bin by 3 XRD_bin_mean <- resample_bin(XRD, by = 3, f = mean) XRD_bin_min <- resample_bin(XRD, by = 3, f = min)  plot(XRD, type = \"l\", xlim = c(25, 35),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_bin_mean, type = \"l\", col = \"red\") lines(XRD_bin_min, type = \"l\", col = \"green\")   ## Downsample by 10 XRD_down <- resample_down(XRD, by = 10)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_down, type = \"l\", col = \"red\")   ## Linearly interpolate XRD_approx <- resample_interpolate(XRD, from = 20, to = 40, by = 0.02)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_approx, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize intensities by AUC \u2014 rescale_area","title":"Normalize intensities by AUC \u2014 rescale_area","text":"Rescales intensities area curve (AUC) equal 1.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize intensities by AUC \u2014 rescale_area","text":"","code":"rescale_area(x, y, ...)  # S4 method for numeric,numeric rescale_area(x, y, method = c(\"rectangle\", \"trapezoid\"), ...)  # S4 method for ANY,missing rescale_area(x, method = c(\"rectangle\", \"trapezoid\"), ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize intensities by AUC \u2014 rescale_area","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. method character string specifying method integration. must one \"rectangle\" \"trapezoid\". unambiguous substring can given.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalize intensities by AUC \u2014 rescale_area","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Normalize intensities by AUC \u2014 rescale_area","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalize intensities by AUC \u2014 rescale_area","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Normalize by area under the curve BEGe_area <- rescale_area(BEGe) plot(BEGe_area, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")  integrate_rectangle(BEGe) #> [1] 147922.1 integrate_rectangle(BEGe_area) #> [1] 1  ## Rescale so that intensities sum to 1 BEGe_total <- rescale_total(BEGe, total = 1) plot(BEGe_total, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Rescale intensities to 0-1 BEGe_range <- rescale_range(BEGe, min = 0, max = 1) plot(BEGe_range, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"Rescales intensities specified minimum maximum.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"","code":"rescale_range(x, y, ...)  rescale_min(x, y, ...)  rescale_max(x, y, ...)  # S4 method for numeric,numeric rescale_range(x, y, min = 0, max = 1)  # S4 method for ANY,missing rescale_range(x, min = 0, max = 1)  # S4 method for numeric,numeric rescale_min(x, y, min = 0)  # S4 method for ANY,missing rescale_min(x, min = 0)  # S4 method for numeric,numeric rescale_max(x, y, max = 1)  # S4 method for ANY,missing rescale_max(x, max = 1)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. min legnth-one numeric vector specifying output minimum. max legnth-one numeric vector specifying output maximum.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Normalize by area under the curve BEGe_area <- rescale_area(BEGe) plot(BEGe_area, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")  integrate_rectangle(BEGe) #> [1] 147922.1 integrate_rectangle(BEGe_area) #> [1] 1  ## Rescale so that intensities sum to 1 BEGe_total <- rescale_total(BEGe, total = 1) plot(BEGe_total, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Rescale intensities to 0-1 BEGe_range <- rescale_range(BEGe, min = 0, max = 1) plot(BEGe_range, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":null,"dir":"Reference","previous_headings":"","what":"Rescale intensities to sum to a specified value \u2014 rescale_total","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"Rescales intensities sum specified value.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"","code":"rescale_total(x, y, ...)  # S4 method for numeric,numeric rescale_total(x, y, total = 1)  # S4 method for ANY,missing rescale_total(x, total = 1)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. total legnth-one numeric vector specifying output total. Defaults 1, .e. normalizes total intensity.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Normalize by area under the curve BEGe_area <- rescale_area(BEGe) plot(BEGe_area, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")  integrate_rectangle(BEGe) #> [1] 147922.1 integrate_rectangle(BEGe_area) #> [1] 1  ## Rescale so that intensities sum to 1 BEGe_total <- rescale_total(BEGe, total = 1) plot(BEGe_total, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Rescale intensities to 0-1 BEGe_range <- rescale_range(BEGe, min = 0, max = 1) plot(BEGe_range, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Intensities \u2014 rescale_transform","title":"Transform Intensities \u2014 rescale_transform","text":"Transform Intensities","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Intensities \u2014 rescale_transform","text":"","code":"rescale_transform(x, y, ...)  # S4 method for numeric,numeric rescale_transform(x, y, f, ...)  # S4 method for ANY,missing rescale_transform(x, f, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Intensities \u2014 rescale_transform","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra arguments passed f. f function takes numeric vector intensities argument returns numeric vector.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Intensities \u2014 rescale_transform","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Intensities \u2014 rescale_transform","text":"Transformation intensities can used improve identification peaks low signal--noise ratio.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Transform Intensities \u2014 rescale_transform","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Intensities \u2014 rescale_transform","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Transform intensities BEGe_trans <- rescale_transform(BEGe, f = sqrt) plot(BEGe_trans, type = \"l\", xlab = \"Energy (keV)\", ylab = \"sqrt(Count)\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":null,"dir":"Reference","previous_headings":"","what":"Bind \u2014 signal_bind","title":"Bind \u2014 signal_bind","text":"Combines XY objects.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bind \u2014 signal_bind","text":"","code":"signal_bind(...)  # S4 method for ANY signal_bind(...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bind \u2014 signal_bind","text":"... object can interpreted suitable way (see grDevices::xy.coords()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bind \u2014 signal_bind","text":"Returns matrix intensities.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bind \u2014 signal_bind","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bind \u2014 signal_bind","text":"","code":"## X-ray diffraction data(\"XRD\")  XRD1 <- signal_drift(XRD, lag = 1500)  ## Bind XRD_bind <- signal_bind(XRD, XRD1) XRD_bind[, 1:10] #>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> XRD  6780 6786 6773 6961 6958 6796 6920 6910 6785  6879 #> XRD1 8280 8286 8273 8461 8458 8296 8420 8410 8285  8379  ## Mean XRD_mean <- signal_mean(XRD, XRD1)  plot(NULL, type = \"l\", xlim = c(10, 70) , ylim = c(3000, 36000),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD, type = \"l\") lines(XRD1, type = \"l\") lines(XRD_mean, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":null,"dir":"Reference","previous_headings":"","what":"Baseline Correction \u2014 signal_correct","title":"Baseline Correction \u2014 signal_correct","text":"Baseline Correction","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Baseline Correction \u2014 signal_correct","text":"","code":"signal_correct(x, y, ...)  # S4 method for numeric,numeric signal_correct(x, y, method = c(\"linear\", \"rubberband\", \"SNIP\", \"4S\"), ...)  # S4 method for ANY,missing signal_correct(x, method = c(\"linear\", \"rubberband\", \"SNIP\", \"4S\"), ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Baseline Correction \u2014 signal_correct","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra arguments passed baseline_*() (see details). method character string specifying method baseline estimation. must one \"linear\", \"rubberband\", \"SNIP\" \"4S\" (see details). unambiguous substring can given.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Baseline Correction \u2014 signal_correct","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Baseline Correction \u2014 signal_correct","text":"Available methods baseline estimation: linear Linear baseline estimation (see baseline_linear()). rubberband Rubberband baseline estimation (see baseline_rubberband()). SNIP Sensitive Nonlinear Iterative Peak clipping algorithm (see baseline_snip()). 4S 4S Peak Filling (see baseline_peakfilling()).","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Baseline Correction \u2014 signal_correct","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Baseline Correction \u2014 signal_correct","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Drift baseline <- baseline_snip(BEGe) BEGe_drif <- signal_drift(BEGe, lag = baseline, subtract = TRUE)  plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\") lines(BEGe_drif, type = \"l\", col = \"red\")   ## Correct BEGe_corr <- signal_correct(BEGe, method = \"SNIP\")  plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\") lines(BEGe_corr, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":null,"dir":"Reference","previous_headings":"","what":"Drift Intensities \u2014 signal_drift","title":"Drift Intensities \u2014 signal_drift","text":"Drift Intensities","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drift Intensities \u2014 signal_drift","text":"","code":"signal_drift(x, y, lag, ...)  # S4 method for numeric,numeric,numeric signal_drift(x, y, lag)  # S4 method for ANY,missing,ANY signal_drift(x, lag, subtract = FALSE)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drift Intensities \u2014 signal_drift","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). lag numeric vector specifying offset object can interpreted suitable way (see grDevices::xy.coords()) ... Currently used. subtract logical scalar: lag subtracted y?","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drift Intensities \u2014 signal_drift","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Drift Intensities \u2014 signal_drift","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Drift Intensities \u2014 signal_drift","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Drift BEGe_plus <- signal_drift(BEGe, lag = 250) BEGe_minus <- signal_drift(BEGe, lag = 250, subtract = TRUE)  plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\") lines(BEGe_plus, type = \"l\", col = \"red\") lines(BEGe_minus, type = \"l\", col = \"green\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Intensities \u2014 signal_mean","title":"Mean Intensities \u2014 signal_mean","text":"Mean Intensities","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean Intensities \u2014 signal_mean","text":"","code":"signal_mean(...)  # S4 method for ANY signal_mean(...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean Intensities \u2014 signal_mean","text":"... object can interpreted suitable way (see grDevices::xy.coords()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean Intensities \u2014 signal_mean","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean Intensities \u2014 signal_mean","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean Intensities \u2014 signal_mean","text":"","code":"## X-ray diffraction data(\"XRD\")  XRD1 <- signal_drift(XRD, lag = 1500)  ## Bind XRD_bind <- signal_bind(XRD, XRD1) XRD_bind[, 1:10] #>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> XRD  6780 6786 6773 6961 6958 6796 6920 6910 6785  6879 #> XRD1 8280 8286 8273 8461 8458 8296 8420 8410 8285  8379  ## Mean XRD_mean <- signal_mean(XRD, XRD1)  plot(NULL, type = \"l\", xlim = c(10, 70) , ylim = c(3000, 36000),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD, type = \"l\") lines(XRD1, type = \"l\") lines(XRD_mean, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":null,"dir":"Reference","previous_headings":"","what":"Shift the X Scale \u2014 signal_shift","title":"Shift the X Scale \u2014 signal_shift","text":"Shifts x scale given value.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shift the X Scale \u2014 signal_shift","text":"","code":"signal_shift(x, y, lag, ...)  # S4 method for numeric,numeric signal_shift(x, y, lag)  # S4 method for ANY,missing signal_shift(x, lag)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shift the X Scale \u2014 signal_shift","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). lag numeric vector specifying offset. ... Currently used.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shift the X Scale \u2014 signal_shift","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Shift the X Scale \u2014 signal_shift","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Shift the X Scale \u2014 signal_shift","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Shift by one degree XRD_offset <- signal_shift(XRD, lag = 1) lines(XRD_offset, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":null,"dir":"Reference","previous_headings":"","what":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"Penalized Likelihood Smoothing","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"","code":"smooth_likelihood(x, y, ...)  # S4 method for numeric,numeric smooth_likelihood(x, y, lambda, d = 2, SE = FALSE, progress = interactive())  # S4 method for ANY,missing smooth_likelihood(x, lambda, d = 2, SE = FALSE, progress = interactive())","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. lambda integer giving smoothing parameter. larger lambda , smoother curve. d integer specifying order penalty. SE logical scalar: standard errors returned? progress logical scalar: progress bar displayed?","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"Matrix required.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"de Rooi, J. J., van der Pers, N. M., Hendrikx, R. W. ., Delhez, R., B\u00f6ttger . J. Eilers, P. H. C. (2014). Smoothing X-ray diffraction data Ka2 elimination using penalized likelihood composite link model. Journal Applied Crystallography, 47: 852-860. doi:10.1107/S1600576714005809","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"J. J. de Rooi et al. (original R code).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"","code":"if (FALSE) { ## X-ray diffraction data(\"XRD\")  ## Subset from 20 to 40 degrees XRD <- signal_select(XRD, from = 20, to = 40)  ## Plot diffractogram plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Penalized likelihood smoothing lambda <- seq(from = 1, to = 8, length.out = 40) lambda <- 10^lambda  likelihood <- smooth_likelihood(XRD, lambda = lambda, d = 3) lines(likelihood, col = \"red\")  ## Strip ka2 ka2 <- ka2_strip_penalized(XRD, lambda = lambda, tau = 0.5, nseg = 1) lines(ka2, col = \"blue\") }","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":null,"dir":"Reference","previous_headings":"","what":"Loess Smoothing \u2014 smooth_loess","title":"Loess Smoothing \u2014 smooth_loess","text":"Smoothes intensities loess fitting.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loess Smoothing \u2014 smooth_loess","text":"","code":"smooth_loess(x, y, ...)  # S4 method for numeric,numeric smooth_loess(x, y, span = 0.75, ...)  # S4 method for ANY,missing smooth_loess(x, span = 0.75, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loess Smoothing \u2014 smooth_loess","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra arguments passed stats::loess(). span integer specifying degree smoothing (see stats::loess()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loess Smoothing \u2014 smooth_loess","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Loess Smoothing \u2014 smooth_loess","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Loess Smoothing \u2014 smooth_loess","text":"","code":"## Simulate data with some noise x <- seq(-4, 4, length = 100) y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)  ## Plot spectrum plot(x, y, type = \"l\", xlab = \"\", ylab = \"\")   ## Rectangular smoothing unweighted <- smooth_rectangular(x, y, m = 3) plot(unweighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Triangular smoothing weighted <- smooth_triangular(x, y, m = 5) plot(weighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Loess smoothing loess <- smooth_loess(x, y, span = 0.75) plot(loess, type = \"l\", xlab = \"\", ylab = \"\")   ## Savitzky\u2013Golay filter savitzky <- smooth_savitzky(x, y, m = 21, p = 2) plot(savitzky, type = \"l\", xlab = \"\", ylab = \"\")   ## Whittaker smoothing whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2) plot(whittaker, type = \"l\", xlab = \"\", ylab = \"\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectangular Smoothing \u2014 smooth_rectangular","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"Unweighted sliding-average rectangular Smoothing.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"","code":"smooth_rectangular(x, y, ...)  # S4 method for numeric,numeric smooth_rectangular(x, y, m = 3)  # S4 method for ANY,missing smooth_rectangular(x, m)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. m odd integer giving window size (.e. number adjacent points used; see window_sliding()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"replaces point signal average \\(m\\) adjacent points.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, progressively wider/narrower windows used ends data series.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"","code":"## Simulate data with some noise x <- seq(-4, 4, length = 100) y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)  ## Plot spectrum plot(x, y, type = \"l\", xlab = \"\", ylab = \"\")   ## Rectangular smoothing unweighted <- smooth_rectangular(x, y, m = 3) plot(unweighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Triangular smoothing weighted <- smooth_triangular(x, y, m = 5) plot(weighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Loess smoothing loess <- smooth_loess(x, y, span = 0.75) plot(loess, type = \"l\", xlab = \"\", ylab = \"\")   ## Savitzky\u2013Golay filter savitzky <- smooth_savitzky(x, y, m = 21, p = 2) plot(savitzky, type = \"l\", xlab = \"\", ylab = \"\")   ## Whittaker smoothing whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2) plot(whittaker, type = \"l\", xlab = \"\", ylab = \"\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":null,"dir":"Reference","previous_headings":"","what":"Savitzky-Golay Filter \u2014 smooth_savitzky","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"Savitzky-Golay Filter","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"","code":"smooth_savitzky(x, y, ...)  # S4 method for numeric,numeric smooth_savitzky(x, y, m = 3, p = 2)  # S4 method for ANY,missing smooth_savitzky(x, m, p)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. m odd integer giving window size (.e. number adjacent points used). p integer giving degree polynomial used.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"method based least-squares fitting polynomials segments \\(m\\) adjacent points.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, original \\((m - 1) / 2\\) points ends data series preserved.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"Gorry, P. . (1990). General Least-Squares Smoothing Differentiation Convolution (Savitzky-Golay) Method. Analytical Chemistry, 62(6), p. 570-573. doi:10.1021/ac00205a007 . Savitzky, . & Golay, M. J. E. (1964). Smoothing Differentiation Data Simplified Least Squares Procedures. Analytical Chemistry, 36(8), p. 1627-1639. doi:10.1021/ac60214a047 .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"","code":"## Simulate data with some noise x <- seq(-4, 4, length = 100) y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)  ## Plot spectrum plot(x, y, type = \"l\", xlab = \"\", ylab = \"\")   ## Rectangular smoothing unweighted <- smooth_rectangular(x, y, m = 3) plot(unweighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Triangular smoothing weighted <- smooth_triangular(x, y, m = 5) plot(weighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Loess smoothing loess <- smooth_loess(x, y, span = 0.75) plot(loess, type = \"l\", xlab = \"\", ylab = \"\")   ## Savitzky\u2013Golay filter savitzky <- smooth_savitzky(x, y, m = 21, p = 2) plot(savitzky, type = \"l\", xlab = \"\", ylab = \"\")   ## Whittaker smoothing whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2) plot(whittaker, type = \"l\", xlab = \"\", ylab = \"\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Triangular Smoothing \u2014 smooth_triangular","title":"Triangular Smoothing \u2014 smooth_triangular","text":"Weighted sliding-average triangular smoothing.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triangular Smoothing \u2014 smooth_triangular","text":"","code":"smooth_triangular(x, y, ...)  # S4 method for numeric,numeric smooth_triangular(x, y, m = 3)  # S4 method for ANY,missing smooth_triangular(x, m)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triangular Smoothing \u2014 smooth_triangular","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. m odd integer giving window size (.e. number adjacent points used; see window_sliding()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Triangular Smoothing \u2014 smooth_triangular","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Triangular Smoothing \u2014 smooth_triangular","text":"replaces point signal weighted mean \\(m\\) adjacent points.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Triangular Smoothing \u2014 smooth_triangular","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, progressively wider/narrower windows used ends data series.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Triangular Smoothing \u2014 smooth_triangular","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triangular Smoothing \u2014 smooth_triangular","text":"","code":"## Simulate data with some noise x <- seq(-4, 4, length = 100) y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)  ## Plot spectrum plot(x, y, type = \"l\", xlab = \"\", ylab = \"\")   ## Rectangular smoothing unweighted <- smooth_rectangular(x, y, m = 3) plot(unweighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Triangular smoothing weighted <- smooth_triangular(x, y, m = 5) plot(weighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Loess smoothing loess <- smooth_loess(x, y, span = 0.75) plot(loess, type = \"l\", xlab = \"\", ylab = \"\")   ## Savitzky\u2013Golay filter savitzky <- smooth_savitzky(x, y, m = 21, p = 2) plot(savitzky, type = \"l\", xlab = \"\", ylab = \"\")   ## Whittaker smoothing whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2) plot(whittaker, type = \"l\", xlab = \"\", ylab = \"\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":null,"dir":"Reference","previous_headings":"","what":"Whittaker Smoothing \u2014 smooth_whittaker","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"Whittaker Smoothing","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"","code":"smooth_whittaker(x, y, ...)  # S4 method for numeric,numeric smooth_whittaker(x, y, lambda = 1600, d = 2, sparse = FALSE)  # S4 method for ANY,missing smooth_whittaker(x, lambda = 1600, d = 2, sparse = FALSE)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. lambda integer giving smoothing parameter. larger lambda , smoother curve. d integer specifying order penalty. sparse logical scalar: sparse matrices used computation? TRUE, Matrix required.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"Eilers, P. H. C. (2003). Perfect Smoother. Analytical Chemistry, 75(14): 3631-36. doi:10.1021/ac034173t .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"","code":"## Simulate data with some noise x <- seq(-4, 4, length = 100) y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)  ## Plot spectrum plot(x, y, type = \"l\", xlab = \"\", ylab = \"\")   ## Rectangular smoothing unweighted <- smooth_rectangular(x, y, m = 3) plot(unweighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Triangular smoothing weighted <- smooth_triangular(x, y, m = 5) plot(weighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Loess smoothing loess <- smooth_loess(x, y, span = 0.75) plot(loess, type = \"l\", xlab = \"\", ylab = \"\")   ## Savitzky\u2013Golay filter savitzky <- smooth_savitzky(x, y, m = 21, p = 2) plot(savitzky, type = \"l\", xlab = \"\", ylab = \"\")   ## Whittaker smoothing whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2) plot(whittaker, type = \"l\", xlab = \"\", ylab = \"\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset \u2014 subset","title":"Subset \u2014 subset","text":"signal_select() allows subset values x. signal_slice() allows subset position along x.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset \u2014 subset","text":"","code":"signal_select(x, y, ...)  signal_slice(x, y, ...)  # S4 method for numeric,numeric signal_select(x, y, from, to)  # S4 method for ANY,missing signal_select(x, from, to)  # S4 method for numeric,numeric signal_slice(x, y, subset)  # S4 method for ANY,missing signal_slice(x, subset)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset \u2014 subset","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. , numeric value giving first last value (x unit) selected. subset integer vector giving either positive values keep, negative values drop. values provided must either positive negative (coerced integer .integer()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset \u2014 subset","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Subset \u2014 subset","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset \u2014 subset","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Subset from 2.75 keV to 200 keV BEGe_1 <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe_1, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Subset from the 20th to the 1250th value BEGe_2 <- signal_slice(BEGe, subset = 20:1250)  ## Plot spectrum plot(BEGe_2, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":null,"dir":"Reference","previous_headings":"","what":"Sliding Windows \u2014 window_sliding","title":"Sliding Windows \u2014 window_sliding","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, progressively wider/narrower windows evaluated ends data series.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sliding Windows \u2014 window_sliding","text":"","code":"window_sliding(n, m, ...)  # S4 method for integer,integer window_sliding(n, m, i = NULL)  # S4 method for numeric,numeric window_sliding(n, m, i = NULL)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sliding Windows \u2014 window_sliding","text":"n integer giving length data series (coerced .integer() hence truncated toward zero). m odd integer giving window size, .e. number adjacent points used (coerced .integer() hence truncated toward zero). ... Currently used. vector integer specifying indices data points windows returned. NULL (default), windows evaluated data point.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sliding Windows \u2014 window_sliding","text":"Returns length-\\(n\\)  list integer vectors (indices data points window).","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sliding Windows \u2014 window_sliding","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sliding Windows \u2014 window_sliding","text":"","code":"## Length of the data series n <- 10  ## Progressive sliding windows sliding <- window_sliding(n = n, m = 5)  plot(NULL, xlim = c(1, n), ylim = c(1, 10.5), xlab = \"Index\", ylab = \"Window\") for (i in seq_along(sliding)) {   w <- sliding[[i]]   text(x = w, y = rep(i, length(w)), labels = w, pos = 3)   lines(w, rep(i, length(w)), type = \"l\", lwd = 2) }   ## Tumbling windows ## (compare with drop = TRUE) tumbling <- window_tumbling(n = n, m = 3, drop = FALSE)  plot(NULL, xlim = c(1, n), ylim = c(1, 5.5), xlab = \"Index\", ylab = \"Window\") for (i in seq_along(tumbling)) {   w <- tumbling[[i]]   text(x = w, y = rep(i, length(w)), labels = w, pos = 3)   lines(w, rep(i, length(w)), type = \"l\", lwd = 2) }","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":null,"dir":"Reference","previous_headings":"","what":"Tumbling Windows \u2014 window_tumbling","title":"Tumbling Windows \u2014 window_tumbling","text":"Tumbling Windows","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tumbling Windows \u2014 window_tumbling","text":"","code":"window_tumbling(n, m, ...)  # S4 method for integer,integer window_tumbling(n, m, drop = FALSE)  # S4 method for numeric,numeric window_tumbling(n, m, drop = FALSE)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tumbling Windows \u2014 window_tumbling","text":"n integer giving length data series (coerced .integer() hence truncated toward zero). m integer giving window size, .e. number adjacent points used (coerced .integer() hence truncated toward zero). ... Currently used. drop logical scalar: m multiple n, last data points removed windows length?","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tumbling Windows \u2014 window_tumbling","text":"Returns list integer vectors (indices data points window).","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tumbling Windows \u2014 window_tumbling","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tumbling Windows \u2014 window_tumbling","text":"","code":"## Length of the data series n <- 10  ## Progressive sliding windows sliding <- window_sliding(n = n, m = 5)  plot(NULL, xlim = c(1, n), ylim = c(1, 10.5), xlab = \"Index\", ylab = \"Window\") for (i in seq_along(sliding)) {   w <- sliding[[i]]   text(x = w, y = rep(i, length(w)), labels = w, pos = 3)   lines(w, rep(i, length(w)), type = \"l\", lwd = 2) }   ## Tumbling windows ## (compare with drop = TRUE) tumbling <- window_tumbling(n = n, m = 3, drop = FALSE)  plot(NULL, xlim = c(1, n), ylim = c(1, 5.5), xlab = \"Index\", ylab = \"Window\") for (i in seq_along(tumbling)) {   w <- tumbling[[i]]   text(x = w, y = rep(i, length(w)), labels = w, pos = 3)   lines(w, rep(i, length(w)), type = \"l\", lwd = 2) }","package":"alkahest"},{"path":[],"package":"tekton"},{"path":"https://packages.tesselle.org/tekton/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"pkgdown Template for tesselle Packages","text":"tekton provides custom pkgdown template tesselle packages. Please don\u2019t use package.","code":"","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"pkgdown Template for tesselle Packages","text":"can install latest version tekton repository :","code":"install.packages(\"tekton\", repos = \"https://tesselle.r-universe.dev\")","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"pkgdown Template for tesselle Packages","text":"Make sure _pkgdown.yaml contains: work properly, tekton requires assets templates inst/pkgdown/assets/. , set default_assets: false _pkgdown.yaml.","code":"template:   package: tekton","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/reference/test.html","id":null,"dir":"Reference","previous_headings":"","what":"Test Function \u2014 test","title":"Test Function \u2014 test","text":"Test Function","code":"","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/reference/test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test Function \u2014 test","text":"","code":"test(x = 1)","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/reference/test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test Function \u2014 test","text":"x value.","code":"","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/reference/test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test Function \u2014 test","text":"NULL","code":"","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/reference/test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test Function \u2014 test","text":"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor reprehenderit voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt culpa qui officia deserunt mollit anim id est laborum.","code":"","package":"tekton"}]
\ No newline at end of file
+[{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Quantitative Chronology in Archaeology","text":"ananke provides functions radiocarbon calibration chronological analysis.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Quantitative Chronology in Archaeology","text":"can install released version ananke CRAN : development version GitHub :","code":"install.packages(\"ananke\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/ananke\")","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Quantitative Chronology in Archaeology","text":"","code":"## Load packages library(ananke) ## Data from Bosch et al. 2015 data(\"ksarakil\")  ## Calibrate multiple dates cal <- c14_calibrate(   ages = ksarakil$date,   errors = ksarakil$error,   names = ksarakil$code,   curves = \"marine13\",   reservoir_offsets = 53,   reservoir_errors = 43,   from = 50000, to = 0 )  ## Plot plot(cal) plot(cal, density = FALSE, level = 0.95, lwd = 5)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Quantitative Chronology in Archaeology","text":"Please note ananke project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":[],"package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedAges-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","title":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","text":"S4 class represent calibrated radiocarbon ages.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedAges-class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","text":"ages numeric vector giving BP ages calibrated. errors numeric vector giving standard deviation ages calibrated. curves character vector specifying calibration curves used.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedAges-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","text":"F14C logical scalar: status integer vector specifying calibration status. must one \"0\" (OK), \"1\" (calibration range) \"2\" (may extend calibration range).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedAges-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","text":"class inherits TimeSeries.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedAges-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calibrated Radiocarbon Ages \u2014 CalibratedAges-class","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedSPD-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Calibrated SPD \u2014 CalibratedSPD-class","title":"Calibrated SPD \u2014 CalibratedSPD-class","text":"S4 class represent summed probability distributions (SPD) radiocarbon dates.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedSPD-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calibrated SPD \u2014 CalibratedSPD-class","text":"class inherits TimeSeries.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/CalibratedSPD-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calibrated SPD \u2014 CalibratedSPD-class","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":null,"dir":"Reference","previous_headings":"","what":"F14C \u2014 F14C","title":"F14C \u2014 F14C","text":"Converts F14C ratio 14C age.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"F14C \u2014 F14C","text":"","code":"BP14C_to_F14C(ages, errors, ...)  F14C_to_BP14C(ratios, errors, ...)  # S4 method for numeric,numeric BP14C_to_F14C(ages, errors)  # S4 method for numeric,numeric F14C_to_BP14C(ratios, errors)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"F14C \u2014 F14C","text":"ages numeric vector giving radiocarbon ages. errors numeric vector giving standard deviation ages/ratios. ... Currently used. ratios numeric vector giving F14C ratios.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"F14C \u2014 F14C","text":"two-column data.frame.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"F14C \u2014 F14C","text":"Bronk Ramsey, C. (2008). RADIOCARBON DATING: REVOLUTIONS UNDERSTANDING. Archaeometry, 50:249-275. doi:10.1111/j.1475-4754.2008.00394.x .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/F14C.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"F14C \u2014 F14C","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ProxyRecord-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Proxy Record \u2014 ProxyRecord-class","title":"Proxy Record \u2014 ProxyRecord-class","text":"S4 class store proxy records.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ProxyRecord-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Proxy Record \u2014 ProxyRecord-class","text":"class inherits TimeSeries.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ProxyRecord-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Proxy Record \u2014 ProxyRecord-class","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/RECE-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Radiocarbon Event Count Ensemble \u2014 RECE-class","title":"Radiocarbon Event Count Ensemble \u2014 RECE-class","text":"S4 class represent radiocarbon event count ensemble.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/RECE-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Radiocarbon Event Count Ensemble \u2014 RECE-class","text":"class inherits TimeSeries.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/RECE-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Radiocarbon Event Count Ensemble \u2014 RECE-class","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ananke-package.html","id":null,"dir":"Reference","previous_headings":"","what":"ananke: Quantitative Chronology in Archaeology \u2014 ananke-package","title":"ananke: Quantitative Chronology in Archaeology \u2014 ananke-package","text":"package (one paragraph).","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ananke-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"ananke: Quantitative Chronology in Archaeology \u2014 ananke-package","text":"ananke uses following options() configure behavior: ananke.calendar: TimeScale object (default calendar printing). ananke.grid: numeric value specifying number equally spaced points densities estimated (defaults \\(512\\)). power \\(2\\). ananke.progress: logical scalar. progress bars displayed? ananke.verbose: logical scalar. R report extra information progress? Defaults TRUE.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ananke-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"ananke: Quantitative Chronology in Archaeology \u2014 ananke-package","text":"Full list authors contributors (alphabetic order): Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":null,"dir":"Reference","previous_headings":"","what":"14C Calibration \u2014 c14_calibrate","title":"14C Calibration \u2014 c14_calibrate","text":"Calibrates radiocarbon dates.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"14C Calibration \u2014 c14_calibrate","text":"","code":"c14_calibrate(ages, errors, ...)  # S4 method for numeric,numeric c14_calibrate(   ages,   errors,   names = NULL,   curves = \"intcal20\",   reservoir_offsets = 0,   reservoir_errors = 0,   from = 55000,   to = 0,   resolution = 1,   normalize = TRUE,   F14C = FALSE,   drop = TRUE,   eps = 1e-06 )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"14C Calibration \u2014 c14_calibrate","text":"ages numeric vector giving BP ages calibrated. errors numeric vector giving standard deviation ages calibrated. ... Currently used. names character vector specifying names ages (e.g. laboratory codes). curves character vector specifying calibration curve used. Different curves can specified per dated sample. reservoir_offsets numeric vector giving offset values marine reservoir effect (defaults 0; .e. offset). reservoir_errors numeric vector giving offset value errors marine reservoir effect (defaults 0; .e. offset). length-one numeric vector specifying earliest data calibrate , cal. BP years. length-one numeric vector specifying latest data calibrate , cal. BP years. resolution length-one numeric vector specifying temporal resolution (years) calibration. normalize logical scalar: calibration normalized? F14C logical scalar: calibration carried F14C space? drop logical scalar: years zero probability discarded? TRUE (default), results narrower time range. eps length-one numeric value giving cutoff calibration values removed.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"14C Calibration \u2014 c14_calibrate","text":"CalibratedAges object.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"14C Calibration \u2014 c14_calibrate","text":"Adapted Bchron::BchronCalibrate() Andrew Parnell rcarbon::calibrate() Andrew Bevan Enrico Crema.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"14C Calibration \u2014 c14_calibrate","text":"Bronk Ramsey, C. (2008). RADIOCARBON DATING: REVOLUTIONS UNDERSTANDING. Archaeometry, 50:249-275. doi:10.1111/j.1475-4754.2008.00394.x .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"14C Calibration \u2014 c14_calibrate","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_calibrate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"14C Calibration \u2014 c14_calibrate","text":"","code":"## Calibrate a single date cal <- c14_calibrate(300, 20) plot(cal, panel.first = graphics::grid())   ## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") ) plot(cal, calendar = BP(), panel.first = graphics::grid())  plot(cal, interval = FALSE)  plot(cal[, 1, ], col.interval = \"red\")   plot(cal, density = FALSE, level = 0.68, lwd = 5)  plot(cal, density = FALSE, level = 0.95, lwd = 5)   # \\donttest{ ## Out of 14C range? out <- c14_calibrate(130, 20) #> Warning: Date \u201cX1\u201d may extent out of range. plot(out) #> Warning: Date \u201cX1\u201d may extent out of range.  # }","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":null,"dir":"Reference","previous_headings":"","what":"14C Calibration \u2014 c14_combine","title":"14C Calibration \u2014 c14_combine","text":"Combines radiocarbon dates.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"14C Calibration \u2014 c14_combine","text":"","code":"c14_combine(ages, errors, ...)  # S4 method for numeric,numeric c14_combine(ages, errors, groups = NULL)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"14C Calibration \u2014 c14_combine","text":"ages numeric vector giving BP ages calibrated. errors numeric vector giving standard deviation ages calibrated. ... Currently used. groups factor sense .factor(groups) defines groups combine . NULL (default), dates combined. NAs treated isolated dates.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"14C Calibration \u2014 c14_combine","text":"data.frame following columns:","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"14C Calibration \u2014 c14_combine","text":"Ward, G. K. Wilson, S. R. (1978). Procedures Comparing Combining Radiocarbon Age Determinations: Critique. Archaeometry 20(1): 19\u201131. doi:10.1111/j.1475-4754.1978.tb00208.x .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"14C Calibration \u2014 c14_combine","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_combine.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"14C Calibration \u2014 c14_combine","text":"","code":"## Replicate Ward and Wilson (1978), p. 28 polach1972 <- data.frame(   samples = c(\"ANU-7\", \"ANU-7\", \"ANU-7\", \"W-1571\", \"ANU-5\",               \"C-800\", \"L-698D\", \"FSU-3\", \"Tx-44\"),   ages = c(14550, 15000, 13700, 14650, 11700, 10860, 11840, 11245, 10700),   errors = c(270, 600, 300, 500, 260, 410, 100, 450, 210) )  c14_combine(   ages = polach1972$ages,   errors = polach1972$errors,   groups = polach1972$samples ) #>   groups     ages  errors     chi2         p #> 1 W-1571 14650.00 500.000       NA        NA #> 2  ANU-5 11700.00 260.000       NA        NA #> 3  C-800 10860.00 410.000       NA        NA #> 4 L-698D 11840.00 100.000       NA        NA #> 5  FSU-3 11245.00 450.000       NA        NA #> 6  Tx-44 10700.00 210.000       NA        NA #> 7  ANU-7 14253.17 190.325 6.157902 0.1041756","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":null,"dir":"Reference","previous_headings":"","what":"14C Calibration Curve \u2014 c14_curve","title":"14C Calibration Curve \u2014 c14_curve","text":"14C Calibration Curve","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"14C Calibration Curve \u2014 c14_curve","text":"","code":"c14_curve(object, ...)  # S4 method for character c14_curve(object)  # S4 method for CalibratedAges c14_curve(object)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"14C Calibration Curve \u2014 c14_curve","text":"object character vector naming calibration curves (see details). ... Currently used.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"14C Calibration Curve \u2014 c14_curve","text":"list three-column data.frame: calibrated age BP, uncalibrated age BP standard deviation.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"14C Calibration Curve \u2014 c14_curve","text":"following calibration curves available:","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"14C Calibration Curve \u2014 c14_curve","text":"Heaton, Timothy J, Peter K\u00f6hler, Martin Butzin, Edouard Bard, Ron W Reimer, William E N Austin, Christopher Bronk Ramsey, et al. (2020). Marine20 Marine Radiocarbon Age Calibration Curve (0-55,000 Cal BP). Radiocarbon, 62(4): 779-820. doi:10.1017/RDC.2020.68 . Hogg, Alan G, Timothy J Heaton, Quan Hua, Jonathan G Palmer, Chris SM Turney, John Southon, Alex Bayliss, et al. (2020). SHCal20 Southern Hemisphere Calibration, 0-55,000 Years Cal BP. Radiocarbon, 62(4): 759-78. doi:10.1017/RDC.2020.59 . Hogg, Alan G, Quan Hua, Paul G Blackwell, Mu Niu, Caitlin E Buck, Thomas P Guilderson, Timothy J Heaton, et al. (2013). SHCal13 Southern Hemisphere Calibration, 0-50,000 Years Cal BP. Radiocarbon, 55(4): 1889-1903. doi:10.2458/azu_js_rc.55.16783 . Hua, Quan, Mike Barbetti (2004). Review Tropospheric Bomb 14C Data Carbon Cycle Modeling Age Calibration Purposes. Radiocarbon, 46(3): 1273-1298. doi:10.1017/S0033822200033142 . Hua, Quan, Mike Barbetti, Andrzej Z Rakowski (2013). Atmospheric Radiocarbon Period 1950-2010. Radiocarbon, 55(4): 2059\u20112072. doi:10.2458/azu_js_rc.v55i2.16177 . Hua, Quan, Jocelyn C Turnbull, Guaciara M Santos, Andrzej Z Rakowski, Santiago Ancapich\u00fan, Ricardo De Pol-Holz, Samuel Hammer, et al. (2022). Atmospheric Radiocarbon Period 1950-2019. Radiocarbon, 64(4): 723\u2011745. doi:10.1017/RDC.2021.95 . Hughen, K., S. Lehman, J. Southon, J. Overpeck, O. Marchal, C. Herring, J. Turnbull (2004). 14C Activity Global Carbon Cycle Changes Past 50,000 Years. Science, 303(5655): 202\u2011207. doi:10.1126/science.1090300 . Hughen, Konrad , Mike G L Baillie, Edouard Bard, J Warren Beck, Chanda J H Bertrand, Paul G Blackwell, Caitlin E Buck, et al. (2004). Marine04 Marine Radiocarbon Age Calibration, 0-26 cal kyr BP. Radiocarbon, 46(3): 1059\u20111086. doi:10.1017/S0033822200033002 . Kueppers, Lara M., John Southon, Paul Baer, John Harte (2004). Dead Wood Biomass Turnover Time, Measured Radiocarbon, along Subalpine Elevation Gradient. Oecologia, 141(4): 641\u2011651. doi:10.1007/s00442-004-1689-x . McCormac, F G, G Hogg, P G Blackwell, C E Buck, T F G Higham, P J Reimer (2004). Shcal04 Southern Hemisphere Calibration, 0-11.0 cal kyr BP. Radiocarbon, 46(3): 1087\u20111092. doi:10.1017/S0033822200033014 . Reimer, P J, M G L Baillie, E Bard, Bayliss, J W Beck, P G Blackwell, C Bronk Ramsey, et al. (2009). IntCal09 Marine09 Radiocarbon Age Calibration Curves, 0-50,000 Years cal BP. Radiocarbon, 51(4): 1111\u20111150. doi:10.1017/S0033822200034202 . Reimer, Paula J, William E N Austin, Edouard Bard, Alex Bayliss, Paul G Blackwell, Christopher Bronk Ramsey, Martin Butzin, et al. (2020). IntCal20 Northern Hemisphere Radiocarbon Age Calibration Curve (0-55 cal kBP). Radiocarbon, 62(4): 725\u2011757. doi:10.1017/RDC.2020.41 . Reimer, Paula J, Mike G L Baillie, Edouard Bard, Alex Bayliss, J Warren Beck, Chanda J H Bertrand, Paul G Blackwell, et al. (2004). Intcal04 Terrestrial Radiocarbon Age Calibration, 0-26 cal kyr BP. Radiocarbon, 46(3): 1029\u20111058. doi:10.1017/S0033822200032999 . Reimer, Paula J, Edouard Bard, Alex Bayliss, J Warren Beck, Paul G Blackwell, Christopher Bronk Ramsey, Caitlin E Buck, et al. (2013). IntCal13 Marine13 Radiocarbon Age Calibration Curves 0-50,000 Years cal BP. Radiocarbon, 55(4): 1869\u20111887. doi:10.2458/azu_js_rc.55.16947 . Stuiver, Minze, Paula J. Reimer, Edouard Bard, J. Warren Beck, G. S. Burr, Konrad . Hughen, Bernd Kromer, Gerry McCormac, Johannes Van Der Plicht, Marco Spurk (1998). INTCAL98 Radiocarbon Age Calibration, 24,000-0 cal BP. Radiocarbon, 40(3): 1041\u20111083. doi:10.1017/S0033822200019123 . Stuiver, Minze, Paula J. Reimer, Thomas F. Braziunas. (1998). High-Precision Radiocarbon Age Calibration Terrestrial Marine Samples. Radiocarbon, 40(3): 1127\u20111151. doi:10.1017/S0033822200019172 .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"14C Calibration Curve \u2014 c14_curve","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_curve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"14C Calibration Curve \u2014 c14_curve","text":"","code":"## IntCal20 intcal20 <- c14_curve(\"intcal20\") head(intcal20[[1]]) #>   CALBP   AGE ERROR #> 1 55000 50100  1024 #> 2 54980 50081  1018 #> 3 54960 50063  1013 #> 4 54940 50043  1007 #> 5 54920 50027  1003 #> 6 54900 50009   997  ## IntCal intcal <- c14_curve(c(\"intcal09\", \"intcal13\", \"intcal20\")) lapply(X = intcal, FUN = head) #> $intcal09 #>   CALBP   AGE ERROR #> 1 50000 46400   415 #> 2 49900 46316   410 #> 3 49800 46229   403 #> 4 49700 46135   396 #> 5 49600 46036   389 #> 6 49500 45933   380 #>  #> $intcal13 #>   CALBP   AGE ERROR #> 1 50000 46401   274 #> 2 49980 46381   273 #> 3 49960 46362   271 #> 4 49940 46343   270 #> 5 49920 46324   269 #> 6 49900 46305   268 #>  #> $intcal20 #>   CALBP   AGE ERROR #> 1 55000 50100  1024 #> 2 54980 50081  1018 #> 3 54960 50063  1013 #> 4 54940 50043  1007 #> 5 54920 50027  1003 #> 6 54900 50009   997 #>","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":null,"dir":"Reference","previous_headings":"","what":"Radiocarbon Event Count \u2014 c14_ensemble","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"Radiocarbon Event Count","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"","code":"c14_ensemble(object, ...)  # S4 method for CalibratedAges c14_ensemble(   object,   from = NULL,   to = NULL,   by = 10,   n = 100,   calendar = BP(),   progress = getOption(\"ananke.progress\") )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"object CalibratedAges object. ... Currently used. length-one numeric vector specifying earliest data calibrate (cal BP years). length-one numeric vector specifying latest data calibrate (cal BP years). length-one numeric vector specifying temporal resolution (years) calibration. n integer specifying number item choose randomly. calendar TimeScale object specifying calendar (see calendar()). Defaults CE(). progress logical scalar: progress bar displayed?","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"RECE object.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"Carleton, W. C. (2021). Evaluating Bayesian Radiocarbon\u2010dated Event Count (REC) Models Study Long\u2010term Human Environmental Processes. Journal Quaternary Science, 36(1): 110\u201123. doi:10.1002/jqs.3256 .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_ensemble.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Radiocarbon Event Count \u2014 c14_ensemble","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"Plot Calibrated Radiocarbon Ages","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"","code":"# S4 method for CalibratedAges,missing plot(   x,   calendar = getOption(\"ananke.calendar\"),   density = TRUE,   interval = TRUE,   level = 0.954,   sort = TRUE,   decreasing = TRUE,   main = NULL,   sub = NULL,   axes = TRUE,   frame.plot = FALSE,   ann = graphics::par(\"ann\"),   panel.first = NULL,   panel.last = NULL,   col.density = \"grey\",   col.interval = \"#77AADD\",   ... )  # S4 method for CalibratedSPD,missing plot(   x,   calendar = getOption(\"ananke.calendar\"),   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = FALSE,   panel.first = NULL,   panel.last = NULL,   ... )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"x CalibratedAges CalibratedSPD object. calendar TimeScale object specifying target calendar (see calendar()). density logical scalar: density drawn? interval logical scalar: highest density region drawn? level length-one numeric vector giving confidence level. used interval TRUE. sort logical scalar: data sorted? decreasing logical scalar: sort order decreasing? used sort TRUE. main character string giving main title plot. sub character string giving subtitle plot. axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? ann logical scalar: default annotation (title x y labels) appear plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. col.density, col.interval specification plotting colors. ... graphical parameters may also passed arguments function.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"plot() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Calibrated Radiocarbon Ages \u2014 c14_plot","text":"","code":"## Calibrate a single date cal <- c14_calibrate(300, 20) plot(cal, panel.first = graphics::grid())   ## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") ) plot(cal, calendar = BP(), panel.first = graphics::grid())  plot(cal, interval = FALSE)  plot(cal[, 1, ], col.interval = \"red\")   plot(cal, density = FALSE, level = 0.68, lwd = 5)  plot(cal, density = FALSE, level = 0.95, lwd = 5)   # \\donttest{ ## Out of 14C range? out <- c14_calibrate(130, 20) #> Warning: Date \u201cX1\u201d may extent out of range. plot(out) #> Warning: Date \u201cX1\u201d may extent out of range.  # }","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_spd.html","id":null,"dir":"Reference","previous_headings":"","what":"Summed Probability Distributions \u2014 c14_spd","title":"Summed Probability Distributions \u2014 c14_spd","text":"Computes summed probability distributions (SPD) radiocarbon dates.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_spd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summed Probability Distributions \u2014 c14_spd","text":"","code":"c14_spd(object, ...)  # S4 method for CalibratedAges c14_spd(object, normalize_date = FALSE, normalize_spd = FALSE)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_spd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summed Probability Distributions \u2014 c14_spd","text":"object CalibratedAges object. ... Currently used. normalize_date logical scalar: total probability mass calibrated dates normalised (sum unity within time-span analysis)? normalize_spd logical scalar: total probability mass SPD normalised (sum unity)?","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_spd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summed Probability Distributions \u2014 c14_spd","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_spd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summed Probability Distributions \u2014 c14_spd","text":"","code":"## Radiocarbon data from Bosch et al. 2015 data(\"ksarakil\")  ## Calibrate cal <- c14_calibrate(   ages = ksarakil$date,   errors = ksarakil$error,   names = ksarakil$code,   curves = \"marine13\",   reservoir_offsets = 53,   reservoir_errors = 43,   from = 50000, to = 0 ) plot(cal, level = 0.68, flip = TRUE) #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter #> Warning: \"flip\" is not a graphical parameter   ## SPD s <- c14_spd(cal) plot(s)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_uncalibrate.html","id":null,"dir":"Reference","previous_headings":"","what":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","title":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","text":"Uncalibrate Radiocarbon Date","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_uncalibrate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","text":"","code":"c14_uncalibrate(object, ...)  # S4 method for numeric c14_uncalibrate(object, curves = \"intcal20\")  # S4 method for CalibratedAges c14_uncalibrate(object, ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_uncalibrate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","text":"object CalibratedAges object numeric vector calibrated ages (BP). ... Currently used. curves description","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_uncalibrate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/c14_uncalibrate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Uncalibrate a Radiocarbon Date \u2014 c14_uncalibrate","text":"","code":"if (FALSE) { ## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  ## Uncalibrate c14_uncalibrate(cal) }","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Data Frame \u2014 data.frame","title":"Coerce to a Data Frame \u2014 data.frame","text":"Coerce Data Frame","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Data Frame \u2014 data.frame","text":"","code":"# S4 method for CalibratedAges as.data.frame(x, ..., calendar = getOption(\"ananke.calendar\"))  # S4 method for RECE as.data.frame(x, ..., calendar = getOption(\"ananke.calendar\"))  # S4 method for ProxyRecord as.data.frame(x, ..., calendar = getOption(\"ananke.calendar\"))","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Data Frame \u2014 data.frame","text":"x object. ... parameters passed data.frame(). calendar TimeScale object specifying target calendar (see calendar()). NULL, rata die returned.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Data Frame \u2014 data.frame","text":"data.frame extra time column.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Data Frame \u2014 data.frame","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/data.frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to a Data Frame \u2014 data.frame","text":"","code":"## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  head(as.data.frame(cal)) #>    time            X Y #> 1 -3984 1.110191e-06 0 #> 2 -3983 1.434164e-06 0 #> 3 -3982 1.845486e-06 0 #> 4 -3981 2.365558e-06 0 #> 5 -3980 3.020421e-06 0 #> 6 -3979 3.589420e-06 0 head(as.data.frame(cal, calendar = BP())) #>   time            X Y #> 1 5934 1.110191e-06 0 #> 2 5933 1.434164e-06 0 #> 3 5932 1.845486e-06 0 #> 4 5931 2.365558e-06 0 #> 5 5930 3.020421e-06 0 #> 6 5929 3.589420e-06 0 head(as.data.frame(cal, calendar = NULL)) #>       time            X Y #> 1 -1455491 1.110191e-06 0 #> 2 -1455125 1.434164e-06 0 #> 3 -1454760 1.845486e-06 0 #> 4 -1454395 2.365558e-06 0 #> 5 -1454030 3.020421e-06 0 #> 6 -1453664 3.589420e-06 0","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":null,"dir":"Reference","previous_headings":"","what":"Highest Density Regions \u2014 hdr","title":"Highest Density Regions \u2014 hdr","text":"Highest Density Regions","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Highest Density Regions \u2014 hdr","text":"","code":"# S4 method for CalibratedAges,missing interval_hdr(x, level = 0.954, calendar = getOption(\"ananke.calendar\"), ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Highest Density Regions \u2014 hdr","text":"x CalibratedAges object. level length-one numeric vector giving confidence level. calendar TimeScale object specifying target calendar (see calendar()). ... Currently used.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Highest Density Regions \u2014 hdr","text":"Returns list numeric  matrix.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Highest Density Regions \u2014 hdr","text":"Hyndman, R. J. (1996). Computing graphing highest density regions. American Statistician, 50: 120-126. doi:10.2307/2684423 .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Highest Density Regions \u2014 hdr","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/interval_hdr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Highest Density Regions \u2014 hdr","text":"","code":"## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  ## HDR interval_hdr(cal, level = 0.683) #> $X #>      start   end    p #> [1,] -3911 -3876 0.15 #> [2,] -3802 -3706 0.50 #> [3,] -3669 -3659 0.04 #>  #> $Y #>      start   end    p #> [1,] -3336 -3310 0.11 #> [2,] -3297 -3283 0.06 #> [3,] -3272 -3267 0.02 #> [4,] -3240 -3207 0.14 #> [5,] -3194 -3103 0.37 #>  interval_hdr(cal, level = 0.954) #> $X #>      start   end    p #> [1,] -3944 -3855 0.29 #> [2,] -3844 -3833 0.02 #> [3,] -3818 -3653 0.65 #>  #> $Y #>      start   end    p #> [1,] -3355 -3091 0.94 #> [2,] -3050 -3040 0.01 #>  interval_hdr(cal, level = 0.997) #> $X #>      start   end p #> [1,] -3953 -3647 1 #>  #> $Y #>      start   end p #> [1,] -3366 -3014 1 #>","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ksarakil.html","id":null,"dir":"Reference","previous_headings":"","what":"Ks\u00e2r 'Akil Radiocarbon Dates \u2014 ksarakil","title":"Ks\u00e2r 'Akil Radiocarbon Dates \u2014 ksarakil","text":"Ks\u00e2r 'Akil Radiocarbon Dates","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ksarakil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ks\u00e2r 'Akil Radiocarbon Dates \u2014 ksarakil","text":"","code":"ksarakil","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ksarakil.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Ks\u00e2r 'Akil Radiocarbon Dates \u2014 ksarakil","text":"data.frame 16 rows 5 variables: code Laboratory code. date Radiocarbon date (year BP). error Radiocarbon error (year). layer Stratigraphic layer. phase Chronological phase.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/ksarakil.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Ks\u00e2r 'Akil Radiocarbon Dates \u2014 ksarakil","text":"Bosch, M. D., Mannino, M. ., Prendergast, . L., O'Connell, T. C., Demarchi, B., Taylor, S. M., Niven, L., van der Plicht, J. Hublin, J.-J. (2015). New Chronology Ks\u00e2r 'Akil (Lebanon) Supports Levantine Route Modern Human Dispersal Europe. Proceedings National Academy Sciences 112(25): 7683-8. doi:10.1073/pnas.1501529112 .","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Find Labels from Object \u2014 labels","title":"Find Labels from Object \u2014 labels","text":"Find suitable set labels object use printing plotting, example.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find Labels from Object \u2014 labels","text":"","code":"# S4 method for CalibratedAges labels(object, ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find Labels from Object \u2014 labels","text":"object object find labels. ... Currently used.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find Labels from Object \u2014 labels","text":"character vector.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/labels.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Find Labels from Object \u2014 labels","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/lia_age.html","id":null,"dir":"Reference","previous_headings":"","what":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"Compute geological model age (T) U/Pb (mu) Th/U (kappa) ratios lead isotopic measurements.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/lia_age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"","code":"lia_age(x, y, z, ...)  # S4 method for numeric,numeric,numeric lia_age(   x,   y,   z,   t0 = 3.8,   x_star = 18.75,   y_star = 15.63,   z_star = 38.86,   mu = 9.66,   kappa = 3.9,   th232 = 0.049475,   u238 = 0.155125,   u235 = 0.98485,   u238_235 = 137.79,   tolerance = sqrt(.Machine$double.eps) )  # S4 method for list,missing,missing lia_age(   x,   t0 = 3.8,   x_star = 18.75,   y_star = 15.63,   z_star = 38.86,   mu = 9.66,   kappa = 3.9,   th232 = 0.049475,   u238 = 0.155125,   u235 = 0.98485,   u238_235 = 137.79,   tolerance = sqrt(.Machine$double.eps) )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/lia_age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"x numeric vector 206Pb/204Pb ratios. y z missing, must list (data.frame) numeric components (columns) x, y z. y numeric vector 207Pb/204Pb ratios.  missing, attempt made interpret x suitable way. z numeric vector 208Pb/204Pb ratios.  missing, attempt made interpret x suitable way. ... Currently used. t0 numeric value giving time second stage reference model. x_star numeric value giving 206Pb/204Pb ratio \\(t = 0\\). y_star numeric value giving 207Pb/204Pb ratio \\(t = 0\\). z_star numeric value giving 208Pb/204Pb ratio \\(t = 0\\). mu numeric value giving 238U/204Pb ratio reference model. kappa numeric value giving 232Th/238U ratio reference model. th232 numeric value giving decay constants 232Th. u238 numeric value giving decay constants 238U. u235 numeric value giving decay constants 235U. u238_235 numeric value giving 238U/235U ratio. tolerance numeric value specifying tolerance (stopping criteria Newton\u2013Raphson method).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/lia_age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"four columns data.frame: age Geological model age (Ma). mu 238U/204Pb ratio. kappa 232Th/238U ratio. f","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/lia_age.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"Reference values Albarede & Juteau (1984).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/lia_age.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"Albar\u00e8de, F., Desaulty, .-M. & Blichert-Toft, J. (2012). Geological Perspective Use Pb Isotopes Archaeometry. Archaeometry, 54: 853-867. doi:10.1111/j.1475-4754.2011.00653.x . Albar\u00e8de, F. & Juteau, M. (1984). Unscrambling Lead Model Ages. Geochimica et Cosmochimica Acta, 48(1): 207-12. doi:10.1016/0016-7037(84)90364-8 .","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/lia_age.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"N. Frerebeau, F. Albarede (original Matlab code)","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/lia_age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"","code":"Pb <- data.frame(   x = c(18.23247, 18.22936, 18.23102), # Pb206/Pb204   y = c(15.65199, 15.65216, 15.65097), # Pb207/Pb204   z = c(38.5167, 38.51516, 38.51601)   # Pb208/Pb204 )  lia_age(Pb) #>        age       mu    kappa            f #> 1 428.3959 9.859142 4.074891 1.004859e-14 #> 2 430.9796 9.860659 4.076292 1.004859e-14 #> 3 427.6003 9.855397 4.075178 1.004859e-14","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean \u2014 mean","title":"Mean \u2014 mean","text":"Mean","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean \u2014 mean","text":"","code":"# S4 method for CalibratedAges mean(x, na.rm = FALSE, ..., calendar = getOption(\"ananke.calendar\"))  # S4 method for ProxyRecord mean(x, na.rm = FALSE, ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean \u2014 mean","text":"x CalibratedAges object. na.rm logical scalar: NA values stripped computation proceeds? ... Currently used. calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean \u2014 mean","text":"numeric vector.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean \u2014 mean","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean \u2014 mean","text":"","code":"## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  ## Statistics quantile(cal) #>      0%   25%   50%   75%  100% #> X -3984 -3876 -3778 -3729 -3545 #> Y -3984 -3283 -3211 -3145 -2917 median(cal) #>     X     Y  #> -3778 -3211  mean(cal) #>         X         Y  #> -3792.679 -3211.160   ## Plot plot(cal, calendar = CE())  ## Need to set 'calendar' abline(v = median(cal, calendar = CE()), lty = 2, col = \"blue\") abline(v = mean(cal, calendar = CE()), lty = 2, col = \"red\")","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":null,"dir":"Reference","previous_headings":"","what":"Median \u2014 median","title":"Median \u2014 median","text":"Median","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Median \u2014 median","text":"","code":"# S4 method for CalibratedAges median(x, na.rm = FALSE, ..., calendar = getOption(\"ananke.calendar\"))","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Median \u2014 median","text":"x CalibratedAges object. na.rm logical scalar: NA values stripped computation proceeds? ... Currently used. calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Median \u2014 median","text":"numeric vector.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Median \u2014 median","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/median.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Median \u2014 median","text":"","code":"## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  ## Statistics quantile(cal) #>      0%   25%   50%   75%  100% #> X -3984 -3876 -3778 -3729 -3545 #> Y -3984 -3283 -3211 -3145 -2917 median(cal) #>     X     Y  #> -3778 -3211  mean(cal) #>         X         Y  #> -3792.679 -3211.160   ## Plot plot(cal, calendar = CE())  ## Need to set 'calendar' abline(v = median(cal, calendar = CE()), lty = 2, col = \"blue\") abline(v = mean(cal, calendar = CE()), lty = 2, col = \"red\")","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mutators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or Set Parts of an Object \u2014 mutators","title":"Get or Set Parts of an Object \u2014 mutators","text":"Getters setters extract replace parts object.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mutators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or Set Parts of an Object \u2014 mutators","text":"x object get set element(s). value possible value element(s) x.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mutators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get or Set Parts of an Object \u2014 mutators","text":"object sort x new values assigned.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/mutators.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get or Set Parts of an Object \u2014 mutators","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":null,"dir":"Reference","previous_headings":"","what":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"Represents layer-counted proxy records sequences probability distributions absolute, error-free time axes.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"","code":"proxy_ensemble(depth, ...)  # S4 method for numeric proxy_ensemble(   depth,   proxy,   proxy_error,   step,   time,   time_error,   calendar,   from = NULL,   to = NULL,   by = NULL,   n = 30,   progress = getOption(\"ananke.progress\"),   verbose = getOption(\"ananke.verbose\") )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"depth positive numeric vector giving depth proxy values calendar ages measured. must decreasing order (.e. chronological order). ... Currently used. proxy numeric vector giving proxy values. proxy_error numeric vector giving proxy uncertainties. step length-one numeric vector specifying step size (units proxy) proxy records densities estimated. time numeric vector giving calendar ages (years). time_error numeric vector giving calendar age uncertainties (years). calendar TimeScale object specifying calendar time (see calendar()). length-one numeric vector specifying starting value temporal sequence densities estimated (years). length-one numeric vector specifying end value temporal sequence densities estimated (cal BP years). length-one numeric vector specifying increment temporal sequence densities estimated (years). n integer specifying number item choose randomly. progress logical scalar: progress bar displayed? verbose logical scalar: extra information reported?","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"ProxyRecord object.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"Boers, N., Goswami, B. & Ghil, M. (2017). Complete Representation Uncertainties Layer-Counted Paleoclimatic Archives. Climate Past, 13(9): 1169-1180. doi:10.5194/cp-13-1169-2017 .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_ensemble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layer-Counted Proxy Records Uncertainties \u2014 proxy_ensemble","text":"","code":"if (FALSE) { ## Get NGRIP records data(\"ngrip2010\", package = \"folio\") ngrip2010 <- subset(ngrip2010, !is.na(MCE)) ngrip2010 <- ngrip2010[nrow(ngrip2010):1, ] # Sort in chronological order  ## Replicate fig. 3d from Boers et al. (2017) ## /!\\ This may take a while... /!\\ ngrip_record <- proxy_ensemble(   depth = ngrip2010$depth,   proxy = ngrip2010$delta,   proxy_error = 0.01,   step = 0.001,   time = ngrip2010$age,   time_error = ngrip2010$MCE,   calendar = b2k(), # /!\\   by = 20,   n = 30 )  plot(ngrip_record) }","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"Plot Layer-Counted Proxy Records Uncertainties","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"","code":"# S4 method for ProxyRecord,missing plot(   x,   calendar = getOption(\"ananke.calendar\"),   iqr = TRUE,   xlab = NULL,   ylab = NULL,   col = grDevices::hcl.colors(12, \"YlOrRd\", rev = TRUE),   col.mean = \"black\",   col.iqr = col.mean,   lty.mean = 1,   lty.iqr = 3,   lwd.mean = 2,   lwd.iqr = lwd.mean,   ... )","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"x ProxyRecord object. calendar TimeScale object specifying target calendar (see calendar()). iqr logical scalar: mean IQR displayed? xlab, ylab character string giving label x y axis. col list colors generated grDevices::hcl.colors(). col.mean, col.iqr specification line colors. used iqr TRUE. lty.mean, lty.iqr specification line types. used iqr TRUE. lwd.mean, lwd.iqr specification line widths. used iqr TRUE. ... parameters passed graphics::image().","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"plot() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/proxy_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Layer-Counted Proxy Records Uncertainties \u2014 proxy_plot","text":"","code":"if (FALSE) { ## Get NGRIP records data(\"ngrip2010\", package = \"folio\") ngrip2010 <- subset(ngrip2010, !is.na(MCE)) ngrip2010 <- ngrip2010[nrow(ngrip2010):1, ] # Sort in chronological order  ## Replicate fig. 3d from Boers et al. (2017) ## /!\\ This may take a while... /!\\ ngrip_record <- proxy_ensemble(   depth = ngrip2010$depth,   proxy = ngrip2010$delta,   proxy_error = 0.01,   step = 0.001,   time = ngrip2010$age,   time_error = ngrip2010$MCE,   calendar = b2k(), # /!\\   by = 20,   n = 30 )  plot(ngrip_record) }","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Quantiles of a Density Estimate \u2014 quantile","title":"Quantiles of a Density Estimate \u2014 quantile","text":"Quantiles Density Estimate","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quantiles of a Density Estimate \u2014 quantile","text":"","code":"# S4 method for CalibratedAges quantile(   x,   probs = seq(0, 1, 0.25),   na.rm = FALSE,   ...,   calendar = getOption(\"ananke.calendar\") )  # S4 method for ProxyRecord quantile(x, probs = seq(0, 1, 0.25), na.rm = FALSE, ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quantiles of a Density Estimate \u2014 quantile","text":"x CalibratedAges object. probs numeric vector probabilities values \\([0,1]\\). na.rm logical scalar: NA values stripped computation proceeds? ... Currently used. calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quantiles of a Density Estimate \u2014 quantile","text":"numeric  matrix containing quantiles.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Quantiles of a Density Estimate \u2014 quantile","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Quantiles of a Density Estimate \u2014 quantile","text":"","code":"## Calibrate multiple dates cal <- c14_calibrate(   ages = c(5000, 4500),   errors = c(45, 35),   names = c(\"X\", \"Y\") )  ## Statistics quantile(cal) #>      0%   25%   50%   75%  100% #> X -3984 -3876 -3778 -3729 -3545 #> Y -3984 -3283 -3211 -3145 -2917 median(cal) #>     X     Y  #> -3778 -3211  mean(cal) #>         X         Y  #> -3792.679 -3211.160   ## Plot plot(cal, calendar = CE())  ## Need to set 'calendar' abline(v = median(cal, calendar = CE()), lty = 2, col = \"blue\") abline(v = mean(cal, calendar = CE()), lty = 2, col = \"red\")","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"Plot Radiocarbon Event Count Ensemble","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"","code":"# S4 method for RECE,missing plot(x, calendar = getOption(\"ananke.calendar\"), ...)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"x RECE object. calendar TimeScale object specifying target calendar (see calendar()). ... parameters passed graphics::image().","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"image() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"Carleton, W. C. (2021). Evaluating Bayesian Radiocarbon\u2010dated Event Count (REC) Models Study Long\u2010term Human Environmental Processes. Journal Quaternary Science, 36(1): 110\u201123. doi:10.1002/jqs.3256 .","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/rec_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot a Radiocarbon Event Count Ensemble \u2014 rec_plot","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages \u2014 reexports","title":"Objects exported from other packages \u2014 reexports","text":"objects imported packages. Follow links see documentation. aion AD, b2k, BC, BCE, BP, calendar, CE, image, plot, start, start, time, year_axis arkhe interval_hdr","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object \u2014 subset","title":"Extract or Replace Parts of an Object \u2014 subset","text":"Operators acting objects extract replace parts.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object \u2014 subset","text":"","code":"# S4 method for CalibratedAges [(x, i, j, k, drop = FALSE)","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object \u2014 subset","text":"x object extract element(s) replace element(s). , j, k Indices specifying elements extract replace. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement.","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object \u2014 subset","text":"subsetted object.","code":"","package":"ananke"},{"path":[],"package":"ananke"},{"path":"https://packages.tesselle.org/ananke/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object \u2014 subset","text":"N. Frerebeau","code":"","package":"ananke"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"get-started","dir":"Articles","previous_headings":"","what":"Get started","title":"Introduction to nexus","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e.\u00a0quantitative (nonnegative) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986; Greenacre 2021). assumes keep data tidy: variable must saved column observation (sample) must saved row. class simple use inherits base matrix: CompositionMatrix represents closed composition matrix: row matrix sum 1 (relative changes relevant compositional data analysis). original row sums kept internally, source data can restored:","code":"library(nexus) ## Mineral compositions of rock specimens data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Coerce to count data counts <- as_amounts(coda)  all.equal(hongite, as.data.frame(counts)) #> [1] TRUE ## Compositional bar plot plot(coda, order = \"A\")","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-reference-groups","dir":"Articles","previous_headings":"Get started","what":"Working with (reference) groups","title":"Introduction to nexus","text":"Provenance studies typically rely two approaches, can used together: Identification groups among artifacts studied, based mineralogical geochemical criteria (clustering). Comparison -called reference groups, .e.\u00a0known geological sources productive contexts (classification). nexus allows specify whether observation belongs specific group (). coercing data.frame CompositionMatrix object, attempt made automatically detect groups mapping column names. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used groups argument as_composition(). get_groups(x) set_groups(x) <- value allow retrieve set groups existing CompositionMatrix (NA can used specify sample belong group): groups defined, can used methods (e.g.\u00a0plotting).","code":"## Create a data.frame X <- data.frame(   groups = c(\"A\", \"A\", \"B\", \"A\", \"B\", \"C\", \"C\", \"C\", \"B\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) has_groups(Y) #> [1] TRUE ## Set groups (NA means no group) set_groups(Y) <- c(\"X\", \"X\", \"Y\", \"X\", \"Y\", NA, NA, NA, \"Y\")  ## Retrieve groups get_groups(Y) #> [1] \"X\" \"X\" \"Y\" \"X\" \"Y\" NA  NA  NA  \"Y\"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"working-with-repeated-measurements","dir":"Articles","previous_headings":"Get started","what":"Working with repeated measurements","title":"Introduction to nexus","text":"situations, measurements may repeated (e.g.\u00a0multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. coercing data.frame CompositionMatrix object, attempt made automatically detect samples mapping column names. matching column found, row names used default. behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying column used samples argument as_composition(). get_samples(x) set_samples(x) <- value allow retrieve set sample names existing CompositionMatrix (missing values allowed): Note presence repeated measurements may affect calculations (read documentation carefully).","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X) has_replicates(Y) #> [1] TRUE ## Set sample names set_samples(Y) <- c(\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\")  ## Retrieve groups get_samples(Y) #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"log-ratio-transformations","dir":"Articles","previous_headings":"Get started","what":"Log-ratio transformations","title":"Introduction to nexus","text":"amount every part limited whole. absolute amount whole noninformative since typically due artifacts measurement procedure. Thus relative changes relevant. relative amount one part increases, amounts parts must decrease, introducing spurious anticorrelation.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/articles/nexus.html","id":"ceramic-compositions","dir":"Articles","previous_headings":"Use cases","what":"Ceramic compositions","title":"Introduction to nexus","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\")  ## Remove rows with missing values kommos <- remove_NA(kommos, margin = 1)  ## Coerce to a compositional matrix coda <- as_composition(kommos) #> 2 qualitative variables were removed: type, date.  ## Set groups set_groups(coda) <- kommos$type ## Compositional bar plot plot(coda, order = \"Ca\") ## CLR clr <- transform_clr(coda) plot(clr, border = c(\"#EE7733\", \"#0077BB\", \"#33BBEE\", \"#EE3377\")) ## PCA clr_pca <- pca(clr, scale = FALSE)  viz_individuals(clr_pca, highlight = get_groups(coda), pch = 16,                  col = c(\"#EE7733\", \"#0077BB\", \"#33BBEE\", \"#EE3377\")) viz_variables(clr_pca)","package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"nexus-","dir":"","previous_headings":"","what":"Sourcing Archaeological Materials by Chemical Composition","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Exploration analysis compositional data framework Aitchison (1986). package provides tools chemical fingerprinting source tracking ancient materials. Initial development progress.","code":"To cite nexus in publications use:    Frerebeau N (2023). _nexus: Sourcing Archaeological Materials by   Chemical Composition_. Universit\u00e9 Bordeaux Montaigne, Pessac, France.   R package version 0.0.0.9000, <https://packages.tesselle.org/nexus/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{nexus: Sourcing Archaeological Materials by Chemical Composition}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 0.0.0.9000},     url = {https://packages.tesselle.org/nexus/},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Sourcing Archaeological Materials by Chemical Composition","text":"can install released version nexus CRAN : development version GitHub :","code":"install.packages(\"nexus\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/nexus\")","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Sourcing Archaeological Materials by Chemical Composition","text":"nexus provides set S4 classes represent different special types matrix. basic class represents compositional data matrix, .e.\u00a0quantitative (nonnegative) descriptions parts whole, carrying relative, rather absolute, information (Aitchison 1986). assumes keep data tidy: variable must saved column observation (sample) must saved row. new classes simple use inherit base matrix: CompositionMatrix class special slots (see vignette(\"nexus\")): samples repeated measurements/observation, groups group data site/area. coercing data.frame CompositionMatrix object, attempt made automatically assign values slots mapping column names. behavior can disabled setting options(nexus.autodetect = FALSE).","code":"## Load the package library(nexus) ## Mineral compositions of rock specimens data(\"hongite\") head(hongite) #>       A    B    C    D    E #> H1 48.8 31.7  3.8  6.4  9.3 #> H2 48.2 23.8  9.0  9.2  9.8 #> H3 37.0  9.1 34.2  9.5 10.2 #> H4 50.9 23.8  7.2 10.1  8.0 #> H5 44.2 38.3  2.9  7.7  6.9 #> H6 52.3 26.2  4.2 12.5  4.8  ## Coerce to compositional data coda <- as_composition(hongite) head(coda) #> <CompositionMatrix: 6 x 5> #>        A     B     C     D     E #> H1 0.488 0.317 0.038 0.064 0.093 #> H2 0.482 0.238 0.090 0.092 0.098 #> H3 0.370 0.091 0.342 0.095 0.102 #> H4 0.509 0.238 0.072 0.101 0.080 #> H5 0.442 0.383 0.029 0.077 0.069 #> H6 0.523 0.262 0.042 0.125 0.048 ## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   groups = c(\"X\", \"X\", \"X\", \"X\", \"X\", \"X\", \"Y\", \"Y\", \"Y\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Get samples get_samples(Y) #> [1] \"A\" \"A\" \"A\" \"B\" \"B\" \"B\" \"C\" \"C\" \"C\"  ## Get groups get_groups(Y) #> [1] \"X\" \"X\" \"X\" \"X\" \"X\" \"X\" \"Y\" \"Y\" \"Y\"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Sourcing Archaeological Materials by Chemical Composition","text":"Please note nexus project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Matrix \u2014 CompositionMatrix-class","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"S4 class represent compositional data.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"totals numeric vector store absolute row sums (closure compositions). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names ().","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"class inherits matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"code snippets , x CompositionMatrix object. .data.frame(x) Coerces data.frame.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/CompositionMatrix-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Matrix \u2014 CompositionMatrix-class","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## codaccess dim(coda) # Get the matrix dimensions #> [1] 25  5 row(coda) # Get the row indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,]    1    1    1    1    1 #>  [2,]    2    2    2    2    2 #>  [3,]    3    3    3    3    3 #>  [4,]    4    4    4    4    4 #>  [5,]    5    5    5    5    5 #>  [6,]    6    6    6    6    6 #>  [7,]    7    7    7    7    7 #>  [8,]    8    8    8    8    8 #>  [9,]    9    9    9    9    9 #> [10,]   10   10   10   10   10 #> [11,]   11   11   11   11   11 #> [12,]   12   12   12   12   12 #> [13,]   13   13   13   13   13 #> [14,]   14   14   14   14   14 #> [15,]   15   15   15   15   15 #> [16,]   16   16   16   16   16 #> [17,]   17   17   17   17   17 #> [18,]   18   18   18   18   18 #> [19,]   19   19   19   19   19 #> [20,]   20   20   20   20   20 #> [21,]   21   21   21   21   21 #> [22,]   22   22   22   22   22 #> [23,]   23   23   23   23   23 #> [24,]   24   24   24   24   24 #> [25,]   25   25   25   25   25 col(coda, as.factor = TRUE) # Get the column indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,] A    B    C    D    E    #>  [2,] A    B    C    D    E    #>  [3,] A    B    C    D    E    #>  [4,] A    B    C    D    E    #>  [5,] A    B    C    D    E    #>  [6,] A    B    C    D    E    #>  [7,] A    B    C    D    E    #>  [8,] A    B    C    D    E    #>  [9,] A    B    C    D    E    #> [10,] A    B    C    D    E    #> [11,] A    B    C    D    E    #> [12,] A    B    C    D    E    #> [13,] A    B    C    D    E    #> [14,] A    B    C    D    E    #> [15,] A    B    C    D    E    #> [16,] A    B    C    D    E    #> [17,] A    B    C    D    E    #> [18,] A    B    C    D    E    #> [19,] A    B    C    D    E    #> [20,] A    B    C    D    E    #> [21,] A    B    C    D    E    #> [22,] A    B    C    D    E    #> [23,] A    B    C    D    E    #> [24,] A    B    C    D    E    #> [25,] A    B    C    D    E    #> Levels: A B C D E nrow(coda) # Get the number of rows #> [1] 25 ncol(coda) # Get the number of columns #> [1] 5 dimnames(coda) # Get the dimension names #> [[1]] #>  [1] \"H1\"  \"H2\"  \"H3\"  \"H4\"  \"H5\"  \"H6\"  \"H7\"  \"H8\"  \"H9\"  \"H10\" \"H11\" \"H12\" #> [13] \"H13\" \"H14\" \"H15\" \"H16\" \"H17\" \"H18\" \"H19\" \"H20\" \"H21\" \"H22\" \"H23\" \"H24\" #> [25] \"H25\" #>  #> [[2]] #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" #>  rownames(coda) <- LETTERS[1:25] # Set the row names rownames(coda) # Get the rownames #>  [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\" \"J\" \"K\" \"L\" \"M\" \"N\" \"O\" \"P\" \"Q\" \"R\" \"S\" #> [20] \"T\" \"U\" \"V\" \"W\" \"X\" \"Y\" colnames(coda) <- letters[21:25] # Set the column names colnames(coda) # Get the column names #> [1] \"u\" \"v\" \"w\" \"x\" \"y\"  ## Subset coda[[1, 1]] # Get the first value #> [1] 0.488 coda[1] # Get the first value #> [1] 0.488 coda[, ] # Get all values #> <CompositionMatrix: 25 x 5> #>           u         v          w          x          y #> A 0.4880000 0.3170000 0.03800000 0.06400000 0.09300000 #> B 0.4820000 0.2380000 0.09000000 0.09200000 0.09800000 #> C 0.3700000 0.0910000 0.34200000 0.09500000 0.10200000 #> D 0.5090000 0.2380000 0.07200000 0.10100000 0.08000000 #> E 0.4420000 0.3830000 0.02900000 0.07700000 0.06900000 #> F 0.5230000 0.2620000 0.04200000 0.12500000 0.04800000 #> G 0.4460000 0.3300000 0.04600000 0.12200000 0.05600000 #> H 0.3460000 0.0520000 0.42900000 0.09600000 0.07700000 #> I 0.4120000 0.1170000 0.26700000 0.09600000 0.10800000 #> J 0.4260000 0.4660000 0.00700000 0.05600000 0.04500000 #> K 0.4990000 0.1950000 0.11400000 0.09500000 0.09700000 #> L 0.4520000 0.3730000 0.02700000 0.05500000 0.09300000 #> M 0.3270000 0.0850000 0.38900000 0.08000000 0.11900000 #> N 0.4140000 0.1290000 0.23400000 0.15800000 0.06500000 #> O 0.4620000 0.1750000 0.15800000 0.08300000 0.12200000 #> P 0.3230000 0.0730000 0.40900000 0.12900000 0.06600000 #> Q 0.4320000 0.4430000 0.01000000 0.07800000 0.03700000 #> R 0.4954955 0.3233233 0.03103103 0.08708709 0.06306306 #> S 0.4230000 0.1580000 0.20400000 0.08300000 0.13200000 #> T 0.4460000 0.1150000 0.23800000 0.11600000 0.08500000 #> U 0.4580000 0.1660000 0.16800000 0.12000000 0.08800000 #> V 0.4990000 0.2500000 0.06800000 0.10900000 0.07400000 #> W 0.4860000 0.3400000 0.02500000 0.09400000 0.05500000 #> X 0.4550000 0.1660000 0.17600000 0.09600000 0.10700000 #> Y 0.4590000 0.2490000 0.09700000 0.09800000 0.09700000 coda[1, , drop = FALSE] # Get the first row #> <CompositionMatrix: 1 x 5> #>       u     v     w     x     y #> A 0.488 0.317 0.038 0.064 0.093 coda[, 1:3] # Get the first three column #> <CompositionMatrix: 25 x 3> #>           u         v          w #> A 0.4880000 0.3170000 0.03800000 #> B 0.4820000 0.2380000 0.09000000 #> C 0.3700000 0.0910000 0.34200000 #> D 0.5090000 0.2380000 0.07200000 #> E 0.4420000 0.3830000 0.02900000 #> F 0.5230000 0.2620000 0.04200000 #> G 0.4460000 0.3300000 0.04600000 #> H 0.3460000 0.0520000 0.42900000 #> I 0.4120000 0.1170000 0.26700000 #> J 0.4260000 0.4660000 0.00700000 #> K 0.4990000 0.1950000 0.11400000 #> L 0.4520000 0.3730000 0.02700000 #> M 0.3270000 0.0850000 0.38900000 #> N 0.4140000 0.1290000 0.23400000 #> O 0.4620000 0.1750000 0.15800000 #> P 0.3230000 0.0730000 0.40900000 #> Q 0.4320000 0.4430000 0.01000000 #> R 0.4954955 0.3233233 0.03103103 #> S 0.4230000 0.1580000 0.20400000 #> T 0.4460000 0.1150000 0.23800000 #> U 0.4580000 0.1660000 0.16800000 #> V 0.4990000 0.2500000 0.06800000 #> W 0.4860000 0.3400000 0.02500000 #> X 0.4550000 0.1660000 0.17600000 #> Y 0.4590000 0.2490000 0.09700000","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Log-Ratio Matrix \u2014 LogRatio-class","title":"Log-Ratio Matrix \u2014 LogRatio-class","text":"S4 classes represent log-ratio data transformations.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Log-Ratio Matrix \u2014 LogRatio-class","text":"totals numeric vector store absolute row sums (closure compositions). samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). parts character vector store part names. ratio character vector store ratio names. order integer vector store original ordering columns. base numeric matrix store basis transformation. weights numeric vector store weights assigned respective log-ratios.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Log-Ratio Matrix \u2014 LogRatio-class","text":"classes inherit matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Log-Ratio Matrix \u2014 LogRatio-class","text":"code snippets , x LogRatio object. .data.frame(x) Coerces data.frame.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogRatio-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Log-Ratio Matrix \u2014 LogRatio-class","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical Matrix \u2014 LogicalMatrix-class","title":"Logical Matrix \u2014 LogicalMatrix-class","text":"S4 classes represent \\(m \\times p\\) logical matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Logical Matrix \u2014 LogicalMatrix-class","text":".Data \\(m \\times p\\) logical matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Logical Matrix \u2014 LogicalMatrix-class","text":"class inherits matrix.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/LogicalMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Logical Matrix \u2014 LogicalMatrix-class","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Numeric Matrix \u2014 NumericMatrix-class","title":"Numeric Matrix \u2014 NumericMatrix-class","text":"S4 classes represent \\(m \\times p\\) numeric matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Numeric Matrix \u2014 NumericMatrix-class","text":".Data \\(m \\times p\\) numeric matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Numeric Matrix \u2014 NumericMatrix-class","text":"class inherits matrix.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/NumericMatrix-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Numeric Matrix \u2014 NumericMatrix-class","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Outliers \u2014 OutlierIndex-class","title":"Outliers \u2014 OutlierIndex-class","text":"S4 class store result outlier detection.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Outliers \u2014 OutlierIndex-class","text":".Data logical vector. samples character vector store sample identifiers (allows duplicates case repeated measurements). groups character vector store group names (). distances numeric vector giving squared Mahalanobis distance. limit numeric value giving cut-value used outlier detection (quantile Chi-squared distribution). robust logical scalar: robust estimators used? dof (non-negative) numeric value giving degrees freedom.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Outliers \u2014 OutlierIndex-class","text":"classes inherit logical.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Outliers \u2014 OutlierIndex-class","text":"code snippets , x OutlierIndex object. .data.frame(x) Coerces data.frame.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/OutlierIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outliers \u2014 OutlierIndex-class","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Summary Statistics of Data Subsets \u2014 aggregate","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"Splits data subsets, computes summary statistics , returns result.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"","code":"# S4 method for CompositionMatrix aggregate(x, by, FUN, ...)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"x CompositionMatrix object. character string specifying grouping element. must one \"samples\" \"groups\". unambiguous substring can given. FUN function compute summary statistics. ... arguments passed FUN.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"matrix.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/aggregate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute Summary Statistics of Data Subsets \u2014 aggregate","text":"","code":"## Create a data.frame X <- data.frame(   samples = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"C\", \"C\", \"C\"),   groups = c(\"X\", \"X\", \"X\", \"X\", \"X\", \"X\", \"Y\", \"Y\", \"Y\"),   Ca = c(7.72, 7.32, 3.11, 7.19, 7.41, 5, 4.18, 1, 4.51),   Fe = c(6.12, 5.88, 5.12, 6.18, 6.02, 7.14, 5.25, 5.28, 5.72),   Na = c(0.97, 1.59, 1.25, 0.86, 0.76, 0.51, 0.75, 0.52, 0.56) )  ## Coerce to a compositional matrix Y <- as_composition(X)  ## Compositional mean by sample aggregate(Y, by = \"samples\", FUN = mean) #>          Ca        Fe         Na #> A 0.4468157 0.4539051 0.09927924 #> B 0.4746506 0.4742013 0.05114805 #> C 0.3067545 0.6238351 0.06941036  ## Compositional mean by group aggregate(Y, by = \"groups\", FUN = mean) #>          Ca        Fe         Na #> X 0.4625002 0.4659342 0.07156562 #> Y 0.3067545 0.6238351 0.06941036","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to Amounts \u2014 as_amounts","title":"Coerce to Amounts \u2014 as_amounts","text":"Coerce Amounts","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to Amounts \u2014 as_amounts","text":"","code":"as_amounts(from, ...)  # S4 method for CompositionMatrix as_amounts(from)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to Amounts \u2014 as_amounts","text":"CompositionMatrix object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to Amounts \u2014 as_amounts","text":"numeric  matrix.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to Amounts \u2014 as_amounts","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_amounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to Amounts \u2014 as_amounts","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies ## (use get_totals() to retrieve these values) ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to an S3 data.frame X <- data.frame(B) head(X) #>           V1         V2        V3        V4         V5 samples groups #> 1 0.19241983 0.24489796 0.1049563 0.2565598 0.20116618       1   <NA> #> 2 0.08620690 0.24482759 0.2137931 0.1413793 0.31379310       2   <NA> #> 3 0.04388715 0.20689655 0.2194357 0.2664577 0.26332288       3   <NA> #> 4 0.06666667 0.12222222 0.3333333 0.4500000 0.02777778       4   <NA> #> 5 0.16417910 0.25373134 0.3084577 0.1293532 0.14427861       5   <NA> #> 6 0.31970260 0.01858736 0.2156134 0.3197026 0.12639405       6   <NA>","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Closed Compositional Matrix \u2014 as_composition","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"Coerces object CompositionMatrix object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"","code":"as_composition(from, ...)  # S4 method for matrix as_composition(from)  # S4 method for data.frame as_composition(from, samples = NULL, groups = NULL)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"matrix data.frame coerced. ... Currently used. samples integer giving index column used sample identification: allows identify replicated measurements. NULL (default), row names used sample IDs. groups integer giving index column used group samples. NULL (default), grouping stored.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"CompositionMatrix object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"CompositionMatrix class special slots: samples repeated measurements/observation, groups group data site/area. coercing data.frame CompositionMatrix object, attempt made automatically assign values slots mapping column names (case insensitive, plural insensitive). behavior can disabled setting options(nexus.autodetect = FALSE) overridden explicitly specifying columns used. See vignette(\"nexus\").","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"non-numeric variable removed.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_composition.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to a Closed Compositional Matrix \u2014 as_composition","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies ## (use get_totals() to retrieve these values) ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to an S3 data.frame X <- data.frame(B) head(X) #>           V1        V2        V3         V4         V5 samples groups #> 1 0.06229508 0.1442623 0.1540984 0.31803279 0.32131148       1   <NA> #> 2 0.29194631 0.3187919 0.3355705 0.04026846 0.01342282       2   <NA> #> 3 0.19018405 0.2546012 0.1533742 0.17177914 0.23006135       3   <NA> #> 4 0.24226804 0.2448454 0.2345361 0.02577320 0.25257732       4   <NA> #> 5 0.05498282 0.3161512 0.2336770 0.19587629 0.19931271       5   <NA> #> 6 0.31785714 0.1321429 0.1535714 0.07142857 0.32500000       6   <NA>","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph of Log-ratios \u2014 as_graph","title":"Graph of Log-ratios \u2014 as_graph","text":"Produces graph log-ratios.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph of Log-ratios \u2014 as_graph","text":"","code":"as_graph(object, ...)  # S4 method for LR as_graph(object)  # S4 method for ALR as_graph(object)  # S4 method for ILR as_graph(object)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph of Log-ratios \u2014 as_graph","text":"object LogRatio object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph of Log-ratios \u2014 as_graph","text":"igraph graph object.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Graph of Log-ratios \u2014 as_graph","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/as_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph of Log-ratios \u2014 as_graph","text":"","code":"library(igraph) #>  #> Attaching package: \u2018igraph\u2019 #> The following objects are masked from \u2018package:stats\u2019: #>  #>     decompose, spectrum #> The following object is masked from \u2018package:base\u2019: #>  #>     union  ## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda) lr_graph <- as_graph(lr) plot(lr_graph)   ## Additive log-ratio alr <- transform_alr(coda) alr_graph <- as_graph(alr) plot(alr_graph)   ## Isometric log-ratio ilr <- transform_ilr(coda) ilr_graph <- as_graph(ilr) plot(ilr_graph)   plr <- transform_plr(coda) plr_graph <- as_graph(plr) plot(plr_graph)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":null,"dir":"Reference","previous_headings":"","what":"Closure Operation \u2014 closure","title":"Closure Operation \u2014 closure","text":"Closes compositions sum 1.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Closure Operation \u2014 closure","text":"","code":"closure(object, ...)  # S4 method for numeric closure(object, total = 1)  # S4 method for matrix closure(object, total = 1)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Closure Operation \u2014 closure","text":"object numeric vector matrix. ... Currently used. total numeric vector specifying total amount compositions closed (defaults 1).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Closure Operation \u2014 closure","text":"numeric vector matrix (object).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"missing-values-policy","dir":"Reference","previous_headings":"","what":"Missing Values Policy","title":"Closure Operation \u2014 closure","text":"Missing values omitted calculations.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Closure Operation \u2014 closure","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/closure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Closure Operation \u2014 closure","text":"","code":"## Create a count matrix A1 <- matrix(data = as.numeric(sample(1:100, 100, TRUE)), nrow = 20)  ## Coerce to compositions B <- as_composition(A1)  ## Row sums are internally stored before coercing to relative frequencies ## (use get_totals() to retrieve these values) ## This allows to restore the source data A2 <- as_amounts(B)  ## Coerce to an S3 data.frame X <- data.frame(B) head(X) #>           V1         V2         V3        V4         V5 samples groups #> 1 0.01526718 0.31679389 0.18320611 0.1374046 0.34732824       1   <NA> #> 2 0.03271028 0.10280374 0.12616822 0.2990654 0.43925234       2   <NA> #> 3 0.06985294 0.09558824 0.30147059 0.2095588 0.32352941       3   <NA> #> 4 0.13586957 0.23913043 0.19293478 0.2500000 0.18206522       4   <NA> #> 5 0.19220056 0.21727019 0.08356546 0.2395543 0.26740947       5   <NA> #> 6 0.26543210 0.01851852 0.21604938 0.4876543 0.01234568       6   <NA>","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":null,"dir":"Reference","previous_headings":"","what":"Variance and Covariance \u2014 covariance","title":"Variance and Covariance \u2014 covariance","text":"variance() computes log-ratio variance matrix. covariance() computes log-ratio covariance matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variance and Covariance \u2014 covariance","text":"","code":"covariance(x, ...)  variance(x, ...)  # S4 method for CompositionMatrix variance(x)  # S4 method for CompositionMatrix covariance(x, method = \"pearson\")","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variance and Covariance \u2014 covariance","text":"x CompositionMatrix object. ... Currently used. method character string indicating correlation coefficient (covariance) computed (see stats::cov()).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variance and Covariance \u2014 covariance","text":"matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variance and Covariance \u2014 covariance","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variance and Covariance \u2014 covariance","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variance and Covariance \u2014 covariance","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Variance matrix var(coda) #>               A            B            C             D             E #> A  2.977949e-03  0.003304739 -0.005983339 -6.607438e-05 -0.0002332741 #> B  3.304739e-03  0.013987684 -0.014219818 -1.382358e-03 -0.0016902464 #> C -5.983339e-03 -0.014219818  0.017768613  1.020312e-03  0.0014142324 #> D -6.607438e-05 -0.001382358  0.001020312  5.545169e-04 -0.0001263967 #> E -2.332741e-04 -0.001690246  0.001414232 -1.263967e-04  0.0006356848  ## Covariance matrix cov(coda) #>               A            B            C             D             E #> A  2.977949e-03  0.003304739 -0.005983339 -6.607438e-05 -0.0002332741 #> B  3.304739e-03  0.013987684 -0.014219818 -1.382358e-03 -0.0016902464 #> C -5.983339e-03 -0.014219818  0.017768613  1.020312e-03  0.0014142324 #> D -6.607438e-05 -0.001382358  0.001020312  5.545169e-04 -0.0001263967 #> E -2.332741e-04 -0.001690246  0.001414232 -1.263967e-04  0.0006356848  ## Variation matrix variation(coda) #>           A         B           C          D         E #> A 0.0000000 0.2592742  1.53285862 0.08281464 0.1385604 #> B 0.8011244 0.0000000  3.00068035 0.54727146 0.6490135 #> C 1.5999805 0.7988561  0.00000000 1.11145146 0.9476375 #> D 1.5483008 0.7471764 -0.05167973 0.00000000 0.1870579 #> E 1.7152103 0.9140859  0.11522977 0.16690950 0.0000000","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Distances \u2014 dist","title":"Distances \u2014 dist","text":"Computes log-ratio variance matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Distances \u2014 dist","text":"","code":"# S4 method for CompositionMatrix dist(x, method = \"euclidean\", diag = FALSE, upper = FALSE, p = 2)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Distances \u2014 dist","text":"x CompositionMatrix object. method character string specifying distance measure used. See stats::dist() available distances. diag logical scalar indicating whether diagonal distance matrix printed. upper logical scalar indicating whether upper triangle distance matrix printed. p integer giving power Minkowski distance.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Distances \u2014 dist","text":"stats::dist object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distances \u2014 dist","text":"Distances computed CLR-transformed data.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Distances \u2014 dist","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distances \u2014 dist","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Distances \u2014 dist","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Distance dist(coda) #>            H1        H2        H3        H4        H5        H6        H7 #> H2  0.8767510                                                             #> H3  2.5214253 1.6650912                                                   #> H4  0.7906400 0.2952543 1.8639740                                         #> H5  0.4739924 1.1608245 2.8213666 0.9854538                               #> H6  0.9678208 1.0017438 2.4503854 0.7106924 0.8126273                     #> H7  0.8391283 0.9264506 2.4466687 0.6726300 0.6518013 0.3068355           #> H8  3.0681017 2.2077617 0.5985190 2.3755249 3.3531743 2.9031508 2.9350491 #> H9  2.1567755 1.3025730 0.3646671 1.5092037 2.4608605 2.1188602 2.1067706 #> H10 1.5855253 2.3852360 4.0452164 2.1890800 1.2401119 1.7847939 1.7285362 #> H11 1.1710927 0.3101166 1.3704961 0.5142905 1.4599034 1.1921282 1.1626052 #> H12 0.3727262 1.2405255 2.8797113 1.1509884 0.4539174 1.1923398 1.0473042 #> H13 2.6796901 1.8462061 0.2974720 2.0700018 3.0032107 2.6883648 2.6669260 #> H14 2.1787003 1.3234987 0.8580849 1.4065875 2.3581311 1.8190796 1.8478752 #> H15 1.4784947 0.6708213 1.0579099 0.9297665 1.8185451 1.6194568 1.5714340 #> H16 2.9044778 2.0288430 0.6027605 2.1709606 3.1392211 2.6453694 2.6681226 #> H17 1.4546884 2.1462022 3.7770078 1.9169098 1.0304796 1.4107166 1.3729948 #> H18 0.5245452 1.0606339 2.7007677 0.8376898 0.2629770 0.5769217 0.4884938 #> H19 1.7602135 0.9534581 0.8105519 1.2096096 2.0997516 1.8869609 1.8364379 #> H20 2.1115841 1.2415737 0.5413915 1.3933788 2.3711124 1.9319745 1.9510350 #> H21 1.6327549 0.7593779 0.9906382 0.9018811 1.8714132 1.4647356 1.4581381 #> H22 0.8008035 0.4134000 1.9478272 0.1333961 0.9287606 0.5909027 0.5497058 #> H23 0.7451072 1.2918095 2.9137094 1.0529779 0.3583952 0.6293206 0.5735426 #> H24 1.6082346 0.7503990 0.9163484 0.9700216 1.9090190 1.6170556 1.5859866 #> H25 0.9383107 0.1051583 1.6279995 0.3274461 1.1953636 1.0126284 0.9224570 #>            H8        H9       H10       H11       H12       H13       H14 #> H2                                                                        #> H3                                                                        #> H4                                                                        #> H5                                                                        #> H6                                                                        #> H7                                                                        #> H8                                                                        #> H9  0.9397076                                                             #> H10 4.5619484 3.6846865                                                   #> H11 1.9033794 1.0096405 2.6762596                                         #> H12 3.4332992 2.5152272 1.2980229 1.5387862                               #> H13 0.6382499 0.5712106 4.2301042 1.5639306 3.0279063                     #> H14 1.1707267 0.7298713 3.5473128 1.0636854 2.5411784 1.1433041           #> H15 1.6287975 0.6973619 3.0370608 0.4288520 1.8301814 1.2017611 1.0344674 #> H16 0.4545771 0.8638763 4.3488475 1.7374933 3.2690987 0.7689998 0.8360769 #> H17 4.2728994 3.4243251 0.4863777 2.4251205 1.2552674 3.9824638 3.2094091 #> H18 3.2105210 2.3441270 1.3561461 1.3391357 0.6366131 2.8999267 2.1988548 #> H19 1.3980195 0.4714590 3.3222469 0.7032641 2.1054467 0.9228103 0.9859377 #> H20 0.9877633 0.3329260 3.5815737 0.9408789 2.4792771 0.8218637 0.4308744 #> H21 1.4914563 0.6632621 3.0879216 0.4819969 1.9980537 1.2277365 0.5920798 #> H22 2.4502129 1.5980522 2.1227178 0.6233978 1.1448814 2.1615401 1.4406247 #> H23 3.4120401 2.5610218 1.1797726 1.5632549 0.7625165 3.1207729 2.3708636 #> H24 1.4738079 0.5530808 3.1343759 0.4647774 1.9682225 1.1021347 0.8059578 #> H25 2.1729555 1.2681367 2.4277793 0.3059376 1.2955966 1.8116276 1.2646069 #>           H15       H16       H17       H18       H19       H20       H21 #> H2                                                                        #> H3                                                                        #> H4                                                                        #> H5                                                                        #> H6                                                                        #> H7                                                                        #> H8                                                                        #> H9                                                                        #> H10                                                                       #> H11                                                                       #> H12                                                                       #> H13                                                                       #> H14                                                                       #> H15                                                                       #> H16 1.5257009                                                             #> H17 2.8148034 4.0297987                                                   #> H18 1.7276112 2.9936000 1.0890523                                         #> H19 0.2925366 1.3251713 3.0989710 2.0135135                               #> H20 0.7661338 0.8061360 3.2888972 2.2273992 0.6511045                     #> H21 0.4989043 1.2756316 2.7966378 1.7339926 0.5870924 0.5064689           #> H22 1.0426202 2.2267808 1.8298329 0.7673337 1.3160904 1.4643450 0.9696446 #> H23 1.9607003 3.1805225 0.8658073 0.2491895 2.2446978 2.4281713 1.9360069 #> H24 0.2290380 1.3313017 2.8816469 1.7984553 0.3052918 0.5584031 0.3003119 #> H25 0.6589617 1.9776437 2.1771982 1.0988769 0.9297336 1.2023185 0.7102152 #>           H22       H23       H24 #> H2                                #> H3                                #> H4                                #> H5                                #> H6                                #> H7                                #> H8                                #> H9                                #> H10                               #> H11                               #> H12                               #> H13                               #> H14                               #> H15                               #> H16                               #> H17                               #> H18                               #> H19                               #> H20                               #> H21                               #> H22                               #> H23 0.9671322                     #> H24 1.0663845 2.0204099           #> H25 0.4247642 1.3238750 0.7183694  ## Mahalanobis distance mahalanobis(coda) #>  #>         H1         H2         H3         H4         H5         H6         H7  #>  4.6145006  0.7777782  1.5371605  1.9871544  5.1045081  5.7616379  6.2864611  #>         H8         H9        H10        H11        H12        H13        H14  #>  8.4640271  1.0131441 38.9984476  1.4645650  4.6164165  5.8470116  4.9213668  #>        H15        H16        H17        H18        H19        H20        H21  #>  2.3263308  6.9073081  5.3144948  2.3719491  3.4688162  2.6904442  1.8863565  #>        H22        H23        H24        H25  #>  4.1617381 22.7833067  4.3266981  2.1501323","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Working With Groups \u2014 groups","title":"Working With Groups \u2014 groups","text":"Retrieves defines groups observations belong.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Working With Groups \u2014 groups","text":"","code":"has_groups(x)  get_groups(x)  set_groups(x) <- value  # S4 method for CompositionMatrix has_groups(x)  # S4 method for LogRatio has_groups(x)  # S4 method for OutlierIndex has_groups(x)  # S4 method for CompositionMatrix get_groups(x)  # S4 method for LogRatio get_groups(x)  # S4 method for OutlierIndex get_groups(x)  # S4 method for CompositionMatrix set_groups(x) <- value","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Working With Groups \u2014 groups","text":"x object get set groups. value possible value groups x.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Working With Groups \u2014 groups","text":"set_groups() returns object sort x new group names assigned. get_groups() returns group names x. has_groups() returns logical scalar.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Working With Groups \u2014 groups","text":"See vignette(\"nexus\").","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/groups.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Working With Groups \u2014 groups","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":null,"dir":"Reference","previous_headings":"","what":"Hongite Mineralogy \u2014 hongite","title":"Hongite Mineralogy \u2014 hongite","text":"Mineral compositions 25 rock specimens hongite type.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hongite Mineralogy \u2014 hongite","text":"","code":"hongite","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Hongite Mineralogy \u2014 hongite","text":"data.frame 5 variables (minerals): Albite (percent). B Blandite (percent). C Cornite (percent). D Daubite (percent). E Endite (percent).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/hongite.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hongite Mineralogy \u2014 hongite","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":null,"dir":"Reference","previous_headings":"","what":"Mahalanobis Distance \u2014 mahalanobis","title":"Mahalanobis Distance \u2014 mahalanobis","text":"Computes squared Mahalanobis distance rows x.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mahalanobis Distance \u2014 mahalanobis","text":"","code":"# S4 method for CompositionMatrix mahalanobis(x, center, cov, ..., robust = TRUE, method = c(\"mve\", \"mcd\"))  # S4 method for ILR mahalanobis(x, center, cov, ..., robust = TRUE, method = c(\"mve\", \"mcd\"))","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mahalanobis Distance \u2014 mahalanobis","text":"x CompositionMatrix ILR object. center numeric vector giving mean vector distribution. missing, estimated x. cov numeric matrix giving covariance distribution. missing, estimated x. ... Extra parameters passed MASS::cov.rob(). used robust TRUE. robust logical scalar: robust location scatter estimation used? method character string specifying method used. must one \"mve\" (minimum volume ellipsoid) \"mcd\" (minimum covariance determinant). used robust TRUE.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mahalanobis Distance \u2014 mahalanobis","text":"numeric vector.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mahalanobis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mahalanobis Distance \u2014 mahalanobis","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Mean \u2014 mean","title":"Compositional Mean \u2014 mean","text":"Compositional Mean","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compositional Mean \u2014 mean","text":"","code":"# S4 method for CompositionMatrix mean(x, ...)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compositional Mean \u2014 mean","text":"x CompositionMatrix object. ... argument passed methods.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compositional Mean \u2014 mean","text":"numeric vector.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compositional Mean \u2014 mean","text":"Closed vector columns geometric means.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compositional Mean \u2014 mean","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 .","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Mean \u2014 mean","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Mean \u2014 mean","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Mean mean(coda) #>          A          B          C          D          E  #> 0.48941415 0.21966082 0.09881294 0.10405382 0.08805827","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":null,"dir":"Reference","previous_headings":"","what":"nexus: Sourcing Archaeological Materials by Chemical Composition \u2014 nexus-package","title":"nexus: Sourcing Archaeological Materials by Chemical Composition \u2014 nexus-package","text":"Exploration analysis compositional data framework Aitchison (1986) <isbn: 9780412280603>. package provides tools chemical fingerprinting source tracking ancient materials.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"nexus: Sourcing Archaeological Materials by Chemical Composition \u2014 nexus-package","text":"nexus uses following options() configure behavior: nexus.autodetect: logical scalar. Try automatically assign values corresponding slot CompositionMatrix object coercing data.frame? Defaults TRUE. nexus.verbose: logical scalar. R report extra information progress? Defaults TRUE.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/nexus-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"nexus: Sourcing Archaeological Materials by Chemical Composition \u2014 nexus-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Detection \u2014 outliers","title":"Outlier Detection \u2014 outliers","text":"Outlier Detection","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Detection \u2014 outliers","text":"","code":"outliers(object, ...)  # S4 method for CompositionMatrix outliers(   object,   center = NULL,   cov = NULL,   robust = TRUE,   method = c(\"mve\", \"mcd\"),   quantile = 0.975,   ... )","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Detection \u2014 outliers","text":"object CompositionMatrix. ... Extra parameters passed MASS::cov.rob(). used robust TRUE. center numeric vector giving mean vector distribution. missing, estimated x. cov numeric matrix giving covariance distribution. missing, estimated x. robust logical scalar: robust location scatter estimation used? method character string specifying method used. must one \"mve\" (minimum volume ellipsoid) \"mcd\" (minimum covariance determinant). used robust TRUE. quantile length-one numeric vector giving significance level. quantile used cut-value outlier detection: observations larger (squared) Mahalanobis distance considered potential outliers.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Detection \u2014 outliers","text":"OutlierIndex object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Outlier Detection \u2014 outliers","text":"outlier can defined large Mahalanobis distance observations. way, certain proportion observations can identified, e.g. top 2% values (.e. values 0.98th percentile Chi-2 distribution). one hand, Mahalanobis distance likely strongly affected presence outliers. Rousseeuw van Zomeren (1990) thus recommend using robust methods (excessively affected presence outliers). hand, choice threshold classifying observation outlier discussed. apparent reason particular threshold applicable data sets (Filzmoser, Garrett, Reimann 2005).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Outlier Detection \u2014 outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 . Rousseeuw, P. J. & van Zomeren, B. C. (1990). Unmasking Multivariate Outliers Leverage Points. Journal American Statistical Association, 85(411): 633-639. doi:10.1080/01621459.1990.10474920 . Santos, F. (2020). Modern methods old data: overview robust methods outliers detection applications osteology. Journal Archaeological Science: Reports, 32, 102423. doi:10.1016/j.jasrep.2020.102423 .","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Outlier Detection \u2014 outliers","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outlier Detection \u2014 outliers","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Detect outliers out <- outliers(coda) #>   ## Quantile-Quantile plot plot(out, qq = TRUE)   ## Plot plot(out)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Components Analysis \u2014 pca_coda","title":"Principal Components Analysis \u2014 pca_coda","text":"Computes principal components analysis based singular value decomposition.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Components Analysis \u2014 pca_coda","text":"","code":"# S4 method for CompositionMatrix pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )  # S4 method for LogRatio pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Components Analysis \u2014 pca_coda","text":"object LogRatio object. center logical scalar: variables shifted zero centered? scale logical scalar: variables scaled unit variance? rank integer value specifying maximal number components kept results. NULL (default), \\(p - 1\\) components returned. sup_row numeric logical vector specifying indices supplementary rows (individuals). sup_col numeric logical vector specifying indices supplementary columns (variables). weight_row numeric vector specifying active row (individual) weights. NULL (default), uniform weights used. Row weights internally normalized sum 1 weight_col numeric vector specifying active column (variable) weights. NULL (default), uniform weights (1) used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Components Analysis \u2014 pca_coda","text":"dimensio::PCA object.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Components Analysis \u2014 pca_coda","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/pca_coda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Components Analysis \u2014 pca_coda","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Centered log-ratio clr <- transform_clr(coda)  ## PCA X <- pca(clr)  ## Plot viz_individuals(X)  viz_variables(X)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Compositional Data \u2014 plot_coda","title":"Plot Compositional Data \u2014 plot_coda","text":"Displays compositional bar chart.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Compositional Data \u2014 plot_coda","text":"","code":"# S4 method for CompositionMatrix,missing plot(   x,   order = NULL,   decreasing = FALSE,   groups = get_groups(x),   horiz = TRUE,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   ... )","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Compositional Data \u2014 plot_coda","text":"x CompositionMatrix object. order integer vector giving index column used ordering data. decreasing logical scalar: sort order increasing decreasing? groups factor sense .factor(groups) defines grouping. set, matrix panels defined groups drawn. horiz logical scalar. FALSE, bars drawn vertically first bar left. TRUE (default), bars drawn horizontally first bottom. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? ... parameters passed graphics::barplot().","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Compositional Data \u2014 plot_coda","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Compositional Data \u2014 plot_coda","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_coda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Compositional Data \u2014 plot_coda","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Bar plot plot(coda, order = 2)   ## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  plot(coda, order = 1, main = \"Kommos Ceramics\")  plot(coda, order = 1, horiz = FALSE, main = \"Kommos Ceramics\")","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Log-Ratios \u2014 plot_logratio","title":"Plot Log-Ratios \u2014 plot_logratio","text":"Displays density plot.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Log-Ratios \u2014 plot_logratio","text":"","code":"# S4 method for LogRatio,missing plot(   x,   order = NULL,   decreasing = FALSE,   groups = get_groups(x),   rug = TRUE,   ticksize = 0.05,   ncol = NULL,   flip = FALSE,   xlab = NULL,   ylab = NULL,   main = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   legend = list(x = \"topright\"),   ... )","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Log-Ratios \u2014 plot_logratio","text":"x LogRatio object. order logical scalar: ratio ordered? decreasing logical scalar: sort order increasing decreasing? groups factor sense .factor(groups) defines grouping. set, matrix panels defined groups drawn. rug logical scalar: rug representation (1-d plot) data added plot? ticksize length-one numeric vector giving length ticks making rug. Positive lengths give inwards ticks. used rug TRUE. ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? xlab, ylab character vector giving x y axis labels. main character string giving main title plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed. ... graphical parameters, particularly, border col.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Log-Ratios \u2014 plot_logratio","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Log-Ratios \u2014 plot_logratio","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_logratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Log-Ratios \u2014 plot_logratio","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Log ratio clr <- transform_clr(coda) plot(clr, group = NULL, flip = TRUE, border = \"black\", col = NA)  plot(clr, flip = TRUE)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Outliers \u2014 plot_outliers","title":"Plot Outliers \u2014 plot_outliers","text":"Plot Outliers","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Outliers \u2014 plot_outliers","text":"","code":"# S4 method for OutlierIndex,missing plot(   x,   qq = FALSE,   limit = TRUE,   probs = c(0.25, 0.75),   col = c(\"#004488\", \"#DDAA33\"),   pch = c(15, 16),   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Outliers \u2014 plot_outliers","text":"x OutlierIndex object. qq logical scalar: quantile-quantile plot produced? limit logical scalar: cut-value outlier detection displayed? used qq FALSE. probs length-two numeric vector representing probabilities. Corresponding quantile pairs define line drawn (see stats::qqline()). used qq TRUE. col vector colors. pch vector plotting character (symbol). xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Outliers \u2014 plot_outliers","text":"plot() called side-effects: results graphic displayed (invisibly return x).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot Outliers \u2014 plot_outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 .","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Outliers \u2014 plot_outliers","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Outliers \u2014 plot_outliers","text":"","code":"## Data from Day et al. 2011 data(\"kommos\", package = \"folio\") # Coerce to compositional data kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping #> 1 qualitative variable was removed: date.  ## Detect outliers out <- outliers(coda) #>   ## Quantile-Quantile plot plot(out, qq = TRUE)   ## Plot plot(out)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages \u2014 reexports","title":"Objects exported from other packages \u2014 reexports","text":"objects imported packages. Follow links see documentation. arkhe remove_NA, remove_zero, replace_NA, replace_zero dimensio viz_individuals, viz_variables isopleuros ternary_plot","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Working With Samples \u2014 samples","title":"Working With Samples \u2014 samples","text":"Retrieves defines sample names.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Working With Samples \u2014 samples","text":"","code":"has_replicates(x)  get_samples(x)  set_samples(x) <- value  # S4 method for CompositionMatrix has_replicates(x)  # S4 method for LogRatio has_replicates(x)  # S4 method for OutlierIndex has_replicates(x)  # S4 method for CompositionMatrix get_samples(x)  # S4 method for LogRatio get_samples(x)  # S4 method for OutlierIndex get_samples(x)  # S4 method for CompositionMatrix set_samples(x) <- value","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Working With Samples \u2014 samples","text":"x object get set samples. value possible value samples x.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Working With Samples \u2014 samples","text":"set_samples() returns object sort x new sample names assigned. get_samples() returns sample names x. has_replicates() returns logical scalar.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Working With Samples \u2014 samples","text":"situations, measurements may repeated (e.g. multiple chemical analyses sample). presence repeated measurements can specified giving several observations sample name. See vignette(\"nexus\").","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/samples.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Working With Samples \u2014 samples","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object \u2014 subset","title":"Extract or Replace Parts of an Object \u2014 subset","text":"Operators acting objects extract replace parts.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object \u2014 subset","text":"","code":"# S4 method for CompositionMatrix [(x, i, j, ..., drop = TRUE)  # S4 method for CompositionMatrix [(x, i, j, ...) <- value  # S4 method for CompositionMatrix [[(x, i, j, ...) <- value","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object \u2014 subset","text":"x object extract element(s) replace element(s). , j Indices specifying elements extract replace. Indices numeric, integer character vectors empty (missing) NULL. Numeric values coerced integer .integer() (hence truncated towards zero). Character vectors matched name elements. empty index (comma separated blank) indicates entries dimension selected. ... Currently used. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement. value possible value element(s) x.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object \u2014 subset","text":"subsetted object sort x.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object \u2014 subset","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or Replace Parts of an Object \u2014 subset","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## codaccess dim(coda) # Get the matrix dimensions #> [1] 25  5 row(coda) # Get the row indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,]    1    1    1    1    1 #>  [2,]    2    2    2    2    2 #>  [3,]    3    3    3    3    3 #>  [4,]    4    4    4    4    4 #>  [5,]    5    5    5    5    5 #>  [6,]    6    6    6    6    6 #>  [7,]    7    7    7    7    7 #>  [8,]    8    8    8    8    8 #>  [9,]    9    9    9    9    9 #> [10,]   10   10   10   10   10 #> [11,]   11   11   11   11   11 #> [12,]   12   12   12   12   12 #> [13,]   13   13   13   13   13 #> [14,]   14   14   14   14   14 #> [15,]   15   15   15   15   15 #> [16,]   16   16   16   16   16 #> [17,]   17   17   17   17   17 #> [18,]   18   18   18   18   18 #> [19,]   19   19   19   19   19 #> [20,]   20   20   20   20   20 #> [21,]   21   21   21   21   21 #> [22,]   22   22   22   22   22 #> [23,]   23   23   23   23   23 #> [24,]   24   24   24   24   24 #> [25,]   25   25   25   25   25 col(coda, as.factor = TRUE) # Get the column indexes #>       [,1] [,2] [,3] [,4] [,5] #>  [1,] A    B    C    D    E    #>  [2,] A    B    C    D    E    #>  [3,] A    B    C    D    E    #>  [4,] A    B    C    D    E    #>  [5,] A    B    C    D    E    #>  [6,] A    B    C    D    E    #>  [7,] A    B    C    D    E    #>  [8,] A    B    C    D    E    #>  [9,] A    B    C    D    E    #> [10,] A    B    C    D    E    #> [11,] A    B    C    D    E    #> [12,] A    B    C    D    E    #> [13,] A    B    C    D    E    #> [14,] A    B    C    D    E    #> [15,] A    B    C    D    E    #> [16,] A    B    C    D    E    #> [17,] A    B    C    D    E    #> [18,] A    B    C    D    E    #> [19,] A    B    C    D    E    #> [20,] A    B    C    D    E    #> [21,] A    B    C    D    E    #> [22,] A    B    C    D    E    #> [23,] A    B    C    D    E    #> [24,] A    B    C    D    E    #> [25,] A    B    C    D    E    #> Levels: A B C D E nrow(coda) # Get the number of rows #> [1] 25 ncol(coda) # Get the number of columns #> [1] 5 dimnames(coda) # Get the dimension names #> [[1]] #>  [1] \"H1\"  \"H2\"  \"H3\"  \"H4\"  \"H5\"  \"H6\"  \"H7\"  \"H8\"  \"H9\"  \"H10\" \"H11\" \"H12\" #> [13] \"H13\" \"H14\" \"H15\" \"H16\" \"H17\" \"H18\" \"H19\" \"H20\" \"H21\" \"H22\" \"H23\" \"H24\" #> [25] \"H25\" #>  #> [[2]] #> [1] \"A\" \"B\" \"C\" \"D\" \"E\" #>  rownames(coda) <- LETTERS[1:25] # Set the row names rownames(coda) # Get the rownames #>  [1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\" \"G\" \"H\" \"I\" \"J\" \"K\" \"L\" \"M\" \"N\" \"O\" \"P\" \"Q\" \"R\" \"S\" #> [20] \"T\" \"U\" \"V\" \"W\" \"X\" \"Y\" colnames(coda) <- letters[21:25] # Set the column names colnames(coda) # Get the column names #> [1] \"u\" \"v\" \"w\" \"x\" \"y\"  ## Subset coda[[1, 1]] # Get the first value #> [1] 0.488 coda[1] # Get the first value #> [1] 0.488 coda[, ] # Get all values #> <CompositionMatrix: 25 x 5> #>           u         v          w          x          y #> A 0.4880000 0.3170000 0.03800000 0.06400000 0.09300000 #> B 0.4820000 0.2380000 0.09000000 0.09200000 0.09800000 #> C 0.3700000 0.0910000 0.34200000 0.09500000 0.10200000 #> D 0.5090000 0.2380000 0.07200000 0.10100000 0.08000000 #> E 0.4420000 0.3830000 0.02900000 0.07700000 0.06900000 #> F 0.5230000 0.2620000 0.04200000 0.12500000 0.04800000 #> G 0.4460000 0.3300000 0.04600000 0.12200000 0.05600000 #> H 0.3460000 0.0520000 0.42900000 0.09600000 0.07700000 #> I 0.4120000 0.1170000 0.26700000 0.09600000 0.10800000 #> J 0.4260000 0.4660000 0.00700000 0.05600000 0.04500000 #> K 0.4990000 0.1950000 0.11400000 0.09500000 0.09700000 #> L 0.4520000 0.3730000 0.02700000 0.05500000 0.09300000 #> M 0.3270000 0.0850000 0.38900000 0.08000000 0.11900000 #> N 0.4140000 0.1290000 0.23400000 0.15800000 0.06500000 #> O 0.4620000 0.1750000 0.15800000 0.08300000 0.12200000 #> P 0.3230000 0.0730000 0.40900000 0.12900000 0.06600000 #> Q 0.4320000 0.4430000 0.01000000 0.07800000 0.03700000 #> R 0.4954955 0.3233233 0.03103103 0.08708709 0.06306306 #> S 0.4230000 0.1580000 0.20400000 0.08300000 0.13200000 #> T 0.4460000 0.1150000 0.23800000 0.11600000 0.08500000 #> U 0.4580000 0.1660000 0.16800000 0.12000000 0.08800000 #> V 0.4990000 0.2500000 0.06800000 0.10900000 0.07400000 #> W 0.4860000 0.3400000 0.02500000 0.09400000 0.05500000 #> X 0.4550000 0.1660000 0.17600000 0.09600000 0.10700000 #> Y 0.4590000 0.2490000 0.09700000 0.09800000 0.09700000 coda[1, , drop = FALSE] # Get the first row #> <CompositionMatrix: 1 x 5> #>       u     v     w     x     y #> A 0.488 0.317 0.038 0.064 0.093 coda[, 1:3] # Get the first three column #> <CompositionMatrix: 25 x 3> #>           u         v          w #> A 0.4880000 0.3170000 0.03800000 #> B 0.4820000 0.2380000 0.09000000 #> C 0.3700000 0.0910000 0.34200000 #> D 0.5090000 0.2380000 0.07200000 #> E 0.4420000 0.3830000 0.02900000 #> F 0.5230000 0.2620000 0.04200000 #> G 0.4460000 0.3300000 0.04600000 #> H 0.3460000 0.0520000 0.42900000 #> I 0.4120000 0.1170000 0.26700000 #> J 0.4260000 0.4660000 0.00700000 #> K 0.4990000 0.1950000 0.11400000 #> L 0.4520000 0.3730000 0.02700000 #> M 0.3270000 0.0850000 0.38900000 #> N 0.4140000 0.1290000 0.23400000 #> O 0.4620000 0.1750000 0.15800000 #> P 0.3230000 0.0730000 0.40900000 #> Q 0.4320000 0.4430000 0.01000000 #> R 0.4954955 0.3233233 0.03103103 #> S 0.4230000 0.1580000 0.20400000 #> T 0.4460000 0.1150000 0.23800000 #> U 0.4580000 0.1660000 0.16800000 #> V 0.4990000 0.2500000 0.06800000 #> W 0.4860000 0.3400000 0.02500000 #> X 0.4550000 0.1660000 0.17600000 #> Y 0.4590000 0.2490000 0.09700000","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":null,"dir":"Reference","previous_headings":"","what":"Row Sums \u2014 totals","title":"Row Sums \u2014 totals","text":"Retrieves defines row sums (closure).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row Sums \u2014 totals","text":"","code":"get_totals(x)  set_totals(x) <- value  # S4 method for CompositionMatrix get_totals(x)  # S4 method for CompositionMatrix set_totals(x) <- value","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row Sums \u2014 totals","text":"x object get set totals. value possible value totals x.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row Sums \u2014 totals","text":"set_totals() returns object sort x new row sums assigned. get_totals() returns row sums x.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/totals.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Row Sums \u2014 totals","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":null,"dir":"Reference","previous_headings":"","what":"Additive Log-Ratios (ALR) \u2014 transform_alr","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"Computes ALR transformation.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"","code":"transform_alr(object, ...)  # S4 method for CompositionMatrix transform_alr(object, j = ncol(object))","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"object CompositionMatrix object. ... Currently used. j integer giving index rationing part (denominator).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"ALR object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_alr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Additive Log-Ratios (ALR) \u2014 transform_alr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered Log-Ratios (CLR) \u2014 transform_clr","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"Computes CLR transformation.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"","code":"transform_clr(object, ...)  # S4 method for CompositionMatrix transform_clr(object, weights = FALSE)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"object CompositionMatrix object. ... Currently used. weights logical scalar: sould varying weight used. FALSE (default), equally-weighted parts used. Alternatively, positive numeric vector weights can specified.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"CLR object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_clr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Centered Log-Ratios (CLR) \u2014 transform_clr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":null,"dir":"Reference","previous_headings":"","what":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"Computes ILR transformations.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"","code":"transform_ilr(object, ...)  # S4 method for CompositionMatrix transform_ilr(object)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"object CompositionMatrix object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"ILR object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"ILR transformation provides coordinates composition respect given orthonormal basis. transform_ilr() uses orthonormal basis (Helmert matrix) originally defined Egozcue et al. (2003).","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barcel\u00f3-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Inverse Log-Ratio Transformation \u2014 transform_inverse","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"Computes inverse log-ratio transformations.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"","code":"transform_inverse(object, ...)  # S4 method for CLR transform_inverse(object)  # S4 method for ALR transform_inverse(object)  # S4 method for ILR transform_inverse(object)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"object CompositionMatrix object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"CompositionMatrix object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barcel\u00f3-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Fi\u0161erov\u00e1, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455\u2011468. doi:10.1007/s11004-011-9333-x . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise Log-Ratios (LR) \u2014 transform_lr","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"Computes pairwise log-ratio transformation.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"","code":"transform_lr(object, ...)  # S4 method for CompositionMatrix transform_lr(object)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"object CompositionMatrix object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"LR object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot Log-Ratios (PLR) \u2014 transform_plr","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"Computes PLR transformations.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"","code":"transform_plr(object, ...)  # S4 method for CompositionMatrix transform_plr(object, pivot = 1)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"object CompositionMatrix object. ... Currently used. pivot integer giving index pivotal variable.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"PLR object.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"Fi\u0161erov\u00e1, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455\u2011468. doi:10.1007/s11004-011-9333-x . Hron, K., Filzmoser, P., de Caritat, P., Fi\u0161erov\u00e1, E. & Gardlo, . (2017). Weighted Pivot Coordinates Compositional Data Application Geochemical Mapping. Mathematical Geosciences, 49(6), 797-814. doi:10.1007/s11004-017-9684-z . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Pairwise log-ratio lr <- transform_lr(coda)  ## Centered log-ratio clr <- transform_clr(coda)  ## Additive log-ratio alr <- transform_alr(coda)  ## Isometric log-ratio ilr <- transform_ilr(coda) plr <- transform_plr(coda)  ## Inverse transformation inv_clr <- transform_inverse(clr) all.equal(coda, inv_clr) #> [1] TRUE  inv_alr <- transform_inverse(alr) all.equal(coda, inv_alr) #> [1] TRUE  inv_ilr <- transform_inverse(ilr) all.equal(coda, inv_ilr) #> [1] TRUE  inv_plr <- transform_inverse(plr) all.equal(coda, inv_plr) #> [1] TRUE","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":null,"dir":"Reference","previous_headings":"","what":"Variation Matrix \u2014 variation","title":"Variation Matrix \u2014 variation","text":"Computes compositional variation array.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variation Matrix \u2014 variation","text":"","code":"variation(object, ...)  # S4 method for CompositionMatrix variation(object)","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variation Matrix \u2014 variation","text":"object CompositionMatrix object. ... Currently used.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variation Matrix \u2014 variation","text":"matrix.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Variation Matrix \u2014 variation","text":"compositional variation array square matrix upper triangular part displays log-ratio variances lower triangular part displays log-ratio means.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variation Matrix \u2014 variation","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 .","code":"","package":"nexus"},{"path":[],"package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Variation Matrix \u2014 variation","text":"N. Frerebeau","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/variation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Variation Matrix \u2014 variation","text":"","code":"## Coerce to compositional data data(\"hongite\") coda <- as_composition(hongite)  ## Variance matrix var(coda) #>               A            B            C             D             E #> A  2.977949e-03  0.003304739 -0.005983339 -6.607438e-05 -0.0002332741 #> B  3.304739e-03  0.013987684 -0.014219818 -1.382358e-03 -0.0016902464 #> C -5.983339e-03 -0.014219818  0.017768613  1.020312e-03  0.0014142324 #> D -6.607438e-05 -0.001382358  0.001020312  5.545169e-04 -0.0001263967 #> E -2.332741e-04 -0.001690246  0.001414232 -1.263967e-04  0.0006356848  ## Covariance matrix cov(coda) #>               A            B            C             D             E #> A  2.977949e-03  0.003304739 -0.005983339 -6.607438e-05 -0.0002332741 #> B  3.304739e-03  0.013987684 -0.014219818 -1.382358e-03 -0.0016902464 #> C -5.983339e-03 -0.014219818  0.017768613  1.020312e-03  0.0014142324 #> D -6.607438e-05 -0.001382358  0.001020312  5.545169e-04 -0.0001263967 #> E -2.332741e-04 -0.001690246  0.001414232 -1.263967e-04  0.0006356848  ## Variation matrix variation(coda) #>           A         B           C          D         E #> A 0.0000000 0.2592742  1.53285862 0.08281464 0.1385604 #> B 0.8011244 0.0000000  3.00068035 0.54727146 0.6490135 #> C 1.5999805 0.7988561  0.00000000 1.11145146 0.9476375 #> D 1.5483008 0.7471764 -0.05167973 0.00000000 0.1870579 #> E 1.7152103 0.9140859  0.11522977 0.16690950 0.0000000","package":"nexus"},{"path":"https://packages.tesselle.org/kairos/articles/event.html","id":"definition","dir":"Articles","previous_headings":"","what":"Definition","title":"Event Date Model","text":"Event accumulation dates density estimates occupation duration archaeological site (L. Bellanger, Husi, Tomassone 2006; L. Bellanger, Tomassone, Husi 2008; Lise Bellanger Husi 2012). event date estimation terminus post-quem archaeological assemblage. accumulation date represents \u201cchronological profile\u201d assemblage. According Lise Bellanger Husi (2012), accumulation date can interpreted \u201cbest [\u2026] formation process reflecting duration succession events scale archaeological time, worst, imprecise dating due contamination context residual intrusive material.\u201d words, accumulation dates estimate occurrence archaeological events rhythms long term.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/event.html","id":"event-date","dir":"Articles","previous_headings":"Definition","what":"Event Date","title":"Event Date Model","text":"Event dates estimated fitting Gaussian multiple linear regression model factors resulting correspondence analysis - somewhat similar idea introduced Poblome Groenen (2003). model results known dates selection reliable contexts allows predict event dates remaining assemblages. First, correspondence analysis (CA) carried summarize information count matrix \\(X\\). correspondence analysis \\(X\\) provides coordinates \\(m\\) rows along \\(q\\) factorial components, denoted \\(f_{ik} ~\\forall \\\\left[ 1,m \\right], k \\\\left[ 1,q \\right]\\). , assuming \\(n\\) assemblages reliably dated another source, Gaussian multiple linear regression model fitted factorial components \\(n\\) dated assemblages: \\[ t^E_i = \\beta_{0} + \\sum_{k = 1}^{q} \\beta_{k} f_{ik} + \\epsilon_i ~\\forall \\[1,n] \\] \\(t^E_i\\) known date point estimate \\(\\)th assemblage, \\(\\beta_k\\) regression coefficients \\(\\epsilon_i\\) normally, identically independently distributed random variables, \\(\\epsilon_i \\sim \\mathcal{N}(0,\\sigma^2)\\). \\(n\\) equations stacked together written matrix notation \\[ t^E = F \\beta + \\epsilon \\] \\(\\epsilon \\sim \\mathcal{N}_{n}(0,\\sigma^2 I_{n})\\), \\(\\beta = \\left[ \\beta_0 \\cdots \\beta_q \\right]' \\\\mathbb{R}^{q+1}\\) \\[ F = \\begin{bmatrix} 1 & f_{11} & \\cdots & f_{1q} \\\\ 1 & f_{21} & \\cdots & f_{2q} \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ 1 & f_{n1} & \\cdots & f_{nq} \\end{bmatrix} \\] Assuming \\(F'F\\) nonsingular, ordinary least squares estimator unknown parameter vector \\(\\beta\\) : \\[ \\widehat{\\beta} = \\left( F'F \\right)^{-1} F' t^E \\] Finally, given vector CA coordinates \\(f_i\\), predicted event date assemblage \\(t^E_i\\) : \\[ \\widehat{t^E_i} = f_i \\hat{\\beta} \\] endpoints \\(100(1 \u2212 \\alpha)\\)% associated prediction confidence interval given : \\[ \\widehat{t^E_i} \\pm t_{\\alpha/2,n-q-1} \\sqrt{\\widehat{V}} \\] \\(\\widehat{V_i}\\) estimator variance prediction error:  \\[ \\widehat{V_i} = \\widehat{\\sigma}^2 \\left( f_i^T \\left( F'F \\right)^{-1} f_i + 1 \\right) \\] \\(\\widehat{\\sigma} = \\frac{\\sum_{=1}^{n} \\left( t_i - \\widehat{t^E_i} \\right)^2}{n - q - 1}\\). probability density event date \\(t^E_i\\) can described normal distribution: \\[ t^E_i \\sim \\mathcal{N}(\\widehat{t^E_i},\\widehat{V_i}) \\]","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/event.html","id":"accumulation-date","dir":"Articles","previous_headings":"Definition","what":"Accumulation Date","title":"Event Date Model","text":"row (assemblages) columns (types) CA coordinates linked together -called transition formulae, event dates type \\(t^E_j\\) can predicted following procedure . , accumulation date \\(t^A_i\\) defined weighted mean event date ceramic types found given assemblage. weights conditional frequencies respective types assemblage (akin MCD). accumulation date estimated : \\[ \\widehat{t^A_i} = \\sum_{j = 1}^{p} \\widehat{t^E_j} \\times \\frac{x_{ij}}{x_{\\cdot}} \\] probability density accumulation date \\(t^A_i\\) can described Gaussian mixture: \\[ t^A_i \\sim \\frac{x_{ij}}{x_{\\cdot}} \\mathcal{N}(\\widehat{t^E_j},\\widehat{V_j}^2) \\] Interestingly, integral accumulation date offers estimates cumulative occurrence archaeological events, close enough definition tempo plot introduced Dye (2016).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/event.html","id":"limitation","dir":"Articles","previous_headings":"","what":"Limitation","title":"Event Date Model","text":"Event accumulation dates estimation relies conditions assumptions matrix seriation problem. Dunnell (1970) summarizes conditions assumptions follows. homogeneity conditions state groups included seriation must: comparable duration. Belong cultural tradition. Come local area. mathematical assumptions state distribution historical temporal class: continuous time. Exhibits form unimodal curve. Theses assumptions create distributional model ordering accomplished arranging matrix class distributions approximate required pattern. resulting order inferred chronological. Predicted dates interpreted care: dates highly dependent range known dates fit regression.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/event.html","id":"usage","dir":"Articles","previous_headings":"","what":"Usage","title":"Event Date Model","text":"package provides implementation chronological modeling method developed Lise Bellanger Husi (2012). method slightly modified allows construction different probability density curves archaeological assemblage dates (event, activity tempo).    Resampling methods can used check stability resulting model. jackknife() used, one type/fabric removed time statistics recalculated. way, one can assess whether certain type/fabric substantial influence date estimate. bootstrap() used, large number new bootstrap assemblages created, sample size, resampling original assemblage replacement. , examination bootstrap statistics makes possible pinpoint assemblages require investigation.","code":"## Bellanger et al. did not publish the data supporting their demonstration:  ## no replication of their results is possible.  ## Here is an example using the Zuni dataset from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) ## The names of the vector entries must match the names of the assemblages zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, dates = zuni_dates, rank = 10)  ## Model summary ## (results are expressed in rata die) summary(model) #>  #> Call: #> stats::lm(formula = date ~ ., data = contexts) #>  #> Residuals: #>   LZ0852   LZ0610   LZ0578   LZ0569   LZ0563   LZ0329   LZ0322   LZ0279  #>  -479.32   351.48  1283.51   163.57 -1626.71  -290.90   950.04 -1427.33  #>   LZ0227   LZ0067   LZ0066  LZ0005Q     CS16    CS144   LZ1209  #>  -280.59   -50.24   266.02    45.83  -105.47  1016.24   183.86  #> attr(,\"class\") #> [1] \"RataDie\" #> attr(,\"class\")attr(,\"package\") #> [1] \"aion\" #>  #> Coefficients: #>             Estimate Std. Error t value Pr(>|t|)     #> (Intercept) 425015.4     1064.9 399.095 2.36e-10 *** #> F1          -57813.4      602.6 -95.938 7.08e-08 *** #> F2            9387.7      615.2  15.260 0.000108 *** #> F3           -2047.2      789.1  -2.594 0.060411 .   #> F4            3928.9     2127.1   1.847 0.138452     #> F5           -1146.2     1442.3  -0.795 0.471276     #> F6             995.1      485.4   2.050 0.109663     #> F7            1667.0     1906.7   0.874 0.431304     #> F8            4126.0     1264.9   3.262 0.031027 *   #> F9           -1889.1     1079.6  -1.750 0.155039     #> F10           -144.8      967.9  -0.150 0.888300     #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Residual standard error: 1494 on 4 degrees of freedom #> Multiple R-squared:  0.9997, Adjusted R-squared:  0.999  #> F-statistic:  1433 on 10 and 4 DF,  p-value: 1.168e-06  ## Extract model coefficients ## (convert results to Gregorian years) coef(model, calendar = CE()) #>  (Intercept)           F1           F2           F3           F4           F5  #> 1163.6512430 -158.2887134   25.7005062   -5.6059619   10.7559523   -3.1402928  #>           F6           F7           F8           F9          F10  #>    2.7236281    4.5617156   11.2951729   -5.1728421   -0.3984238  ## Extract residual standard deviation ## (convert results to Gregorian years) sigma(model, calendar = CE()) #> [1] 4.088072  ## Extract model residuals ## (convert results to Gregorian years) resid(model, calendar = CE()) #>  [1] -1.3132066  0.9602214  3.5123344  0.4453877 -4.4554781 -0.7975492 #>  [7]  2.6001038 -3.9104984 -0.7693659 -0.1399926  0.7260925  0.1228117 #> [13] -0.2908912  2.7814906  0.5009809  ## Extract model fitted values ## (convert results to Gregorian years) fitted(model, calendar = CE()) #>  [1] 1217.3105 1073.0370 1176.4849 1096.5531 1210.4540 1076.7948 1106.3999 #>  [8] 1122.9078 1104.7666  863.1376 1110.2712  858.8744 1328.2882 1259.2185 #> [15] 1250.4963 ## Estimate event dates ## (results are expressed in rata die) eve <- predict_event(model, margin = 1, level = 0.95) head(eve) #>            date    lower    upper    error #> LZ1105 426454.0 420445.5 432462.4 2164.087 #> LZ1103 417145.8 414689.9 419601.7  884.558 #> LZ1100 421968.1 416394.9 427541.4 2007.317 #> LZ1099 400974.7 396642.1 405307.4 1560.503 #> LZ1097 397176.5 393404.6 400948.3 1358.530 #> LZ1096 306371.0 300801.2 311940.7 2006.057 ## Activity plot plot(model, type = \"activity\", event = TRUE, select = 1:6) plot(model, type = \"activity\", event = TRUE, select = \"LZ1105\") ## Tempo plot plot(model, type = \"tempo\", select = \"LZ1105\") ## Check model variability ## (results are expressed in rata die) ## Warning: this may take a few seconds  ## Jackknife fabrics jack <- jackknife(model) head(jack) #>             date     lower     upper    error       bias #> LZ1105 155634807 155628798 155640815 2164.087 2638541997 #> LZ1103 152396320 152393864 152398776  884.558 2583645955 #> LZ1100 154171872 154166299 154177445 2007.317 2613748366 #> LZ1099 146238877 146234544 146243210 1560.503 2479244339 #> LZ1097 145631868 145628096 145635640 1358.530 2468989755 #> LZ1096 112226452 112220882 112232021 2006.057 1902641373  ## Bootstrap of assemblages boot <- bootstrap(model, n = 30) head(boot) #>             min     mean      max       Q5      Q95 #> LZ1105 406020.9 425870.4 436145.4 415913.2 434356.6 #> LZ1103 391447.1 417507.5 434309.3 402091.8 431136.3 #> LZ1100 372675.6 421710.3 454426.6 400813.4 443846.4 #> LZ1099 396986.6 400943.7 406847.4 397244.2 404539.2 #> LZ1097 331959.5 390339.5 429463.5 356698.7 418504.6 #> LZ1096 264824.9 303420.4 410236.2 264824.9 350859.9","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/seriation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Seriation","text":"matrix seriation problem archaeology based three conditions two assumptions, Dunnell (1970) summarizes follows. homogeneity conditions state groups included seriation must: comparable duration, Belong cultural tradition, Come local area. mathematical assumptions state distribution historical temporal class: continuous time, Exhibits form unimodal curve. Theses assumptions create distributional model ordering accomplished arranging matrix class distributions approximate required pattern. resulting order inferred chronological.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/seriation.html","id":"reciprocal-ranking","dir":"Articles","previous_headings":"","what":"Reciprocal ranking","title":"Seriation","text":"Reciprocal ranking iteratively rearrange rows /columns according weighted rank data matrix convergence (Ihm 2005). given incidence matrix \\(C\\): rows \\(C\\) rearranged increasing order : \\[ x_{} = \\sum_{j = 1}^{p} j \\frac{c_{ij}}{c_{\\cdot}} \\] columns \\(C\\) rearranged similar way: \\[ y_{j} = \\sum_{= 1}^{m} \\frac{c_{ij}}{c_{\\cdot j}} \\] two steps repeated convergence. Note procedure enter infinite loop.  positive difference column mean percentage (french \u201c\u00e9cart positif au pourcentage moyen\u201d, EPPM) represents deviation situation statistical independence (Desachy 2004). independence can interpreted absence relationships types chronological order assemblages, EPPM useful graphical tool explore significance relationship rows columns related seriation (Desachy 2004).","code":"## Build an incidence matrix with random data set.seed(12345) bin <- sample(c(TRUE, FALSE), 400, TRUE, c(0.6, 0.4)) incidence1 <- matrix(bin, nrow = 20)  ## Get seriation order on rows and columns ## If no convergence is reached before the maximum number of iterations (100),  ## it stops with a warning. (indices <- seriate_rank(incidence1, margin = c(1, 2), stop = 100)) #> <RankPermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 6 15 12 14 2 5 10 8 13 20 16 18 19 7 9 1 3 11 17 4... #> - Column order: 9 4 2 8 3 1 6 5 16 20 15 14 12 17 13 10 19 7 11 18...  ## Permute matrix rows and columns incidence2 <- permute(incidence1, indices)  ## Plot matrix tabula::plot_heatmap(incidence1, col = c(\"white\", \"black\")) tabula::plot_heatmap(incidence2, col = c(\"white\", \"black\")) ## Replicates Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Plot frequencies and EPPM values tabula::seriograph(compiegne) ## Get seriation order for columns on EPPM using the reciprocal ranking method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #> <RankPermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Permute columns compiegne_permuted <- permute(compiegne, indices)  ## Plot frequencies and EPPM values tabula::seriograph(compiegne_permuted)","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/seriation.html","id":"seriation","dir":"Articles","previous_headings":"Correspondence analysis","what":"Seriation","title":"Seriation","text":"Correspondence Analysis (CA) effective method seriation archaeological assemblages. order rows columns given coordinates along one dimension CA space, assumed account temporal variation. direction temporal change within correspondence analysis space arbitrary: additional information needed determine actual order time.","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni) ## Get row permutations from CA coordinates (zun_indices <- seriate_average(zuni, margin = c(1, 2))) #> <AveragePermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 372 387 350 367 110 417 364 407 357 356 160 373 34... #> - Column order: 18 14 17 16 13 15 9 8 12 11 6 7 5 10 4 2 3 1...  ## Plot CA results dimensio::biplot(zun_indices) ## Permute data matrix zuni_permuted <- permute(zuni, zun_indices)  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni_permuted)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/articles/seriation.html","id":"refining","dir":"Articles","previous_headings":"Correspondence analysis","what":"Refining","title":"Seriation","text":"Peeples Schachner (2012) propose procedure identify samples subject sampling error samples underlying structural relationships might influencing ordering along CA space. relies partial bootstrap approach CA-based seriation sample replicated n times. maximum dimension length convex hull around sample point cloud allows remove samples given cutoff value. According Peeples Schachner (2012), \u201c[] point removal procedure [results ] reduced dataset position individuals within CA highly stable produces ordering consistent assumptions frequency seriation.\u201d","code":"## Partial bootstrap CA ## Warning: this may take a few seconds! zuni_boot <- dimensio::bootstrap(zun_indices, n = 30)  ## Bootstrap CA results for the rows ## (add convex hull) zuni_boot |>    dimensio::viz_rows(col = \"lightgrey\", pch = 16) |>    dimensio::viz_hull(col = adjustcolor(\"#004488\", alpha = 0.5))  ## Bootstrap CA results for the columns zuni_boot |>    dimensio::viz_columns(pch = 16) ## Replicates Peeples and Schachner 2012 results ## Samples with convex hull maximum dimension length greater than the cutoff ## value will be marked for removal. ## Define cutoff as one standard deviation above the mean fun <- function(x) { mean(x) + sd(x) } (zuni_refine <- seriate_refine(zun_indices, cutoff = fun, margin = 1)) #> <RefinePermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 372 350 387 367 110 417 364 357 407 160 356 373 34... #> - Column order: 17 18 14 16 13 15 9 8 12 11 6 10 7 5 4 2 3 1... #> Partial bootstrap refinement: #> - Cutoff value: 1.75 #> - Rows to keep: 354 of 420 (84%)  ## Plot CA results for the rows dimensio::viz_rows(zuni_refine, highlight = \"observation\", pch = c(16, 15)) ## Histogram of convex hull maximum dimension length hist(zuni_refine[[\"length\"]], xlab = \"Maximum length\", main = \"\") abline(v = zuni_refine[[\"cutoff\"]], col = \"red\") ## Permute data matrix zuni_permuted2 <- permute(zuni, zuni_refine)  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni_permuted2)","package":"kairos"},{"path":[],"package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"convenient reproducible toolkit relative absolute dating analysis chronological patterns. package includes functions chronological modeling dating archaeological assemblages count data. provides methods matrix seriation. also allows compute time point estimates density estimates occupation duration archaeological site. kairos provides methods : Matrix seriation: seriate_rank() seriate_average() Mean ceramic date estimation (South 1977): mcd() Event accumulation date estimation (Bellanger Husi 2012): event() Aoristic analysis (Ratcliffe 2000): aoristic() Chronological apportioning (Roberts et al.\u00a02012): apportion() tabula companion package kairos provides functions visualization analysis archaeological count data.","code":"To cite kairos in publications use:    Frerebeau N (2023). _kairos: Analysis of Chronological Patterns from   Archaeological Count Data_. Universit\u00e9 Bordeaux Montaigne, Pessac,   France. doi:10.5281/zenodo.5653896   <https://doi.org/10.5281/zenodo.5653896>, R package version 2.0.1,   <https://packages.tesselle.org/kairos/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{kairos: Analysis of Chronological Patterns from Archaeological Count Data}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 2.0.1},     url = {https://packages.tesselle.org/kairos/},     doi = {10.5281/zenodo.5653896},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"can install released version kairos CRAN : development version GitHub :","code":"install.packages(\"kairos\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/kairos\")","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"kairos v2.0 uses aion internal date representation. Look vignette(\"aion\") start. assumes keep data tidy: variable (type/taxa) must saved column observation (sample/case) must saved row.","code":"## Load packages library(kairos) ## Data from Husi 2022 data(\"loire\", package = \"folio\") keep <- c(\"Anjou\", \"Bl\u00e9sois\", \"Orl\u00e9anais\", \"Haut-Poitou\", \"Touraine\") loire <- subset(loire, area %in% keep)  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (weights) by group ao <- aoristic(loire_range, step = 50, weight = TRUE, groups = loire$area)  ## Plot plot(ao, col = \"grey\") ## Rate of change by group ro <- roc(ao, n = 30) plot(ro)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"Please note kairos project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":[],"package":"kairos"},{"path":[],"package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/AoristicSum-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Aoristic Sum \u2014 AoristicSum-class","title":"Aoristic Sum \u2014 AoristicSum-class","text":"S4 class represent aoristic analysis results.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/AoristicSum-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Aoristic Sum \u2014 AoristicSum-class","text":"breaks RataDie vector giving date break time-blocks. weights numeric vector. groups character vector store group names (). p numeric array giving aorisitic probabilities.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/AoristicSum-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Aoristic Sum \u2014 AoristicSum-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/AoristicSum-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Aoristic Sum \u2014 AoristicSum-class","text":"code snippets , x AoristicSum object. .data.frame(x) Coerces data.frame.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/AoristicSum-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Aoristic Sum \u2014 AoristicSum-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/CountApportion-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Apportioning \u2014 CountApportion-class","title":"Count Apportioning \u2014 CountApportion-class","text":"S4 class represent artifact apportioning results. Gives apportioning artifact types (columns) per site (rows) per period (dim. 3).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/CountApportion-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Count Apportioning \u2014 CountApportion-class","text":".Data \\(m \\times p \\times k\\) array giving proportion artifact type (\\(p\\)) given period (\\(k\\)). p \\(m \\times p \\times k\\) array giving probability apportioning artifact type (\\(p\\)) given period (\\(k\\)). method character string specifying distribution used apportioning (type popularity curve). length-one numeric vector giving beginning period interest (years AD). length-one numeric vector giving end period interest (years AD). step length-one numeric vector giving step size, .e. width time step apportioning (years AD).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/CountApportion-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count Apportioning \u2014 CountApportion-class","text":"class inherits base array.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/CountApportion-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Count Apportioning \u2014 CountApportion-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/EventDate-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Date Model \u2014 EventDate-class","title":"Date Model \u2014 EventDate-class","text":"S4 class store event accumulation times archaeological assemblages.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/EventDate-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Date Model \u2014 EventDate-class","text":"dates length-\\(m\\) numeric vector dates expressed rata die. model multiple linear model: Gaussian multiple linear regression model fitted event date estimation prediction. keep integer vector giving subscripts CA components keep.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/EventDate-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Date Model \u2014 EventDate-class","text":"Dates internally stored rata die. class inherits dimensio::CA.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/EventDate-class.html","id":"extract","dir":"Reference","previous_headings":"","what":"Extract","title":"Date Model \u2014 EventDate-class","text":"code snippets , x EventDate object. time(x) Extract dates assemblages. coef(x) Extract model coefficients. fitted(x) Extract model fitted values. residuals(x) Extract model residuals. sigma(x) Extract residual standard deviation. terms(x) Extract model terms.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/EventDate-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Date Model \u2014 EventDate-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/IncrementTest-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Frequency Increment Test \u2014 IncrementTest-class","title":"Frequency Increment Test \u2014 IncrementTest-class","text":"S4 class represent Frequency Increment Test results.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/IncrementTest-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Frequency Increment Test \u2014 IncrementTest-class","text":"statistic numeric vector giving values t-statistic. parameter integer giving degrees freedom t-statistic. p_value numeric vector giving p-value test.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/IncrementTest-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Frequency Increment Test \u2014 IncrementTest-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/IncrementTest-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Frequency Increment Test \u2014 IncrementTest-class","text":"code snippets , x IncrementTest object. .data.frame(x) Coerces data.frame.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/IncrementTest-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Frequency Increment Test \u2014 IncrementTest-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/MeanDate-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Date \u2014 MeanDate-class","title":"Mean Date \u2014 MeanDate-class","text":"S4 class store weighted mean date (e.g. Mean Ceramic Date) archaeological assemblages.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/MeanDate-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Mean Date \u2014 MeanDate-class","text":"dates length-\\(p\\) numeric vector giving dates (ceramic) types expressed rata die. replications numeric matrix giving replications.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/MeanDate-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mean Date \u2014 MeanDate-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/MeanDate-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Mean Date \u2014 MeanDate-class","text":"code snippets , x MeanDate object. .data.frame(x) Coerces data.frame.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/MeanDate-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean Date \u2014 MeanDate-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/PermutationOrder-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutation Order \u2014 PermutationOrder-class","title":"Permutation Order \u2014 PermutationOrder-class","text":"S4 classes represent permutation order.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/PermutationOrder-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Permutation Order \u2014 PermutationOrder-class","text":"rows_order integer vector giving rows permutation. columns_order integer vector giving columns permutation.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/PermutationOrder-class.html","id":"subset","dir":"Reference","previous_headings":"","what":"Subset","title":"Permutation Order \u2014 PermutationOrder-class","text":"code snippets , x PermutationOrder object. x[[]] Extract information slot selected subscript . length-one character vector.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/PermutationOrder-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Permutation Order \u2014 PermutationOrder-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RateOfChange-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate of Change \u2014 RateOfChange-class","title":"Rate of Change \u2014 RateOfChange-class","text":"S4 class represent rates change aoristic analysis.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RateOfChange-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Rate of Change \u2014 RateOfChange-class","text":"replicates non-negative integer giving number replications. groups character vector store group names ().","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RateOfChange-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rate of Change \u2014 RateOfChange-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RateOfChange-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Rate of Change \u2014 RateOfChange-class","text":"code snippets , x AoristicSum object. .data.frame(x) Coerces data.frame.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RateOfChange-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rate of Change \u2014 RateOfChange-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RefinePermutationOrder-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial Bootstrap CA \u2014 RefinePermutationOrder-class","title":"Partial Bootstrap CA \u2014 RefinePermutationOrder-class","text":"S4 class store partial bootstrap correspondence analysis results.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RefinePermutationOrder-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Partial Bootstrap CA \u2014 RefinePermutationOrder-class","text":"length numeric vector giving convex hull maximum dimension length. cutoff length-one numeric vector giving cutoff value samples selection. keep integer vector giving subscript variables kept.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/RefinePermutationOrder-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Partial Bootstrap CA \u2014 RefinePermutationOrder-class","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":null,"dir":"Reference","previous_headings":"","what":"Aoristic Analysis \u2014 aoristic","title":"Aoristic Analysis \u2014 aoristic","text":"Computes aoristic sum.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aoristic Analysis \u2014 aoristic","text":"","code":"aoristic(x, y, ...)  # S4 method for numeric,numeric aoristic(   x,   y,   step = 1,   start = min(x),   end = max(y),   calendar = CE(),   weight = TRUE,   groups = NULL )  # S4 method for ANY,missing aoristic(   x,   step = 1,   start = NULL,   end = NULL,   calendar = CE(),   weight = TRUE,   groups = NULL )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aoristic Analysis \u2014 aoristic","text":"x, y numeric vector giving lower upper boundaries time intervals, respectively. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. step length-one integer vector giving step size, .e. width time step time series (defaults \\(1\\), .e. annual level). start length-one numeric vector giving beginning time window. end length-one numeric vector giving end time window. calendar TimeScale object specifying calendar x y (see calendar()). Defaults Gregorian Common Era. weight logical scalar: aoristic sum weighted length periods (default). FALSE aoristic sum number elements within time block. groups factor vector sense .factor(groups) defines grouping. x list (data.frame), groups can length-one vector giving index grouping component (column) x.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aoristic Analysis \u2014 aoristic","text":"AoristicSum object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aoristic Analysis \u2014 aoristic","text":"Aoristic analysis used determine probability contemporaneity archaeological sites assemblages. aoristic analysis distributes probability event uniformly temporal fraction period considered. aoristic sum distribution total number events assumed within period. Muller Hinz (2018) pointed overlapping temporal intervals related period categorization dating accuracy likely bias analysis. proposed weighting method overcome problem. method implemented (moment), see aoristAAR package.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Aoristic Analysis \u2014 aoristic","text":"Crema, E. R. (2012). Modelling Temporal Uncertainty Archaeological Analysis. Journal Archaeological Method Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3 . Johnson, . (2004). Aoristic Analysis: Seeds New Approach Mapping Archaeological Distributions Time. Ausserer, K. F., B\u00f6rner, W., Goriany, M. & Karlhuber-V\u00f6ckl, L. (ed.), Enter Past - E-Way Four Dimensions Cultural Heritage, Oxford: Archaeopress, p. 448-52. BAR International Series 1227. doi:10.15496/publikation-2085 M\u00fcller-Schee\u00dfel, N. & Hinz, M. (2018). Aoristic Research R: Correcting Temporal Categorizations Archaeology. Presented Human History Digital Future (CAA 2018), Tubingen, March 21. https://www.youtube.com/watch?v=bUBukex30QI. Palmisano, ., Bevan, . & Shennan, S. (2017). Comparing Archaeological Proxies Long-Term Population Patterns: Example Central Italy. Journal Archaeological Science, 87: 59-72. doi:10.1016/j.jas.2017.10.001 . Ratcliffe, J. H. (2000). Aoristic Analysis: Spatial Interpretation Unspecific Temporal Events. International Journal Geographical Information Science, 14(7): 669-79. doi:10.1080/136588100424963 . Ratcliffe, J. H. (2002). Aoristic Signatures Spatio-Temporal Analysis High Volume Crime Patterns. Journal Quantitative Criminology, 18(1): 23-43. doi:10.1023/:1013240828824 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Aoristic Analysis \u2014 aoristic","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aoristic Analysis \u2014 aoristic","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Chronological Apportioning \u2014 apportion","title":"Chronological Apportioning \u2014 apportion","text":"Chronological Apportioning","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chronological Apportioning \u2014 apportion","text":"","code":"apportion(object, ...)  # S4 method for data.frame apportion(   object,   s0,   s1,   t0,   t1,   from = min(s0),   to = max(s1),   step = 25,   method = c(\"uniform\", \"truncated\"),   z = 2,   progress = getOption(\"kairos.progress\") )  # S4 method for matrix apportion(   object,   s0,   s1,   t0,   t1,   from = min(s0),   to = max(s1),   step = 25,   method = c(\"uniform\", \"truncated\"),   z = 2,   progress = getOption(\"kairos.progress\") )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chronological Apportioning \u2014 apportion","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. s0 length-\\(m\\) numeric vector giving site beginning dates expressed CE years (BCE years must given negative numbers). s1 length-\\(m\\) numeric vector giving site end dates expressed CE years (BCE years must given negative numbers). t0 length-\\(p\\) numeric vector giving type beginning dates expressed CE years (BCE years must given negative numbers). t1 length-\\(p\\) numeric vector giving type end dates expressed CE years (BCE years must given negative numbers). length-one numeric vector giving beginning period interest (years CE). length-one numeric vector giving end period interest (years CE). step length-one integer vector giving step size, .e. width time step apportioning (years CE; defaults \\(25\\)). method character string specifying distribution used (type popularity curve). must one \"uniform\" (uniform distribution) \"truncated\" (truncated standard normal distribution). unambiguous substring can given. z integer value giving lower upper truncation points (defaults \\(2\\)). used method \"truncated\". progress logical scalar: progress bar displayed?","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Chronological Apportioning \u2014 apportion","text":"CountApportion object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chronological Apportioning \u2014 apportion","text":"Roberts, J. M., Mills, B. J., Clark, J. J., Haas, W. R., Huntley, D. L. & Trowbridge, M. . (2012). Method Chronological Apportioning Ceramic Assemblages. Journal Archaeological Science, 39(5): 1513-20. doi:10.1016/j.jas.2011.12.022 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Chronological Apportioning \u2014 apportion","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/data.frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Data Frame \u2014 data.frame","title":"Coerce to a Data Frame \u2014 data.frame","text":"Coerce Data Frame","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/data.frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Data Frame \u2014 data.frame","text":"","code":"# S4 method for MeanDate as.data.frame(x, ..., calendar = getOption(\"kairos.calendar\"))  # S4 method for AoristicSum as.data.frame(x, ..., calendar = getOption(\"kairos.calendar\"))  # S4 method for IncrementTest as.data.frame(x, row.names = NULL, optional = FALSE, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/data.frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Data Frame \u2014 data.frame","text":"x object. ... parameters passed data.frame(). calendar TimeScale object specifying target calendar (see calendar()). NULL, rata die returned. row.names, optional Currently used.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/data.frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Data Frame \u2014 data.frame","text":"data.frame extra time column giving (decimal) years time series sampled.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/data.frame.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Data Frame \u2014 data.frame","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":null,"dir":"Reference","previous_headings":"","what":"Event and Accumulation Dates \u2014 event","title":"Event and Accumulation Dates \u2014 event","text":"Fits date event model.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Event and Accumulation Dates \u2014 event","text":"","code":"event(object, dates, ...)  # S4 method for data.frame,numeric event(object, dates, rank = NULL, sup_row = NULL, calendar = CE(), ...)  # S4 method for matrix,numeric event(object, dates, rank = NULL, sup_row = NULL, calendar = CE(), ...)  # S4 method for EventDate summary(object, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Event and Accumulation Dates \u2014 event","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates numeric vector dates. named, names must match row names object. ... arguments passed internal methods. rank integer specifying number CA factorial components use linear model fitting (see details). NULL (default), axes corresponding least 60% inertia used. sup_row numeric logical vector specifying indices supplementary rows. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Event and Accumulation Dates \u2014 event","text":"EventDate object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Event and Accumulation Dates \u2014 event","text":"implementation chronological modeling method proposed Bellanger Husi (2012, 2013). Event accumulation dates density estimates occupation duration archaeological site (Bellanger Husi 2012, 2013). event date estimation terminus post-quem archaeological assemblage. accumulation date represents \"chronological profile\" assemblage. According Bellanger Husi (2012), accumulation date can interpreted \"best [...] formation process reflecting duration succession events scale archaeological time, worst, imprecise dating due contamination context residual intrusive material.\" words, accumulation dates estimate occurrence archaeological events rhythms long term. Dates converted rata die computation. method relies strong archaeological statistical assumptions (see vignette(\"event\")).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Event and Accumulation Dates \u2014 event","text":"Bellanger, L. & Husi, P. (2013). Mesurer et mod\u00e9liser le temps inscrit dans la mati\u00e8re \u00e0 partir d'une source mat\u00e9rielle : la c\u00e9ramique m\u00e9di\u00e9vale. Mesure et Histoire M\u00e9di\u00e9vale. Histoire ancienne et m\u00e9di\u00e9vale. Paris: Publication de la Sorbonne, p. 119-134. Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Bellanger, L., Tomassone, R. & Husi, P. (2008). Statistical Approach Dating Archaeological Contexts. Journal Data Science, 6, 135-154. Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes arch\u00e9ologiques. Revue de Statistique Appliqu\u00e9e, 54(2), 65-81. Bellanger, L., Husi, P. & Tomassone, R. (2006). Statistical Aspects Pottery Quantification Dating Archaeological Contexts. Archaeometry, 48(1), 169-183. doi:10.1111/j.1475-4754.2006.00249.x . Poblome, J. & Groenen, P. J. F. (2003). Constrained Correspondence Analysis Seriation Sagalassos Tablewares. Doerr, M. & Apostolis, S. (eds.), Digital Heritage Archaeology. Athens: Hellenic Ministry Culture.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Event and Accumulation Dates \u2014 event","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Event and Accumulation Dates \u2014 event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Frequency Increment Test \u2014 fit","title":"Frequency Increment Test \u2014 fit","text":"Frequency Increment Test","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Frequency Increment Test \u2014 fit","text":"","code":"fit(object, dates, ...)  # S4 method for data.frame,numeric fit(object, dates, calendar = CE(), level = 0.95, roll = FALSE, window = 3)  # S4 method for matrix,numeric fit(object, dates, calendar = CE(), level = 0.95, roll = FALSE, window = 3)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Frequency Increment Test \u2014 fit","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates length-\\(m\\) numeric vector dates. ... Currently used. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era. level length-one numeric vector giving confidence level. roll logical scalar: time series subsetted look episodes selection? window odd integer giving size rolling window. used roll TRUE.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Frequency Increment Test \u2014 fit","text":"IncrementTest object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Frequency Increment Test \u2014 fit","text":"Frequency Increment Test (FIT) rejects neutrality distribution normalized variant frequency increments exhibits mean deviates significantly zero. roll TRUE, time series subsetted according window see episodes selection can identified among variables might show overall selection.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Frequency Increment Test \u2014 fit","text":"Feder, . F., Kryazhimskiy, S. & Plotkin, J. B. (2014). Identifying Signatures Selection Genetic Time Series. Genetics, 196(2): 509-522. doi:10.1534/genetics.113.158220 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Frequency Increment Test \u2014 fit","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Frequency Increment Test \u2014 fit","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Group by phase ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Frequency Increment Test freq <- fit(counts, dates, calendar = NULL)  ## Plot time vs abundance plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")   ## Plot time vs abundance and highlight selection freq <- fit(counts, dates, calendar = NULL, roll = TRUE, window = 5) plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/kairos-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in kairos \u2014 kairos-deprecated","title":"Deprecated Functions in kairos \u2014 kairos-deprecated","text":"functions still work removed (defunct) next version.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/kairos-package.html","id":null,"dir":"Reference","previous_headings":"","what":"kairos: Analysis of Chronological Patterns from Archaeological Count Data \u2014 kairos-package","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data \u2014 kairos-package","text":"toolkit absolute relative dating analysis chronological patterns. package includes functions chronological modeling dating archaeological assemblages count data. provides methods matrix seriation. also allows compute time point estimates density estimates occupation duration archaeological site.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/kairos-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data \u2014 kairos-package","text":"kairos uses following options() configure behavior: kairos.progress: logical scalar. progress bars displayed?","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/kairos-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data \u2014 kairos-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Ceramic Date \u2014 mcd","title":"Mean Ceramic Date \u2014 mcd","text":"Estimates Mean Ceramic Date assemblage.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean Ceramic Date \u2014 mcd","text":"","code":"mcd(object, dates, ...)  # S4 method for numeric,numeric mcd(object, dates, calendar = CE())  # S4 method for data.frame,numeric mcd(object, dates, calendar = CE())  # S4 method for matrix,numeric mcd(object, dates, calendar = CE())","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean Ceramic Date \u2014 mcd","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates length-\\(p\\) numeric vector dates expressed years. ... Currently used. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean Ceramic Date \u2014 mcd","text":"MeanDate object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mean Ceramic Date \u2014 mcd","text":"Mean Ceramic Date (MCD) point estimate occupation archaeological site (South 1977). MCD estimated weighted mean date midpoints ceramic types (based absolute dates known production interval) found given assemblage. weights relative frequencies respective types assemblage. bootstrapping procedure used estimate confidence interval given MCD. assemblage, large number new bootstrap replicates created, sample size, resampling original assemblage replacement. MCDs calculated replicates upper lower boundaries confidence interval associated MCD returned.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mean Ceramic Date \u2014 mcd","text":"South, S. . (1977). Method Theory Historical Archaeology. New York: Academic Press.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean Ceramic Date \u2014 mcd","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean Ceramic Date \u2014 mcd","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Set the start and end dates for each ceramic type dates <- list(   LINO = c(600, 875), KIAT = c(850, 950), RED = c(900, 1050),   GALL = c(1025, 1125), ESC = c(1050, 1150), PUBW = c(1050, 1150),   RES = c(1000, 1200), TULA = c(1175, 1300), PINE = c(1275, 1350),   PUBR = c(1000, 1200), WING = c(1100, 1200), WIPO = c(1125, 1225),   SJ = c(1200, 1300), LSJ = c(1250, 1300), SPR = c(1250, 1300),   PINER = c(1275, 1325), HESH = c(1275, 1450), KWAK = c(1275, 1450) )  ## Calculate date midpoints mid <- vapply(X = dates, FUN = mean, FUN.VALUE = numeric(1))  ## Calculate MCD (mc_dates <- mcd(zuni[100:125, ], dates = mid)) #> 26 x 18 x 1 time series observed between 276230.3 and 459613.1 r.d.  ## Get MCD in years CE time(mc_dates, calendar = CE()) #>  [1]  757.2912  796.6659  797.4991  952.5855  996.2952 1016.0738 1027.5011 #>  [8] 1059.5249 1073.6597 1075.5213 1089.5820 1092.8564 1100.0000 1127.7799 #> [15] 1137.1101 1200.0017 1204.3868 1207.1436 1219.4454 1227.3745 1235.4176 #> [22] 1237.5000 1238.8896 1253.1241 1256.2502 1259.3757  ## Plot plot(mc_dates)   ## Bootstrap resampling boot <- bootstrap(mc_dates, n = 30) head(boot) #>         original      mean       bias     error #> LZ0789  757.2917       NaN        NaN        NA #> LZ0783  796.6667  897.7687 101.102036 143.52637 #> LZ0782  797.5000  871.5205  74.020540 104.27580 #> LZ0778  952.5862  969.8740  17.287770  91.74427 #> LZ0777  996.2963 1002.7472   6.450952  83.22155 #> LZ0776 1016.0714 1037.9794  21.908005  71.20200  ## Jackknife resampling jack <- jackknife(mc_dates) head(jack) #>         original      mean       bias    error #> LZ0789  757.2917  768.2870 186.921296 207.5535 #> LZ0783  796.6667  806.9974 175.621693 228.0861 #> LZ0782  797.5000  804.1715 113.415558 169.0563 #> LZ0778  952.5862  954.5205  32.882529 138.6064 #> LZ0777  996.2963  996.6640   6.251785 111.0144 #> LZ0776 1016.0714 1017.1652  18.594831  72.6602  ## Simulation sim <- simulate(mc_dates, nsim = 30) plot(sim, interval = \"range\", pch = 16)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/model.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Model Results \u2014 model","title":"Extract Model Results \u2014 model","text":"coef() extracts model coefficients (see stats::coef()). fitted() extracts model fitted values (see stats::fitted()). residuals() extracts model residuals (see stats::residuals()). sigma() extracts residual standard deviation (see stats::sigma()). terms() extracts model terms (see stats::terms()).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Model Results \u2014 model","text":"","code":"# S4 method for EventDate coef(object, calendar = NULL, ...)  # S4 method for EventDate fitted(object, calendar = NULL, ...)  # S4 method for EventDate residuals(object, calendar = NULL, ...)  # S4 method for EventDate sigma(object, calendar = NULL, ...)  # S4 method for EventDate terms(x, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Model Results \u2014 model","text":"calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned. ... Currently used. x, object EventDate object.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/model.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Model Results \u2014 model","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Model Results \u2014 model","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mutators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or Set Parts of an Object \u2014 mutators","title":"Get or Set Parts of an Object \u2014 mutators","text":"Getters setters retrieve set parts object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mutators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get or Set Parts of an Object \u2014 mutators","text":"","code":"# S4 method for AoristicSum weights(object, ...)  # S4 method for CountApportion weights(object, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mutators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or Set Parts of an Object \u2014 mutators","text":"object object get set element(s). ... Currently used.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/mutators.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get or Set Parts of an Object \u2014 mutators","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":null,"dir":"Reference","previous_headings":"","what":"Rearranges a Data Matrix \u2014 permute","title":"Rearranges a Data Matrix \u2014 permute","text":"permute() rearranges data matrix according permutation order. get_order() returns seriation order rows /columns.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rearranges a Data Matrix \u2014 permute","text":"","code":"permute(object, order, ...)  get_order(x, ...)  # S4 method for data.frame,PermutationOrder permute(object, order)  # S4 method for matrix,PermutationOrder permute(object, order)  # S4 method for PermutationOrder get_order(x, margin = c(1, 2))","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rearranges a Data Matrix \u2014 permute","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. x, order PermutationOrder object giving permutation order rows columns. margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rearranges a Data Matrix \u2014 permute","text":"permute() returns permuted matrix permuted data.frame (object).","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rearranges a Data Matrix \u2014 permute","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/permute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rearranges a Data Matrix \u2014 permute","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #> <RankPermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Aoristic Analysis \u2014 plot_aoristic","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"Plot Aoristic Analysis","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"","code":"# S4 method for AoristicSum,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   type = c(\"bar\"),   flip = FALSE,   ncol = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for AoristicSum image(x, calendar = getOption(\"kairos.calendar\"), ...)  # S4 method for RateOfChange,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   level = 0.95,   flip = FALSE,   ncol = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"x AoristicSum object. calendar TimeScale object specifying target calendar (see calendar()). type character string specifying whether bar density plotted? must one \"bar\" \"density\". unambiguous substring can given. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... parameters passed panel (e.g. graphical parameters). level length-one numeric vector giving confidence level.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_aoristic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Aoristic Analysis \u2014 plot_aoristic","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Event Plot \u2014 plot_event","title":"Event Plot \u2014 plot_event","text":"Produces activity tempo plot.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Event Plot \u2014 plot_event","text":"","code":"# S4 method for EventDate,missing plot(   x,   type = c(\"activity\", \"tempo\"),   event = FALSE,   calendar = getOption(\"kairos.calendar\"),   select = 1,   n = 500,   eps = 1e-09,   col.accumulation = \"black\",   col.event = \"red\",   flip = FALSE,   ncol = NULL,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   ... )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Event Plot \u2014 plot_event","text":"x EventDate object. type character string indicating type plot. must one \"activity\" (default) \"tempo\" (see details). unambiguous substring can given. event logical scalar: distribution event date displayed? used type \"activity\". calendar TimeScale object specifying target calendar (see calendar()). select numeric character vector giving selection assemblage drawn. n length-one non-negative numeric vector giving desired length vector quantiles density computation. eps length-one numeric value giving cutoff values removed. col.accumulation color specification accumulation density curve. col.event color specification event density curve. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? ... parameters passed panel (e.g. graphical parameters).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Event Plot \u2014 plot_event","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"event-and-acccumulation-dates","dir":"Reference","previous_headings":"","what":"Event and Acccumulation Dates","title":"Event Plot \u2014 plot_event","text":"plot() displays probability estimate density curves archaeological assemblage dates (event accumulation dates; Bellanger Husi 2012). event date plotted line, accumulation date shown grey filled area. accumulation date can displayed tempo plot (Dye 2016) activity plot (Philippe Vibet 2020): tempo tempo plot estimates cumulative occurrence archaeological events, slope plot directly reflects pace change. activity activity plot displays first derivative tempo plot.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Event Plot \u2014 plot_event","text":"Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Dye, T. S. (2016). Long-Term Rhythms Development Hawaiian Social Stratification. Journal Archaeological Science, 71, 1-9. doi:10.1016/j.jas.2016.05.006 . Philippe, . & Vibet, M.-. (2020). Analysis Archaeological Phases Using R Package ArchaeoPhases. Journal Statistical Software, Code Snippets, 93(1), 1-25. doi:10.18637/jss.v093.c01 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Event Plot \u2014 plot_event","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Event Plot \u2014 plot_event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection of Selective Processes \u2014 plot_fit","title":"Detection of Selective Processes \u2014 plot_fit","text":"Produces abundance vs time diagram.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection of Selective Processes \u2014 plot_fit","text":"","code":"# S4 method for IncrementTest,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   col.neutral = \"#004488\",   col.selection = \"#BB5566\",   col.roll = \"grey\",   flip = FALSE,   ncol = NULL,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   ... )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection of Selective Processes \u2014 plot_fit","text":"x IncrementTest object plotted. calendar TimeScale object specifying target calendar (see calendar()). col.neutral, col.selection, col.roll vector colors. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? ... parameters passed panel (e.g. graphical parameters).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detection of Selective Processes \u2014 plot_fit","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Detection of Selective Processes \u2014 plot_fit","text":"Results frequency increment test can displayed abundance vs time diagram aid detection quantification selective processes archaeological record. roll TRUE, time series subsetted according window see episodes selection can identified among decoration types might show overall selection. , shading highlights data points fit() identifies selection.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Detection of Selective Processes \u2014 plot_fit","text":"Displaying FIT results abundance vs time diagram adapted Ben Marwick's original idea.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection of Selective Processes \u2014 plot_fit","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detection of Selective Processes \u2014 plot_fit","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Group by phase ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Frequency Increment Test freq <- fit(counts, dates, calendar = NULL)  ## Plot time vs abundance plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")   ## Plot time vs abundance and highlight selection freq <- fit(counts, dates, calendar = NULL, roll = TRUE, window = 5) plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"MCD Plot \u2014 plot_mcd","title":"MCD Plot \u2014 plot_mcd","text":"MCD Plot","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MCD Plot \u2014 plot_mcd","text":"","code":"# S4 method for MeanDate,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   decreasing = TRUE,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for SimulationMeanDate,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   interval = \"student\",   level = 0.8,   decreasing = TRUE,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MCD Plot \u2014 plot_mcd","text":"x MeanDate object. calendar TimeScale object specifying target calendar (see calendar()). decreasing logical scalar: sort increasing decreasing? main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters. interval character string giving type confidence interval returned. must one \"student\" (default), \"normal\", \"percentiles\" \"range\" (min-max). unambiguous substring can given. level length-one numeric vector giving confidence level. used interval \"range\".","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MCD Plot \u2014 plot_mcd","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"MCD Plot \u2014 plot_mcd","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_mcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MCD Plot \u2014 plot_mcd","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Set the start and end dates for each ceramic type dates <- list(   LINO = c(600, 875), KIAT = c(850, 950), RED = c(900, 1050),   GALL = c(1025, 1125), ESC = c(1050, 1150), PUBW = c(1050, 1150),   RES = c(1000, 1200), TULA = c(1175, 1300), PINE = c(1275, 1350),   PUBR = c(1000, 1200), WING = c(1100, 1200), WIPO = c(1125, 1225),   SJ = c(1200, 1300), LSJ = c(1250, 1300), SPR = c(1250, 1300),   PINER = c(1275, 1325), HESH = c(1275, 1450), KWAK = c(1275, 1450) )  ## Calculate date midpoints mid <- vapply(X = dates, FUN = mean, FUN.VALUE = numeric(1))  ## Calculate MCD (mc_dates <- mcd(zuni[100:125, ], dates = mid)) #> 26 x 18 x 1 time series observed between 276230.3 and 459613.1 r.d.  ## Get MCD in years CE time(mc_dates, calendar = CE()) #>  [1]  757.2912  796.6659  797.4991  952.5855  996.2952 1016.0738 1027.5011 #>  [8] 1059.5249 1073.6597 1075.5213 1089.5820 1092.8564 1100.0000 1127.7799 #> [15] 1137.1101 1200.0017 1204.3868 1207.1436 1219.4454 1227.3745 1235.4176 #> [22] 1237.5000 1238.8896 1253.1241 1256.2502 1259.3757  ## Plot plot(mc_dates)   ## Bootstrap resampling boot <- bootstrap(mc_dates, n = 30) head(boot) #>         original      mean      bias     error #> LZ0789  757.2917       NaN       NaN        NA #> LZ0783  796.6667  864.0069 67.340276 129.09206 #> LZ0782  797.5000  848.6961 51.196112 103.65941 #> LZ0778  952.5862  994.9726 42.386429  97.74929 #> LZ0777  996.2963 1003.1996  6.903294  81.11699 #> LZ0776 1016.0714 1029.8378 13.766340  65.24370  ## Jackknife resampling jack <- jackknife(mc_dates) head(jack) #>         original      mean       bias    error #> LZ0789  757.2917  768.2870 186.921296 207.5535 #> LZ0783  796.6667  806.9974 175.621693 228.0861 #> LZ0782  797.5000  804.1715 113.415558 169.0563 #> LZ0778  952.5862  954.5205  32.882529 138.6064 #> LZ0777  996.2963  996.6640   6.251785 111.0144 #> LZ0776 1016.0714 1017.1652  18.594831  72.6602  ## Simulation sim <- simulate(mc_dates, nsim = 30) plot(sim, interval = \"range\", pch = 16)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Abundance vs Time Plot \u2014 plot_time","title":"Abundance vs Time Plot \u2014 plot_time","text":"Produces abundance vs time diagram.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abundance vs Time Plot \u2014 plot_time","text":"","code":"plot_time(object, dates, ...)  # S4 method for data.frame,numeric plot_time(object, dates, calendar = getOption(\"kairos.calendar\"), ...)  # S4 method for matrix,numeric plot_time(object, dates, calendar = getOption(\"kairos.calendar\"), ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abundance vs Time Plot \u2014 plot_time","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates numeric vector dates. ... parameters passed aion::plot(). calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Abundance vs Time Plot \u2014 plot_time","text":"plot_time() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Abundance vs Time Plot \u2014 plot_time","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/plot_time.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Abundance vs Time Plot \u2014 plot_time","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Coerce the merzbach dataset to a count matrix ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Set dates ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Plot abundance vs time plot_time(counts, dates, calendar = NULL, ncol = 3, xlab = \"Phases\")","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Event and Accumulation Dates \u2014 predict_event","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"Estimates event accumulation dates assemblage.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"","code":"predict_event(object, data, ...)  predict_accumulation(object, data, ...)  # S4 method for EventDate,missing predict_event(object, margin = 1, level = 0.95, calendar = NULL)  # S4 method for EventDate,matrix predict_event(object, data, margin = 1, level = 0.95, calendar = NULL)  # S4 method for EventDate,missing predict_accumulation(object, calendar = NULL)  # S4 method for EventDate,matrix predict_accumulation(object, data, level = 0.95, calendar = NULL)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). data numeric matrix data.frame count data (absolute frequencies) predict event accumulation dates. ... arguments passed internal methods. margin numeric vector giving subscripts prediction applied : 1 indicates rows, 2 indicates columns. level length-one numeric vector giving confidence level. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"predict_event() returns data.frame. predict_accumulation() returns data.frame.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"Bellanger, L. & Husi, P. (2013). Mesurer et mod\u00e9liser le temps inscrit dans la mati\u00e8re \u00e0 partir d'une source mat\u00e9rielle : la c\u00e9ramique m\u00e9di\u00e9vale. Mesure et Histoire M\u00e9di\u00e9vale. Histoire ancienne et m\u00e9di\u00e9vale. Paris: Publication de la Sorbonne, p. 119-134. Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Bellanger, L., Tomassone, R. & Husi, P. (2008). Statistical Approach Dating Archaeological Contexts. Journal Data Science, 6, 135-154. Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes arch\u00e9ologiques. Revue de Statistique Appliqu\u00e9e, 54(2), 65-81. Bellanger, L., Husi, P. & Tomassone, R. (2006). Statistical Aspects Pottery Quantification Dating Archaeological Contexts. Archaeometry, 48(1), 169-183. doi:10.1111/j.1475-4754.2006.00249.x .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/predict_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Event and Accumulation Dates \u2014 predict_event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages \u2014 reexports","title":"Objects exported from other packages \u2014 reexports","text":"objects imported packages. Follow links see documentation. aion AD, BC, BCE, BP, CE, b2k, calendar, start, start, time, year_axis arkhe bootstrap, jackknife, remove_NA, remove_zero, replace_NA, replace_zero","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample Event Dates \u2014 resample_event","title":"Resample Event Dates \u2014 resample_event","text":"bootstrap() generate bootstrap estimations event. jackknife() generate jackknife estimations event.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample Event Dates \u2014 resample_event","text":"","code":"# S4 method for EventDate jackknife(object, level = 0.95, progress = getOption(\"kairos.progress\"), ...)  # S4 method for EventDate bootstrap(   object,   level = 0.95,   probs = c(0.05, 0.95),   n = 1000,   progress = getOption(\"kairos.progress\"),   ... )","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample Event Dates \u2014 resample_event","text":"object EventDate object (typically returned event()). level length-one numeric vector giving confidence level. progress logical scalar: progress bar displayed? ... arguments passed internal methods. probs numeric vector probabilities values \\([0,1]\\). n non-negative integer specifying number bootstrap replications.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample Event Dates \u2014 resample_event","text":"data.frame.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Resample Event Dates \u2014 resample_event","text":"jackknife() used, one type/fabric removed time statistics recalculated. way, one can assess whether certain type/fabric substantial influence date estimate. three columns data.frame returned, giving results resampling procedure (jackknifing fabrics) assemblage (rows) following columns: mean jackknife mean (event date). bias jackknife estimate bias. error standard error predicted means. bootstrap() used, large number new bootstrap assemblages created, sample size, resampling original assemblage replacement. , examination bootstrap statistics makes possible pinpoint assemblages require investigation. five columns data.frame returned, giving bootstrap distribution statistics replicated assemblage (rows) following columns: min Minimum value. mean Mean value (event date). max Maximum value. Q5 Sample quantile 0.05 probability. Q95 Sample quantile 0.95 probability.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample Event Dates \u2014 resample_event","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample Mean Ceramic Dates \u2014 resample_mcd","title":"Resample Mean Ceramic Dates \u2014 resample_mcd","text":"bootstrap() generate bootstrap estimations MCD. jackknife() generate jackknife estimations MCD.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample Mean Ceramic Dates \u2014 resample_mcd","text":"","code":"# S4 method for MeanDate bootstrap(object, n = 1000, f = NULL, calendar = getOption(\"kairos.calendar\"))  # S4 method for MeanDate jackknife(object, f = NULL, calendar = getOption(\"kairos.calendar\"))  # S4 method for MeanDate simulate(object, nsim = 1000)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample Mean Ceramic Dates \u2014 resample_mcd","text":"object MeanDate object (typically returned mcd()). n non-negative integer specifying number bootstrap replications. f function takes single numeric vector (result resampling procedure) argument. calendar TimeScale object specifying target calendar (see calendar()). nsim non-negative integer specifying number simulations.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample Mean Ceramic Dates \u2014 resample_mcd","text":"f NULL, bootstrap() jackknife() return data.frame following elements (else, returns result f applied n resampled values) : original observed value. mean bootstrap/jackknife estimate mean. bias bootstrap/jackknife estimate bias. error boostrap/jackknife estimate standard erro.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/resample_mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample Mean Ceramic Dates \u2014 resample_mcd","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate of Change \u2014 roc","title":"Rate of Change \u2014 roc","text":"Computes rate change aoristic analysis.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate of Change \u2014 roc","text":"","code":"roc(object, ...)  # S4 method for AoristicSum roc(object, n = 100)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate of Change \u2014 roc","text":"object AoristicSum object. ... Currently used. n non-negative integer giving number replications (see details).","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate of Change \u2014 roc","text":"RateOfChange object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rate of Change \u2014 roc","text":"Baxter, M. J. & Cool, H. E. M. (2016). Reinventing Wheel? Modelling Temporal Uncertainty Applications Brooch Distributions Roman Britain. Journal Archaeological Science, 66: 120-27. doi:10.1016/j.jas.2015.12.007 . Crema, E. R. (2012). Modelling Temporal Uncertainty Archaeological Analysis. Journal Archaeological Method Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rate of Change \u2014 roc","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/roc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rate of Change \u2014 roc","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Correspondence Analysis-Based Seriation \u2014 seriate_average","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"Correspondence Analysis-Based Seriation","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"","code":"seriate_average(object, ...)  # S4 method for data.frame seriate_average(object, margin = c(1, 2), axes = 1, ...)  # S4 method for matrix seriate_average(object, margin = c(1, 2), axes = 1, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns, c(2, 1) indicates columns rows. axes integer vector giving subscripts CA axes used.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"AveragePermutationOrder object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"Correspondence analysis (CA) effective method seriation archaeological assemblages. order rows columns given coordinates along one dimension CA space, assumed account temporal variation. direction temporal change within correspondence analysis space arbitrary: additional information needed determine actual order time.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"Ihm, P. (2005). Contribution History Seriation Archaeology. C. Weihs & W. Gaul (Eds.), Classification: Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. doi:10.1007/3-540-28084-7_34 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_average.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correspondence Analysis-Based Seriation \u2014 seriate_average","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #> <RankPermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Reciprocal Ranking Seriation \u2014 seriate_rank","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"Reciprocal Ranking Seriation","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"","code":"seriate_rank(object, ...)  # S4 method for data.frame seriate_rank(object, EPPM = FALSE, margin = c(1, 2), stop = 100)  # S4 method for matrix seriate_rank(object, EPPM = FALSE, margin = c(1, 2), stop = 100)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. EPPM logical scalar: seriation computed EPPM instead raw data? margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns, c(2, 1) indicates columns rows. stop integer giving stopping rule (.e. maximum number iterations) avoid infinite loop.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"RankPermutationOrder object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"procedure iteratively rearrange rows /columns according weighted rank data matrix convergence. Note procedure enter infinite loop. convergence reached maximum number iterations, stops warning.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"Desachy, B. (2004). Le s\u00e9riographe EPPM: un outil informatis\u00e9 de s\u00e9riation graphique pour tableaux de comptages. Revue arch\u00e9ologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 . Dunnell, R. C. (1970). Seriation Method Evaluation. American Antiquity, 35(03), 305-319. doi:10.2307/278341 . Ihm, P. (2005). Contribution History Seriation Archaeology. C. Weihs & W. Gaul (Eds.), Classification: Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. doi:10.1007/3-540-28084-7_34 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reciprocal Ranking Seriation \u2014 seriate_rank","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #> <RankPermutationOrder> #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":null,"dir":"Reference","previous_headings":"","what":"Refine CA-based Seriation \u2014 seriate_refine","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"Refine CA-based Seriation","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"","code":"seriate_refine(object, ...)  # S4 method for AveragePermutationOrder seriate_refine(object, cutoff, margin = 1, axes = 1, n = 30, ...)  # S4 method for BootstrapCA seriate_refine(object, cutoff, margin = 1, axes = 1, ...)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"object PermutationOrder object (typically returned seriate_average()). ... arguments passed internal methods. cutoff function takes numeric vector argument returns single numeric value (see ). margin length-one numeric vector giving subscripts refinement applied : 1 indicates rows, 2 indicates columns. axes integer vector giving subscripts CA axes used. n non-negative integer giving number bootstrap replications.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"RefinePermutationOrder object.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"seriate_refine() allows identify samples subject sampling error samples underlying structural relationships might influencing ordering along CA space. relies partial bootstrap approach CA-based seriation sample replicated n times. maximum dimension length convex hull around sample point cloud allows remove samples given cutoff value. According Peebles Schachner (2012), \"[] point removal procedure [results ] reduced dataset position individuals within CA highly stable produces ordering consistent assumptions frequency seriation.\"","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"Peeples, M. ., & Schachner, G. (2012). Refining correspondence analysis-based ceramic seriation regional data sets. Journal Archaeological Science, 39(8), 2818-2827. doi:10.1016/j.jas.2012.04.040 .","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/seriate_refine.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Refine CA-based Seriation \u2014 seriate_refine","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/series.html","id":null,"dir":"Reference","previous_headings":"","what":"Sampling Times \u2014 series","title":"Sampling Times \u2014 series","text":"Get times time series sampled.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sampling Times \u2014 series","text":"","code":"# S4 method for EventDate time(x, calendar = NULL)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sampling Times \u2014 series","text":"x R object. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sampling Times \u2014 series","text":"numeric vector.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/series.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sampling Times \u2014 series","text":"N. Frerebeau","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object \u2014 subset","title":"Extract or Replace Parts of an Object \u2014 subset","text":"Operators acting objects extract replace parts.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object \u2014 subset","text":"","code":"# S4 method for MeanDate [(x, i, j, k, drop = FALSE)  # S4 method for IncrementTest [(x, i, j, k, drop = FALSE)  # S4 method for PermutationOrder,ANY,missing [[(x, i)","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object \u2014 subset","text":"x object extract element(s) replace element(s). , j, k Indices specifying elements extract replace. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement.","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object \u2014 subset","text":"subsetted object.","code":"","package":"kairos"},{"path":[],"package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object \u2014 subset","text":"N. Frerebeau","code":"","package":"kairos"},{"path":[],"package":"tekton"},{"path":"https://packages.tesselle.org/tekton/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"pkgdown Template for tesselle Packages","text":"tekton provides custom pkgdown template tesselle packages. Please don\u2019t use package.","code":"","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"pkgdown Template for tesselle Packages","text":"can install latest version tekton repository :","code":"install.packages(\"tekton\", repos = \"https://tesselle.r-universe.dev\")","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"pkgdown Template for tesselle Packages","text":"Make sure _pkgdown.yaml contains: work properly, tekton requires assets templates inst/pkgdown/assets/. , set default_assets: false _pkgdown.yaml.","code":"template:   package: tekton","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/reference/test.html","id":null,"dir":"Reference","previous_headings":"","what":"Test Function \u2014 test","title":"Test Function \u2014 test","text":"Test Function","code":"","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/reference/test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test Function \u2014 test","text":"","code":"test(x = 1)","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/reference/test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test Function \u2014 test","text":"x value.","code":"","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/reference/test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test Function \u2014 test","text":"NULL","code":"","package":"tekton"},{"path":"https://packages.tesselle.org/tekton/reference/test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test Function \u2014 test","text":"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor reprehenderit voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt culpa qui officia deserunt mollit anim id est laborum.","code":"","package":"tekton"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Tools for Cleaning Rectangular Data","text":"dependency-free collection simple functions cleaning rectangular data. package allows detect, count replace values discard rows/columns using predicate function. addition, provides tools check conditions return informative error messages.","code":"To cite arkhe in publications use:    Frerebeau N (2023). _arkhe: Tools for Cleaning Rectangular Data_.   Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.3526659 <https://doi.org/10.5281/zenodo.3526659>,   R package version 1.3.0, <https://packages.tesselle.org/arkhe/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{arkhe: Tools for Cleaning Rectangular Data}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.3.0},     url = {https://packages.tesselle.org/arkhe/},     doi = {10.5281/zenodo.3526659},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Tools for Cleaning Rectangular Data","text":"can install released version arkhe CRAN : development version GitHub :","code":"install.packages(\"arkhe\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/arkhe\")","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Tools for Cleaning Rectangular Data","text":"","code":"## Load the package library(arkhe)  ## Create a matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    5    8   NA    3    5 #> [2,]    9    7    8    4    2 #> [3,]    4   10    7   NA    2 #> [4,]   NA    5    5    4    3 #> [5,]    7    9    9    8    1  ## Count missing values in rows count(X, f = is.na, margin = 1) #> [1] 1 0 1 1 0 ## Count non-missing values in columns count(X, f = is.na, margin = 2, negate = TRUE) #> V1 V2 V3 V4 V5  #>  4  5  4  4  5  ## Find row with NA detect(X, f = is.na, margin = 1) #> [1]  TRUE FALSE  TRUE  TRUE FALSE ## Find column without any NA detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE) #>    V1    V2    V3    V4    V5  #> FALSE  TRUE FALSE FALSE  TRUE  ## Remove row with any NA discard(X, f = is.na, margin = 1, all = FALSE) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    9    7    8    4    2 #> [2,]    7    9    9    8    1 ## Remove column with any NA discard(X, f = is.na, margin = 2, all = FALSE) #>      [,1] [,2] #> [1,]    8    5 #> [2,]    7    2 #> [3,]   10    2 #> [4,]    5    3 #> [5,]    9    1  ## Replace NA with zeros replace_NA(X, value = 0) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    5    8    0    3    5 #> [2,]    9    7    8    4    2 #> [3,]    4   10    7    0    2 #> [4,]    0    5    5    4    3 #> [5,]    7    9    9    8    1","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Tools for Cleaning Rectangular Data","text":"Please note arkhe project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":[],"package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Row Names to an Explicit Column \u2014 append","title":"Convert Row Names to an Explicit Column \u2014 append","text":"Convert Row Names Explicit Column","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Row Names to an Explicit Column \u2014 append","text":"","code":"append_rownames(x, ...)  # S4 method for data.frame append_rownames(x, after = 0, remove = TRUE, var = \"rownames\")","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Row Names to an Explicit Column \u2014 append","text":"x data.frame. ... Currently used. length-one numeric vector specifying subscript, row names appended. remove logical scalar: row names removed? var character string giving name column use row names.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Row Names to an Explicit Column \u2014 append","text":"data.frame.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert Row Names to an Explicit Column \u2014 append","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/append.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert Row Names to an Explicit Column \u2014 append","text":"","code":"X <- data.frame(   x = 1:5,   y = 6:10,   z = LETTERS[1:5] )  ## Assign column to row names (Y <- assign_rownames(X, 3)) #>   x  y #> A 1  6 #> B 2  7 #> C 3  8 #> D 4  9 #> E 5 10  ## Append row names to data.frame (Z <- append_rownames(Y)) #>   rownames x  y #> 1        A 1  6 #> 2        B 2  7 #> 3        C 3  8 #> 4        D 4  9 #> 5        E 5 10","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/arkhe-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in arkhe \u2014 arkhe-deprecated","title":"Deprecated Functions in arkhe \u2014 arkhe-deprecated","text":"functions still work removed (defunct) next version.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/arkhe-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions in arkhe \u2014 arkhe-deprecated","text":"","code":"wide_to_long(from, ...)  to_long(from, ...)  # S4 method for matrix wide_to_long(from, factor = FALSE, reverse = FALSE)  # S4 method for matrix to_long(from, factor = FALSE, reverse = FALSE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/arkhe-package.html","id":null,"dir":"Reference","previous_headings":"","what":"arkhe: Tools for Cleaning Rectangular Data \u2014 arkhe-package","title":"arkhe: Tools for Cleaning Rectangular Data \u2014 arkhe-package","text":"dependency-free collection simple functions cleaning rectangular data. package allows detect, count replace values discard rows/columns using predicate function. addition, provides tools check conditions return informative error messages.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/arkhe-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"arkhe: Tools for Cleaning Rectangular Data \u2014 arkhe-package","text":"arkhe uses following options() configure behavior: arkhe.verbose: logical scalar. R report extra information progress? Defaults TRUE.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/arkhe-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"arkhe: Tools for Cleaning Rectangular Data \u2014 arkhe-package","text":"Full list authors contributors (alphabetic order): Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"Assign Specific Row/Column Column/Row Names","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"","code":"assign_colnames(x, ...)  assign_rownames(x, ...)  # S4 method for data.frame assign_rownames(x, column, remove = TRUE)  # S4 method for data.frame assign_colnames(x, row, remove = TRUE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"x data.frame. ... Currently used. column length-one numeric vector specifying column number become row names. remove logical scalar: specified row/column removed making column/row names? row length-one numeric vector specifying row number become column names.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"data.frame.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/assign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assign a Specific Row/Column to the Column/Row Names \u2014 assign","text":"","code":"X <- data.frame(   x = 1:5,   y = 6:10,   z = LETTERS[1:5] )  ## Assign column to row names (Y <- assign_rownames(X, 3)) #>   x  y #> A 1  6 #> B 2  7 #> C 3  8 #> D 4  9 #> E 5 10  ## Append row names to data.frame (Z <- append_rownames(Y)) #>   rownames x  y #> 1        A 1  6 #> 2        B 2  7 #> 3        C 3  8 #> 4        D 4  9 #> 5        E 5 10","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Estimation \u2014 bootstrap","title":"Bootstrap Estimation \u2014 bootstrap","text":"Samples randomly elements object replacement.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Estimation \u2014 bootstrap","text":"","code":"bootstrap(object, ...)  # S4 method for numeric bootstrap(object, do, n, ..., f = NULL)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Estimation \u2014 bootstrap","text":"object numeric vector. ... Extra arguments passed . function takes object argument returns single numeric value. n non-negative integer giving number bootstrap replications. f function takes single numeric vector (result ) argument.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Estimation \u2014 bootstrap","text":"f NULL (default), bootstrap() returns named numeric vector following elements: original observed value applied object. mean bootstrap estimate mean . bias bootstrap estimate bias . error bootstrap estimate standard error . f function, bootstrap() returns result f applied n values .","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Estimation \u2014 bootstrap","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Estimation \u2014 bootstrap","text":"","code":"x <- rnorm(20)  ## Bootstrap bootstrap(x, do = mean, n = 100) #>    original        mean        bias       error  #> -0.23001183 -0.26088732 -0.03087549  0.19544745   ## Estimate the 25th and 95th percentiles quant <- function(x) { quantile(x, probs = c(0.25, 0.75)) } bootstrap(x, n = 100, do = mean, f = quant) #>        25%        75%  #> -0.3575141 -0.1015618   ## Get the n bootstrap values bootstrap(x, n = 100, do = mean, f = function(x) { x }) #>   [1] -0.354575156 -0.264014359 -0.024722116  0.168383889 -0.308479383 #>   [6]  0.042123894 -0.631067882 -0.103311499 -0.217030853 -0.180843364 #>  [11] -0.377664859 -0.255918944  0.260098734 -0.039807122 -0.273346343 #>  [16] -0.097273971 -0.421726282 -0.339250807 -0.385834467 -0.082725113 #>  [21] -0.278828961 -0.314183617  0.111879988  0.259573743 -0.182563677 #>  [26] -0.222178437 -0.168244579 -0.319917168 -0.652910481 -0.626622912 #>  [31] -0.149500332 -0.114149030 -0.211087995 -0.035279463 -0.481243393 #>  [36] -0.228694443 -0.296699910 -0.347472668 -0.595869659 -0.123912074 #>  [41] -0.314146171 -0.242591941 -0.092909644 -0.450002215 -0.291444799 #>  [46] -0.246667862 -0.240226038 -0.349990699 -0.345395193 -0.116084354 #>  [51] -0.235571530 -0.210884887 -0.610726433  0.018464397 -0.018265597 #>  [56] -0.245874130 -0.156736947 -0.039486500  0.043348922 -0.495482369 #>  [61] -0.313652243 -0.244748120  0.063470300 -0.117959964 -0.052234695 #>  [66] -0.254617069 -0.280670352 -0.054931345 -0.484812617  0.035113663 #>  [71] -0.061796337 -0.016094557 -0.272161888 -0.528599567 -0.003423236 #>  [76] -0.593896436 -0.285145538 -0.399949034 -0.072200926 -0.110624087 #>  [81] -0.200153483  0.007328726 -0.349747032 -0.371607923 -0.233177528 #>  [86]  0.534766177 -0.052641721 -0.171065756 -0.090688354  0.105562894 #>  [91]  0.043238954 -0.562153775 -0.058901428 -0.349603978  0.004644821 #>  [96] -0.106330578 -0.328705521 -0.495705109 -0.295970657 -0.839429360  ## Jackknife jackknife(x, do = mean) # Sample mean #>   original       mean       bias      error  #> -0.2300118 -0.2300118  0.0000000  0.2154961   ## Get the leave-one-out values instead of summary jackknife(x, do = mean, f = function(x) { x }) #>  [1] -0.2260393 -0.1903113 -0.1957656 -0.2753305 -0.2263948 -0.2667864 #>  [7] -0.2142052 -0.2851663 -0.2555698 -0.1419977 -0.1139680 -0.2245960 #> [13] -0.1886281 -0.2639238 -0.2216209 -0.2753058 -0.1883000 -0.3161935 #> [19] -0.2863064 -0.2438270","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-attribute.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Object Attributes \u2014 check-attribute","title":"Check Object Attributes \u2014 check-attribute","text":"Check Object Attributes","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-attribute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Object Attributes \u2014 check-attribute","text":"","code":"assert_empty(x)  assert_filled(x)  assert_length(x, expected, empty = FALSE)  assert_lengths(x, expected)  assert_dimensions(x, expected)  assert_names(x, expected)  assert_dimnames(x, expected)  assert_rownames(x, expected)  assert_colnames(x, expected)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-attribute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Object Attributes \u2014 check-attribute","text":"x object checked. expected appropriate expected value. empty logical scalar: empty objects ignored?","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-attribute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Object Attributes \u2014 check-attribute","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-attribute.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Object Attributes \u2014 check-attribute","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-data.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Data \u2014 check-data","title":"Check Data \u2014 check-data","text":"assert_missing() assert_infinite() check object contains missing (NA, NaN) infinite (Inf) value. assert_unique() checks object contains duplicated elements.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Data \u2014 check-data","text":"","code":"assert_missing(x)  assert_infinite(x)  assert_unique(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Data \u2014 check-data","text":"x object checked.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Data \u2014 check-data","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Data \u2014 check-data","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Matrix \u2014 check-matrix","title":"Check Matrix \u2014 check-matrix","text":"Check Matrix","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Matrix \u2014 check-matrix","text":"","code":"assert_symmetric(x)  assert_square(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Matrix \u2014 check-matrix","text":"x matrix checked.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Matrix \u2014 check-matrix","text":"Throw error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-matrix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Matrix \u2014 check-matrix","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-comparison.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Numeric Relations \u2014 check-numeric-comparison","title":"Check Numeric Relations \u2014 check-numeric-comparison","text":"Check Numeric Relations","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-comparison.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Numeric Relations \u2014 check-numeric-comparison","text":"","code":"assert_lower(x, y, ...)  assert_greater(x, y, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-comparison.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Numeric Relations \u2014 check-numeric-comparison","text":"x, y numeric object checked. ... Extra parameters passed internal methods.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-comparison.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Numeric Relations \u2014 check-numeric-comparison","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-comparison.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Numeric Relations \u2014 check-numeric-comparison","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-trend.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Numeric Trend \u2014 check-numeric-trend","title":"Check Numeric Trend \u2014 check-numeric-trend","text":"Check Numeric Trend","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-trend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Numeric Trend \u2014 check-numeric-trend","text":"","code":"assert_constant(x, ...)  assert_decreasing(x, ...)  assert_increasing(x, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-trend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Numeric Trend \u2014 check-numeric-trend","text":"x numeric object checked. ... Extra parameters passed internal methods.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-trend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Numeric Trend \u2014 check-numeric-trend","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric-trend.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Numeric Trend \u2014 check-numeric-trend","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Numeric Values \u2014 check-numeric","title":"Check Numeric Values \u2014 check-numeric","text":"Check Numeric Values","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Numeric Values \u2014 check-numeric","text":"","code":"assert_count(x, ...)  assert_whole(x, ...)  assert_positive(x, ...)  assert_negative(x, ...)  assert_odd(x, ...)  assert_even(x, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Numeric Values \u2014 check-numeric","text":"x numeric object checked. ... Extra parameters passed internal methods.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Numeric Values \u2014 check-numeric","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-numeric.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Numeric Values \u2014 check-numeric","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-type.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Data Types \u2014 check-type","title":"Check Data Types \u2014 check-type","text":"Check Data Types","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Data Types \u2014 check-type","text":"","code":"assert_type(x, expected)  assert_scalar(x, expected)  assert_function(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Data Types \u2014 check-type","text":"x object checked. expected character string specifying expected type. must one \"list\", \"atomic\", \"vector\", \"numeric\", \"integer\", \"double\", \"character\" \"logical\".","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-type.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Data Types \u2014 check-type","text":"Throws error, , returns x invisibly otherwise.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check-type.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check Data Types \u2014 check-type","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class Diagnostic \u2014 check_class","title":"Class Diagnostic \u2014 check_class","text":"Class Diagnostic","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Class Diagnostic \u2014 check_class","text":"","code":"check_class(object, conditions)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class Diagnostic \u2014 check_class","text":"object object error messages related. conditions list condition messages.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Class Diagnostic \u2014 check_class","text":"Throw error conditions non-zero length, invisibly returns TRUE .","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/check_class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Class Diagnostic \u2014 check_class","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Empty Rows/Columns \u2014 compact","title":"Remove Empty Rows/Columns \u2014 compact","text":"Removes empty rows/columns array-like object using predicate function.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Empty Rows/Columns \u2014 compact","text":"","code":"compact(x, ...)  compact_cols(x, ...)  compact_rows(x, ...)  # S4 method for ANY compact(x, margin = 1)  # S4 method for ANY compact_cols(x)  # S4 method for ANY compact_rows(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Empty Rows/Columns \u2014 compact","text":"x R object (matrix data.frame). ... Currently used. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns).","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Remove Empty Rows/Columns \u2014 compact","text":"row/column empty contains NA, zeros (type numeric) zero length character strings (type character).","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Remove Empty Rows/Columns \u2014 compact","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/compact.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remove Empty Rows/Columns \u2014 compact","text":"","code":"## Create a data.frame X <- data.frame(A = 0, B = 1:5, C = 6, D = \"\", F = letters[1:5]) X #>   A B C D F #> 1 0 1 6   a #> 2 0 2 6   b #> 3 0 3 6   c #> 4 0 4 6   d #> 5 0 5 6   e  ## Remove empty columns compact(X, margin = 2) #>   B C F #> 1 1 6 a #> 2 2 6 b #> 3 3 6 c #> 4 4 6 d #> 5 5 6 e","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/conditions.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditions \u2014 conditions","title":"Conditions \u2014 conditions","text":"throw_error() stops execution current expression executes error action. throw_warning() generates warning message. catch_conditions() catch_message() handles unusual conditions.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/conditions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditions \u2014 conditions","text":"","code":"throw_error(.subclass, message, call = NULL, ...)  throw_warning(.subclass, message, call = NULL, ...)  catch_message(expr)  catch_conditions(expr)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/conditions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditions \u2014 conditions","text":".subclass character string specifying class message returned. message character string specifying message returned. call call. ... Extra arguments. expr expression evaluated.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/conditions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Conditions \u2014 conditions","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"Computes Wald interval proportion desired level significance.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"","code":"confidence_binomial(object, ...)  # S4 method for numeric confidence_binomial(   object,   n,   level = 0.95,   method = \"wald\",   corrected = FALSE )","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"object numeric vector giving number success. ... Currently used. n length-one numeric vector giving number trials. level length-one numeric vector giving confidence level. Must single number \\(0\\) \\(1\\). method character string specifying method used. unambiguous substring can used. corrected logical scalar: continuity correction used? used method \"wald\".","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"length-two numeric vector giving lower upper confidence limits.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_binomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval for Binomial Proportions \u2014 confidence_binomial","text":"","code":"## Confidence interval for a mean x <- seq(from = -4, to = 4, by = 0.01) y <- dnorm(x)  confidence_mean(y, type = \"student\") #>     lower     upper  #> 0.1151118 0.1345606  confidence_mean(y, type = \"normal\") #>     lower     upper  #> 0.1151265 0.1345459   ## Confidence interval for a propotion confidence_binomial(118, n = 236) #>     lower     upper  #> 0.4362086 0.5637914   x <- c(35, 74, 22, 69) confidence_multinomial(x) #>           lower     upper #> [1,] 0.12234021 0.2276598 #> [2,] 0.30308797 0.4369120 #> [3,] 0.06663649 0.1533635 #> [4,] 0.27911853 0.4108815","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval for a Mean \u2014 confidence_mean","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"Computes confidence interval mean desired level significance.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"","code":"confidence_mean(object, ...)  # S4 method for numeric confidence_mean(object, level = 0.95, type = c(\"student\", \"normal\"))","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"object numeric vector. ... Currently used. level length-one numeric vector giving confidence level. Must single number \\(0\\) \\(1\\). type character string giving type confidence interval returned. must one \"student\" (default) \"normal\". unambiguous substring can given.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"length-two numeric vector giving lower upper confidence limits.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval for a Mean \u2014 confidence_mean","text":"","code":"## Confidence interval for a mean x <- seq(from = -4, to = 4, by = 0.01) y <- dnorm(x)  confidence_mean(y, type = \"student\") #>     lower     upper  #> 0.1151118 0.1345606  confidence_mean(y, type = \"normal\") #>     lower     upper  #> 0.1151265 0.1345459   ## Confidence interval for a propotion confidence_binomial(118, n = 236) #>     lower     upper  #> 0.4362086 0.5637914   x <- c(35, 74, 22, 69) confidence_multinomial(x) #>           lower     upper #> [1,] 0.12234021 0.2276598 #> [2,] 0.30308797 0.4369120 #> [3,] 0.06663649 0.1533635 #> [4,] 0.27911853 0.4108815","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"Computes Wald interval proportion desired level significance.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"","code":"confidence_multinomial(object, ...)  # S4 method for numeric confidence_multinomial(   object,   level = 0.95,   method = \"wald\",   corrected = FALSE )","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"object numeric vector positive integers giving number occurrences class. ... Currently used. level length-one numeric vector giving confidence level. Must single number \\(0\\) \\(1\\). method character string specifying method used. unambiguous substring can used. corrected logical scalar: continuity correction used? used method \"wald\".","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"two column numeric  matrix giving lower upper confidence limits.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/confidence_multinomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence Interval for Multinomial Proportions \u2014 confidence_multinomial","text":"","code":"## Confidence interval for a mean x <- seq(from = -4, to = 4, by = 0.01) y <- dnorm(x)  confidence_mean(y, type = \"student\") #>     lower     upper  #> 0.1151118 0.1345606  confidence_mean(y, type = \"normal\") #>     lower     upper  #> 0.1151265 0.1345459   ## Confidence interval for a propotion confidence_binomial(118, n = 236) #>     lower     upper  #> 0.4362086 0.5637914   x <- c(35, 74, 22, 69) confidence_multinomial(x) #>           lower     upper #> [1,] 0.12234021 0.2276598 #> [2,] 0.30308797 0.4369120 #> [3,] 0.06663649 0.1533635 #> [4,] 0.27911853 0.4108815","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Values Using a Predicate \u2014 count","title":"Count Values Using a Predicate \u2014 count","text":"Counts values rows/columns using predicate function.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Values Using a Predicate \u2014 count","text":"","code":"count(x, ...)  # S4 method for data.frame count(x, f, margin = 1, negate = FALSE, na.rm = FALSE, ...)  # S4 method for matrix count(x, f, margin = 1, negate = FALSE, na.rm = FALSE, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Values Using a Predicate \u2014 count","text":"x R object (matrix data.frame). ... arguments passed f. f predicate function. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). negate logical scalar: negation f used instead f? na.rm logical scalar: NA values stripped computation proceeds?","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Values Using a Predicate \u2014 count","text":"numeric vector.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Count Values Using a Predicate \u2014 count","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Values Using a Predicate \u2014 count","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    3    2    6    6    7 #> [2,]    9    4    4   10    6 #> [3,]   10   10   10   NA   NA #> [4,]    1    8    4    8    7 #> [5,]    4    2   NA    9    5  ## Count missing values in rows count(X, f = is.na, margin = 1) #> [1] 0 0 2 0 1 ## Count non-missing values in columns count(X, f = is.na, margin = 2, negate = TRUE) #> V1 V2 V3 V4 V5  #>  5  5  4  4  4","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":null,"dir":"Reference","previous_headings":"","what":"Find Rows/Columns Using a Predicate \u2014 detect","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"Finds rows/columns array-like object using predicate function.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"","code":"detect(x, ...)  # S4 method for ANY detect(x, f, margin = 1, negate = FALSE, all = FALSE, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"x R object (matrix data.frame). ... arguments passed f. f predicate function. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). negate logical scalar: negation f used instead f? logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"logical vector.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/detect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find Rows/Columns Using a Predicate \u2014 detect","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    6   10    1    4    3 #> [2,]    1    7    1    9    3 #> [3,]    1    1    3    9    1 #> [4,]    7    8    5    1    5 #> [5,]   NA   NA    4   NA    9  ## Find row with NA detect(X, f = is.na, margin = 1) #> [1] FALSE FALSE FALSE FALSE  TRUE ## Find column without any NA detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE) #>    V1    V2    V3    V4    V5  #> FALSE FALSE  TRUE FALSE  TRUE","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/discard.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Rows/Columns Using a Predicate \u2014 discard","title":"Remove Rows/Columns Using a Predicate \u2014 discard","text":"Removes rows/columns array-like object using predicate function.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/discard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Rows/Columns Using a Predicate \u2014 discard","text":"","code":"discard(x, ...)  discard_cols(x, ...)  discard_rows(x, ...)  # S4 method for ANY discard(   x,   f,   margin = 1,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )  # S4 method for ANY discard_rows(   x,   f,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )  # S4 method for ANY discard_cols(   x,   f,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/discard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Rows/Columns Using a Predicate \u2014 discard","text":"x R object (matrix data.frame). ... arguments passed f. f predicate function. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). negate logical scalar: negation f used instead f? logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. verbose logical scalar: R report extra information progress?","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/discard.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Remove Rows/Columns Using a Predicate \u2014 discard","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/discard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remove Rows/Columns Using a Predicate \u2014 discard","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    3    6   NA    9    3 #> [2,]    2    5    5    8   NA #> [3,]    7    3    4    4    9 #> [4,]    1    4    5   NA    2 #> [5,]    8    5    1   10    5  ## Remove row with any NA discard(X, f = is.na, margin = 1, all = FALSE) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    7    3    4    4    9 #> [2,]    8    5    1   10    5 ## Remove column with any NA discard(X, f = is.na, margin = 2, all = FALSE) #>      [,1] [,2] #> [1,]    3    6 #> [2,]    2    5 #> [3,]    7    3 #> [4,]    1    4 #> [5,]    8    5","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/empty.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for Working With Empty String \u2014 empty","title":"Tools for Working With Empty String \u2014 empty","text":"remove_empty() remove rows/columns contain empty strings. replace_empty() replaces empty strings.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/empty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for Working With Empty String \u2014 empty","text":"","code":"remove_empty(x, ...)  replace_empty(x, ...)  # S4 method for ANY remove_empty(x, margin = 1, all = FALSE, na.rm = FALSE)  # S4 method for matrix replace_empty(x, value)  # S4 method for data.frame replace_empty(x, value)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/empty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for Working With Empty String \u2014 empty","text":"x R object (matrix data.frame). ... Currently used. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. na.rm logical scalar: NA values stripped computation proceeds? value possible replacement value.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/empty.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tools for Working With Empty String \u2014 empty","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/empty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for Working With Empty String \u2014 empty","text":"","code":"## Create a data matrix X <- matrix(sample(LETTERS, 25, TRUE), nrow = 5, ncol = 5)  ## Add empty string k <- sample(1:25, 3, FALSE) X[k] <- \"\" X #>      [,1] [,2] [,3] [,4] [,5] #> [1,] \"G\"  \"W\"  \"\"   \"\"   \"U\"  #> [2,] \"Y\"  \"X\"  \"U\"  \"V\"  \"N\"  #> [3,] \"X\"  \"X\"  \"I\"  \"E\"  \"D\"  #> [4,] \"T\"  \"Y\"  \"V\"  \"O\"  \"U\"  #> [5,] \"B\"  \"\"   \"A\"  \"Z\"  \"D\"   ## Remove rows with empty strings remove_empty(X, margin = 1) #>      [,1] [,2] [,3] [,4] [,5] #> [1,] \"Y\"  \"X\"  \"U\"  \"V\"  \"N\"  #> [2,] \"X\"  \"X\"  \"I\"  \"E\"  \"D\"  #> [3,] \"T\"  \"Y\"  \"V\"  \"O\"  \"U\"   ## Replace empty strings replace_empty(X, value = \"XXX\") #>      [,1] [,2]  [,3]  [,4]  [,5] #> [1,] \"G\"  \"W\"   \"XXX\" \"XXX\" \"U\"  #> [2,] \"Y\"  \"X\"   \"U\"   \"V\"   \"N\"  #> [3,] \"X\"  \"X\"   \"I\"   \"E\"   \"D\"  #> [4,] \"T\"  \"Y\"   \"V\"   \"O\"   \"U\"  #> [5,] \"B\"  \"XXX\" \"A\"   \"Z\"   \"D\"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/infinite.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for Working With Infinite Values \u2014 infinite","title":"Tools for Working With Infinite Values \u2014 infinite","text":"remove_Inf() remove rows/columns contain infinite values. replace_Inf replaces infinite values values.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/infinite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for Working With Infinite Values \u2014 infinite","text":"","code":"remove_Inf(x, ...)  replace_Inf(x, ...)  # S4 method for ANY remove_Inf(x, margin = 1, all = FALSE)  # S4 method for matrix replace_Inf(x, value = 0)  # S4 method for data.frame replace_Inf(x, value = 0)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/infinite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for Working With Infinite Values \u2014 infinite","text":"x R object (matrix data.frame). ... Currently used. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. value possible replacement value.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/infinite.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tools for Working With Infinite Values \u2014 infinite","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/infinite.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for Working With Infinite Values \u2014 infinite","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add Inf k <- sample(1:25, 3, FALSE) X[k] <- Inf X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    1    3  Inf    4  Inf #> [2,]    8    5    7    9    1 #> [3,]    6    2    5    6    8 #> [4,]    7    7    5  Inf    5 #> [5,]    7    8    4    1    3  ## Remove rows with Inf remove_Inf(X, margin = 1) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    8    5    7    9    1 #> [2,]    6    2    5    6    8 #> [3,]    7    8    4    1    3  ## Replace Inf with zeros replace_Inf(X, value = 0) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    1    3    0    4    0 #> [2,]    8    5    7    9    1 #> [3,]    6    2    5    6    8 #> [4,]    7    7    5    0    5 #> [5,]    7    8    4    1    3","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":null,"dir":"Reference","previous_headings":"","what":"Bayesian Credible Interval \u2014 interval_credible","title":"Bayesian Credible Interval \u2014 interval_credible","text":"Computes shortest credible interval within unobserved parameter value falls particular probability.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bayesian Credible Interval \u2014 interval_credible","text":"","code":"interval_credible(x, ...)  # S4 method for numeric interval_credible(x, level = 0.95)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bayesian Credible Interval \u2014 interval_credible","text":"x numeric vector. ... Currently used. level length-one numeric vector giving confidence level.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bayesian Credible Interval \u2014 interval_credible","text":"three-columns numeric  matrix giving lower upper boundaries credible interval associated probability.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bayesian Credible Interval \u2014 interval_credible","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_credible.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bayesian Credible Interval \u2014 interval_credible","text":"","code":"## HDR of the Old Faithful eruption times interval_hdr(faithful$eruptions) #>         start      end    p #> [1,] 1.307160 2.837942 0.33 #> [2,] 3.150567 5.295819 0.62","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":null,"dir":"Reference","previous_headings":"","what":"Highest Density Regions \u2014 interval_hdr","title":"Highest Density Regions \u2014 interval_hdr","text":"Highest Density Regions","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Highest Density Regions \u2014 interval_hdr","text":"","code":"interval_hdr(x, y, ...)  # S4 method for numeric,numeric interval_hdr(x, y, level = 0.954)  # S4 method for numeric,missing interval_hdr(x, level = 0.954, ...)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Highest Density Regions \u2014 interval_hdr","text":"x numeric vector giving coordinates points density estimated. y numeric vector giving estimated density values. y missing x numeric vector, density estimates computed x. ... arguments passed stats::density(). level length-one numeric vector giving confidence level.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Highest Density Regions \u2014 interval_hdr","text":"three-columns numeric  matrix giving lower upper boundaries HPD interval associated probabilities.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Highest Density Regions \u2014 interval_hdr","text":"Hyndman, R. J. (1996). Computing graphing highest density regions. American Statistician, 50: 120-126. doi:10.2307/2684423 .","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Highest Density Regions \u2014 interval_hdr","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Highest Density Regions \u2014 interval_hdr","text":"","code":"## HDR of the Old Faithful eruption times interval_hdr(faithful$eruptions) #>         start      end    p #> [1,] 1.307160 2.837942 0.33 #> [2,] 3.150567 5.295819 0.62","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":null,"dir":"Reference","previous_headings":"","what":"Jackknife Estimation \u2014 jackknife","title":"Jackknife Estimation \u2014 jackknife","text":"Jackknife Estimation","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Jackknife Estimation \u2014 jackknife","text":"","code":"jackknife(object, ...)  # S4 method for numeric jackknife(object, do, ..., f = NULL)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Jackknife Estimation \u2014 jackknife","text":"object numeric vector. ... Extra arguments passed . function takes object argument returns single numeric value. f function takes single numeric vector (leave-one-values ) argument.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Jackknife Estimation \u2014 jackknife","text":"f NULL (default), jackknife() returns named numeric vector following elements: original observed value applied object. mean jackknife estimate mean . bias jackknife estimate bias . error jackknife estimate standard error . f function, jackknife() returns result f applied leave-one-values .","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Jackknife Estimation \u2014 jackknife","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/jackknife.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Jackknife Estimation \u2014 jackknife","text":"","code":"x <- rnorm(20)  ## Bootstrap bootstrap(x, do = mean, n = 100) #>    original        mean        bias       error  #>  0.14565989  0.13104524 -0.01461464  0.23848682   ## Estimate the 25th and 95th percentiles quant <- function(x) { quantile(x, probs = c(0.25, 0.75)) } bootstrap(x, n = 100, do = mean, f = quant) #>          25%          75%  #> -0.002251734  0.279260509   ## Get the n bootstrap values bootstrap(x, n = 100, do = mean, f = function(x) { x }) #>   [1]  0.43039038  0.20569337 -0.12346231  0.07890700  0.08198185  0.40706136 #>   [7] -0.10356961  0.09922693  0.05686612  0.26156990  0.28905648  0.02424760 #>  [13]  0.08009081  0.30724032  0.66974867  0.44542265  0.02041284 -0.08116079 #>  [19]  0.01480548  0.11409536  0.60173106  0.09687759 -0.12886483 -0.20829643 #>  [25]  0.37881994  0.31910581 -0.05900876  0.07250347  0.33729817 -0.16638088 #>  [31]  0.32932694  0.10187384 -0.19635637  0.67554312  0.12949750  0.05268718 #>  [37] -0.07856544  0.23526846  0.53351332  0.14306191  0.15852229 -0.12736808 #>  [43]  0.07355868  0.03760085 -0.15446830  0.13021126  0.36332861  0.12115010 #>  [49] -0.14216211  0.18096342  0.15610047  0.80843649 -0.07059272  0.52369545 #>  [55]  0.12995564 -0.11061855  0.15039954  0.02361363  0.10957063 -0.13808787 #>  [61]  0.09491213  0.06801922  0.25202480 -0.08957378  0.27567313  0.57898542 #>  [67]  0.10575104  0.39636880  0.21606101  0.18260413 -0.28894382  0.23905615 #>  [73] -0.25685970  0.09339155  0.48478190  0.14519122  0.34178327 -0.11283620 #>  [79]  0.16934627 -0.06703038  0.46523253  0.25913102  0.26237423  0.09554590 #>  [85]  0.28927284  0.29229987  0.19989578  0.01875762  0.07249745  0.04099358 #>  [91]  0.09222536  0.20267609  0.06144836  0.24978342 -0.20200934  0.37718515 #>  [97]  0.34187158  0.37971883 -0.13657618  0.31474599  ## Jackknife jackknife(x, do = mean) # Sample mean #>  original      mean      bias     error  #> 0.1456599 0.1456599 0.0000000 0.2394428   ## Get the leave-one-out values instead of summary jackknife(x, do = mean, f = function(x) { x }) #>  [1] 0.19152082 0.19342042 0.10769904 0.14875192 0.25618400 0.10601127 #>  [7] 0.11938772 0.20343002 0.10146239 0.08518697 0.10419447 0.15732785 #> [13] 0.12970218 0.08063737 0.11548916 0.08222062 0.18823202 0.27572430 #> [19] 0.11352506 0.15309013","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/keep.html","id":null,"dir":"Reference","previous_headings":"","what":"Keep Rows/Columns Using a Predicate \u2014 keep","title":"Keep Rows/Columns Using a Predicate \u2014 keep","text":"Keeps rows/columns array-like object using predicate function.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/keep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Keep Rows/Columns Using a Predicate \u2014 keep","text":"","code":"keep(x, ...)  keep_cols(x, ...)  keep_rows(x, ...)  # S4 method for ANY keep(   x,   f,   margin = 1,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )  # S4 method for ANY keep_rows(   x,   f,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )  # S4 method for ANY keep_cols(   x,   f,   negate = FALSE,   all = FALSE,   verbose = getOption(\"arkhe.verbose\"),   ... )","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/keep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Keep Rows/Columns Using a Predicate \u2014 keep","text":"x R object (matrix data.frame). ... arguments passed f. f predicate function. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). negate logical scalar: negation f used instead f? logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. verbose logical scalar: R report extra information progress?","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/keep.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Keep Rows/Columns Using a Predicate \u2014 keep","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/keep.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Keep Rows/Columns Using a Predicate \u2014 keep","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    2    5   10    6    2 #> [2,]   NA    6    2    2   NA #> [3,]    6    3    4    8    4 #> [4,]    3    5    3    6    2 #> [5,]    3   NA   10    9   10  ## Keep row without any NA keep(X, f = is.na, margin = 1, negate = TRUE, all = TRUE) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    2    5   10    6    2 #> [2,]    6    3    4    8    4 #> [3,]    3    5    3    6    2 ## Keep row without any NA keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE) #>      [,1] [,2] #> [1,]   10    6 #> [2,]    2    2 #> [3,]    4    8 #> [4,]    3    6 #> [5,]   10    9","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_gcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Greatest Common Divisor \u2014 math_gcd","title":"Greatest Common Divisor \u2014 math_gcd","text":"Computes greatest common divisor (GCD) two integer using Euclidean algorithm.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_gcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Greatest Common Divisor \u2014 math_gcd","text":"","code":"math_gcd(x, y)  # S4 method for numeric,numeric math_gcd(x, y)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_gcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Greatest Common Divisor \u2014 math_gcd","text":"x, y numeric vector.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_gcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Greatest Common Divisor \u2014 math_gcd","text":"numeric vector.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_gcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Greatest Common Divisor \u2014 math_gcd","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_lcm.html","id":null,"dir":"Reference","previous_headings":"","what":"Least Common Multiple \u2014 math_lcm","title":"Least Common Multiple \u2014 math_lcm","text":"Computes lowest common multiple denominators set fractions.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_lcm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Least Common Multiple \u2014 math_lcm","text":"","code":"math_lcm(x, y)  # S4 method for numeric,numeric math_lcm(x, y)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_lcm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Least Common Multiple \u2014 math_lcm","text":"x, y numeric vector.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_lcm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Least Common Multiple \u2014 math_lcm","text":"numeric vector.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/math_lcm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Least Common Multiple \u2014 math_lcm","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/missing.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for Working With Missing Values \u2014 missing","title":"Tools for Working With Missing Values \u2014 missing","text":"remove_NA() remove rows/columns contain missing values. replace_NA replaces missing values values.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/missing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for Working With Missing Values \u2014 missing","text":"","code":"remove_NA(x, ...)  replace_NA(x, ...)  # S4 method for ANY remove_NA(x, margin = 1, all = FALSE)  # S4 method for matrix replace_NA(x, value = 0)  # S4 method for data.frame replace_NA(x, value = 0)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/missing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for Working With Missing Values \u2014 missing","text":"x R object (matrix data.frame). ... Currently used. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. value possible replacement value.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/missing.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tools for Working With Missing Values \u2014 missing","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/missing.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for Working With Missing Values \u2014 missing","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add NA k <- sample(1:25, 3, FALSE) X[k] <- NA X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]   NA    7    7    3    1 #> [2,]    7   NA    9    4    3 #> [3,]    9    6    7    3    5 #> [4,]    4    8    3    9    4 #> [5,]    1   NA   10    7    2  ## Remove rows with NA remove_NA(X, margin = 1) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    9    6    7    3    5 #> [2,]    4    8    3    9    4  ## Replace NA with zeros replace_NA(X, value = 0) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    0    7    7    3    1 #> [2,]    7    0    9    4    3 #> [3,]    9    6    7    3    5 #> [4,]    4    8    3    9    4 #> [5,]    1    0   10    7    2","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the Availability of a Package \u2014 needs","title":"Check the Availability of a Package \u2014 needs","text":"Check Availability Package","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the Availability of a Package \u2014 needs","text":"","code":"needs(x, ask = TRUE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the Availability of a Package \u2014 needs","text":"x character vector naming packages check. ask logical scalar: user asked select packages downloaded installed?","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the Availability of a Package \u2014 needs","text":"Invisibly returns NULL.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check the Availability of a Package \u2014 needs","text":"needs() designed use inside functions package check availability suggested package. required packages available R running interactively, user asked install packages.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/needs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Check the Availability of a Package \u2014 needs","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix Predicates \u2014 predicate-matrix","title":"Matrix Predicates \u2014 predicate-matrix","text":"is_square() checks matrix square. is_symmetric() checks matrix symmetric.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix Predicates \u2014 predicate-matrix","text":"","code":"is_square(x)  is_symmetric(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix Predicates \u2014 predicate-matrix","text":"x matrix tested.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrix Predicates \u2014 predicate-matrix","text":"logical scalar.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Numeric Predicates \u2014 predicate-numeric","title":"Numeric Predicates \u2014 predicate-numeric","text":"Check numeric objects: is_zero() checks object contains zeros. is_odd() is_even() check number odd even, respectively. is_positive() is_negative check object contains (strictly) positive negative numbers. is_whole() checks object contains whole numbers.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Numeric Predicates \u2014 predicate-numeric","text":"","code":"is_zero(x, na.rm = FALSE)  is_odd(x, na.rm = FALSE)  is_even(x, na.rm = FALSE)  is_positive(x, strict = FALSE, na.rm = FALSE)  is_negative(x, strict = FALSE, na.rm = FALSE)  is_whole(x, na.rm = FALSE, tolerance = .Machine$double.eps^0.5)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Numeric Predicates \u2014 predicate-numeric","text":"x numeric object tested. na.rm logical scalar: missing values (including NaN) omitted? strict logical scalar: strict inequality used? tolerance numeric scalar giving tolerance check within.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Numeric Predicates \u2014 predicate-numeric","text":"logical vector.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-scalar.html","id":null,"dir":"Reference","previous_headings":"","what":"Scalar Type Predicates \u2014 predicate-scalar","title":"Scalar Type Predicates \u2014 predicate-scalar","text":"Scalar Type Predicates","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-scalar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scalar Type Predicates \u2014 predicate-scalar","text":"","code":"is_scalar_list(x)  is_scalar_atomic(x)  is_scalar_vector(x)  is_scalar_numeric(x)  is_scalar_integer(x)  is_scalar_double(x)  is_scalar_character(x)  is_scalar_logical(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-scalar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scalar Type Predicates \u2014 predicate-scalar","text":"x object tested.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-scalar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scalar Type Predicates \u2014 predicate-scalar","text":"logical scalar.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-trend.html","id":null,"dir":"Reference","previous_headings":"","what":"Numeric Trend Predicates \u2014 predicate-trend","title":"Numeric Trend Predicates \u2014 predicate-trend","text":"Check numeric objects: is_constant() checks equality among elements vector. is_increasing() is_decreasing() check sequence numbers monotonically increasing decreasing, respectively.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-trend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Numeric Trend Predicates \u2014 predicate-trend","text":"","code":"is_constant(x, tolerance = .Machine$double.eps^0.5, na.rm = FALSE)  is_increasing(x, na.rm = FALSE)  is_decreasing(x, na.rm = FALSE)  is_greater(x, y, strict = FALSE, na.rm = FALSE)  is_lower(x, y, strict = FALSE, na.rm = FALSE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-trend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Numeric Trend Predicates \u2014 predicate-trend","text":"x, y numeric object tested. tolerance numeric scalar giving tolerance check within. na.rm logical scalar: missing values (including NaN) omitted? strict logical scalar: strict inequality used?","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-trend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Numeric Trend Predicates \u2014 predicate-trend","text":"logical scalar.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-type.html","id":null,"dir":"Reference","previous_headings":"","what":"Type Predicates \u2014 predicate-type","title":"Type Predicates \u2014 predicate-type","text":"Type Predicates","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Type Predicates \u2014 predicate-type","text":"","code":"is_list(x)  is_atomic(x)  is_vector(x)  is_numeric(x)  is_integer(x)  is_double(x)  is_character(x)  is_logical(x)  is_error(x)  is_warning(x)  is_message(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Type Predicates \u2014 predicate-type","text":"x object tested.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-type.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Type Predicates \u2014 predicate-type","text":"logical scalar.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-utils.html","id":null,"dir":"Reference","previous_headings":"","what":"Utility Predicates \u2014 predicate-utils","title":"Utility Predicates \u2014 predicate-utils","text":"is_empty() checks object empty (zero-length dimensions). has_length() checks long object. has_names() checks object named. has_duplicates() checks object duplicated elements. has_missing() has_infinite() check object contains missing infinite values.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-utils.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility Predicates \u2014 predicate-utils","text":"","code":"has_length(x, n = NULL)  has_names(x, names = NULL)  has_duplicates(x)  has_missing(x)  has_infinite(x)  is_empty(x)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-utils.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility Predicates \u2014 predicate-utils","text":"x vector tested. n length-one numeric vector specifying length test x . NULL, returns TRUE x length greater zero, FALSE otherwise. names character vector specifying names test x . NULL, returns TRUE x names, FALSE otherwise.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/predicate-utils.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility Predicates \u2014 predicate-utils","text":"logical scalar.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/remove_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove Constant Columns \u2014 remove_constant","title":"Remove Constant Columns \u2014 remove_constant","text":"Remove Constant Columns","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/remove_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove Constant Columns \u2014 remove_constant","text":"","code":"remove_constant(x, ...)  # S4 method for ANY remove_constant(x, na.rm = FALSE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/remove_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove Constant Columns \u2014 remove_constant","text":"x R object (matrix data.frame). ... Currently used. na.rm logical scalar: NA values stripped computation proceeds?","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/remove_constant.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Remove Constant Columns \u2014 remove_constant","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/remove_constant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remove Constant Columns \u2014 remove_constant","text":"","code":"## Create a data.frame X <- data.frame(A = 1, B = 1:3) X #>   A B #> 1 1 1 #> 2 1 2 #> 3 1 3  remove_constant(X) #>   B #> 1 1 #> 2 2 #> 3 3  ## Add NA X[1, 1] <- NA remove_constant(X) #>    A B #> 1 NA 1 #> 2  1 2 #> 3  1 3 remove_constant(X, na.rm = TRUE) #>   B #> 1 1 #> 2 2 #> 3 3","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/validate.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a Condition \u2014 validate","title":"Validate a Condition \u2014 validate","text":"Validate Condition","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/validate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a Condition \u2014 validate","text":"","code":"validate(expr)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/validate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a Condition \u2014 validate","text":"expr object evaluated.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/validate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate a Condition \u2014 validate","text":"Returns NULL success, otherwise returns error string.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/validate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Validate a Condition \u2014 validate","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/with_seed.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate an Expression with a Temporarily Seed \u2014 with_seed","title":"Evaluate an Expression with a Temporarily Seed \u2014 with_seed","text":"Evaluate Expression Temporarily Seed","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/with_seed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate an Expression with a Temporarily Seed \u2014 with_seed","text":"","code":"with_seed(expr, seed, ..., envir = parent.frame(), rounding = TRUE)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/with_seed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate an Expression with a Temporarily Seed \u2014 with_seed","text":"expr expression evaluated. seed single value passed set.seed(). ... arguments passed set.seed(). envir environment expr evaluated. rounding logical scalar: default discrete uniform generation method R versions prior 3.6.0 used? Usefull unit testing.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/with_seed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate an Expression with a Temporarily Seed \u2014 with_seed","text":"results expr evaluated.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/zero.html","id":null,"dir":"Reference","previous_headings":"","what":"Tools for Working With Zeros \u2014 zero","title":"Tools for Working With Zeros \u2014 zero","text":"remove_zero() remove rows/columns contain zeros. replace_zero replaces zeros.","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/zero.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tools for Working With Zeros \u2014 zero","text":"","code":"remove_zero(x, ...)  replace_zero(x, ...)  # S4 method for ANY remove_zero(x, margin = 1, all = FALSE, na.rm = FALSE)  # S4 method for matrix replace_zero(x, value)  # S4 method for data.frame replace_zero(x, value)","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/zero.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tools for Working With Zeros \u2014 zero","text":"x R object (matrix data.frame). ... Currently used. margin length-one numeric vector giving subscripts function applied (1 indicates rows, 2 indicates columns). logical scalar. TRUE, rows/columns whose values meet condition defined f considered. FALSE (default), rows/columns least one value validates condition defined f considered. na.rm logical scalar: NA values stripped computation proceeds? value possible replacement value.","code":"","package":"arkhe"},{"path":[],"package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/zero.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tools for Working With Zeros \u2014 zero","text":"N. Frerebeau","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/arkhe/reference/zero.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tools for Working With Zeros \u2014 zero","text":"","code":"## Create a data matrix X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)  ## Add zero k <- sample(1:25, 3, FALSE) X[k] <- 0 X #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    7   10    3    5    5 #> [2,]    3    9    0    7    7 #> [3,]    7    4    9    1    7 #> [4,]    7    7    8    2    2 #> [5,]   10    2    0   10    0  ## Remove rows with zero remove_zero(X, margin = 1) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    7   10    3    5    5 #> [2,]    7    4    9    1    7 #> [3,]    7    7    8    2    2  ## Replace zero replace_zero(X, value = 1) #>      [,1] [,2] [,3] [,4] [,5] #> [1,]    7   10    3    5    5 #> [2,]    3    9    1    7    7 #> [3,]    7    4    9    1    7 #> [4,]    7    7    8    2    2 #> [5,]   10    2    1   10    1","package":"arkhe"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"do-pca","dir":"Articles","previous_headings":"","what":"Do PCA","title":"Principal Components Analysis","text":"","code":"## Load data data(iris) head(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa  ## Compute PCA ## (non numeric variables are automatically removed) X <- pca(iris, center = TRUE, scale = TRUE) #> 1 qualitative variable was removed: Species.","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"explore-the-results","dir":"Articles","previous_headings":"","what":"Explore the results","title":"Principal Components Analysis","text":"dimensio provides several methods extract (get_*()) results: get_data() returns original data. get_contributions() returns contributions definition principal dimensions. get_coordinates() returns principal standard coordinates. get_correlations() returns correlations variables dimensions. get_cos2() returns cos2 values (.e.\u00a0quality representation points factor map). get_eigenvalues() returns eigenvalues, percentages variance cumulative percentages variance. package also allows quickly visualize (viz_*()) results: biplot() produces biplot. screeplot() produces scree plot. viz_rows()/viz_individuals() displays row/individual principal coordinates. viz_columns()/viz_variables() displays columns/variable principal coordinates. viz_contributions() displays (joint) contributions. viz_cos2() displays (joint) cos2.","code":"## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.9184978 73.342264   73.34226 #> F2   0.9140305 22.969715   96.31198 #> F3   0.1467569  3.688021  100.00000  ## Scree plot screeplot(X, cumulative = TRUE)  ## Plot variable contributions to the definition of the first two axes viz_contributions(X, margin = 2, axes = c(1, 2))","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"pca-biplot","dir":"Articles","previous_headings":"","what":"PCA biplot","title":"Principal Components Analysis","text":"biplot simultaneous representation rows columns rectangular dataset. generalization scatterplot case mutlivariate data: allows visualize much information possible single graph (Greenacre 2010). dimensio allows display two types biplots: form biplot (row-metric-preserving biplot) covariance biplot (column-metric-preserving biplot). See Greenacre (2010) details biplots. form biplot favors representation individuals: distance individuals approximates Euclidean distance rows. form biplot length vector approximates quality representation variable.  covariance biplot favors representation variables: length vector approximates standard deviation variable cosine angle formed two vectors approximates correlation two variables (Greenacre 2010). covariance biplot distance individuals approximates Mahalanobis distance rows.  Biplots drawbacks advantages: can quickly become difficult read display lot information . may preferable visualize results individuals variables separately.","code":"biplot(X, type = \"form\", label = \"variables\") biplot(X, type = \"covariance\", label = \"variables\")","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"plot-pca-loadings","dir":"Articles","previous_headings":"","what":"Plot PCA loadings","title":"Principal Components Analysis","text":"viz_variables() depicts variables rays emanating origin (lengths directions important interpretation).  viz_variables() allows highlight additional information varying different graphical elements (color, transparency, shape size symbols\u2026).","code":"## Plot variables factor map viz_variables(X) ## Highlight cos2 viz_variables(   x = X,    highlight = \"cos2\",    col = khroma::color(\"YlOrBr\")(4, range = c(0.5, 1)) )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"plot-pca-scores","dir":"Articles","previous_headings":"","what":"Plot PCA scores","title":"Principal Components Analysis","text":"viz_individuals() allows display individuals highlight additional information.","code":"## Plot individuals and color by species viz_individuals(   x = X,   highlight = iris$Species,   col = khroma::color(\"bright\")(3), # Custom color scale   pch = c(15, 16, 17) # Custom symbols ) ## Add ellipses viz_individuals(x = X) viz_tolerance(x = X, group = iris$Species, level = 0.95,               border = khroma::color(\"high contrast\")(3))  ## Add convex hull viz_individuals(x = X) viz_hull(x = X, group = iris$Species, level = 0.95,          border = khroma::color(\"high contrast\")(3)) ## Highlight petal length viz_individuals(   x = X,    highlight = iris$Petal.Length,   col = khroma::color(\"YlOrBr\")(12), # Custom color scale   cex = c(1, 2), # Custom size scale   pch = 16 ) ## Highlight contributions viz_individuals(   x = X,    highlight = \"contrib\",   col = khroma::color(\"iridescent\")(12), # Custom color scale   cex = c(1, 2), # Custom size scale   pch = 16 )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/articles/pca.html","id":"custom-plot","dir":"Articles","previous_headings":"","what":"Custom plot","title":"Principal Components Analysis","text":"need flexibility, get_*() family tidy() augment() functions allow extract results data frames thus build custom graphs base graphics ggplot2.","code":"iris_tidy <- tidy(X, margin = 2) head(iris_tidy) #>          label component supplementary coordinate contribution        cos2 #> 1 Petal.Length        F1         FALSE 0.99155518  33.68793618 0.983181682 #> 2 Petal.Length        F2         FALSE 0.02341519   0.05998389 0.000548271 #> 3 Petal.Length        F3         FALSE 0.05444699   2.01999049 0.002964475 #> 4  Petal.Width        F1         FALSE 0.96497896  31.90629060 0.931184395 #> 5  Petal.Width        F2         FALSE 0.06399985   0.44812296 0.004095980 #> 6  Petal.Width        F3         FALSE 0.24298265  40.23019050 0.059040571  iris_augment <- augment(X, margin = 1) head(iris_augment) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.264703  0.4800266     1         FALSE 0.006666667 5.359304     3.572870 #> 2 -2.080961 -0.6741336     2         FALSE 0.006666667 4.784855     3.189904 #> 3 -2.364229 -0.3419080     3         FALSE 0.006666667 5.706480     3.804320 #> 4 -2.299384 -0.5973945     4         FALSE 0.006666667 5.644048     3.762699 #> 5 -2.389842  0.6468354     5         FALSE 0.006666667 6.129742     4.086494 #> 6 -2.075631  1.4891775     6         FALSE 0.006666667 6.525894     4.350596 #>        cos2 #> 1 0.9968578 #> 2 0.9864650 #> 3 0.9995167 #> 4 0.9977577 #> 5 0.9997491 #> 6 0.9998819 ## Custom plot with ggplot2 ggplot2::ggplot(data = iris_augment) +   ggplot2::aes(x = F1, y = F2, colour = contribution) +   ggplot2::geom_vline(xintercept = 0, linewidth = 0.5, linetype = \"dashed\") +   ggplot2::geom_hline(yintercept = 0, linewidth = 0.5, linetype = \"dashed\") +   ggplot2::geom_point() +   ggplot2::coord_fixed() + # /!\\   ggplot2::theme_bw() +   khroma::scale_color_iridescent()","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Multivariate Data Analysis","text":"Simple Principal Components Analysis (PCA; see vignette(\"pca\")) Correspondence Analysis (CA) based Singular Value Decomposition (SVD). package provides S4 classes methods compute, extract, summarize visualize results multivariate data analysis. also includes methods partial bootstrap validation. many good packages multivariate data analysis (FactoMineR, ade4, vegan ca, extended FactoExtra). dimensio designed simple possible, providing necessary tools explore results analysis.","code":"To cite dimensio in publications use:    Frerebeau N (2023). _dimensio: Multivariate Data Analysis_.   Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.4478530 <https://doi.org/10.5281/zenodo.4478530>,   R package version 0.4.0, <https://packages.tesselle.org/dimensio/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{dimensio: Multivariate Data Analysis}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 0.4.0},     doi = {10.5281/zenodo.4478530},     url = {https://packages.tesselle.org/dimensio/},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Multivariate Data Analysis","text":"can install released version dimensio CRAN : development version GitHub :","code":"install.packages(\"dimensio\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/dimensio\")","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Multivariate Data Analysis","text":"","code":"## Install extra packages (if needed) # install.packages(\"khroma\")  ## Load packages library(dimensio)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"compute","dir":"","previous_headings":"Usage","what":"Compute","title":"Multivariate Data Analysis","text":"","code":"## Load data data(iris)  ## Compute PCA ## (non numeric variables are automatically removed) X <- pca(iris, center = TRUE, scale = TRUE) #> 1 qualitative variable was removed: Species.","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"extract","dir":"","previous_headings":"Usage","what":"Extract","title":"Multivariate Data Analysis","text":"dimensio provides several methods extract results: get_data() returns original data. get_contributions() returns contributions definition principal dimensions. get_coordinates() returns principal standard coordinates. get_correlations() returns correlations variables dimensions. get_cos2() returns cos2 values (.e.\u00a0quality representation points factor map). get_eigenvalues() returns eigenvalues, percentages variance cumulative percentages variance.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"visualize","dir":"","previous_headings":"Usage","what":"Visualize","title":"Multivariate Data Analysis","text":"package allows quickly visualize results: biplot() produces biplot. screeplot() produces scree plot. viz_rows()/viz_individuals() displays row/individual principal coordinates. viz_columns()/viz_variables() displays columns/variable principal coordinates. viz_variables() depicts variables rays emanating origin (lengths directions important interpretation). viz_contributions() displays (joint) contributions. viz_cos2() displays (joint) cos2. viz_*() functions allow highlight additional information varying different graphical elements (color, transparency, shape size symbols\u2026).","code":"## Form biplot biplot(X, type = \"form\") ## Highlight species viz_individuals(   x = X,    highlight = iris$Species,    col = khroma::color(\"high contrast\")(3),    pch = 16 ) ## Add ellipses viz_tolerance(   x = X,    group = iris$Species,    level = 0.95,   border = khroma::color(\"high contrast\")(3) )  ## Highlight petal length viz_individuals(   x = X,   highlight = iris$Petal.Length,   col = khroma::color(\"iridescent\")(255),    cex = c(1, 2),   pch = 16 ) ## Plot variables factor map viz_variables(X)  ## Scree plot screeplot(X, eigenvalues = FALSE, cumulative = TRUE)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Multivariate Data Analysis","text":"Please note dimensio project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":[],"package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/CA-class.html","id":null,"dir":"Reference","previous_headings":"","what":"CA Results \u2014 CA-class","title":"CA Results \u2014 CA-class","text":"S4 class store results simple correspondence analysis.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/CA-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"CA Results \u2014 CA-class","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/CA-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CA Results \u2014 CA-class","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## The chi square of independence between the two variables stats::chisq.test(colours) #>  #> \tPearson's Chi-squared test #>  #> data:  colours #> X-squared = 138.29, df = 9, p-value < 2.2e-16 #>   ## Compute correspondence analysis X <- ca(colours)  ## Plot rows viz_rows(X, labels = TRUE)   ## Plot columns viz_columns(X, labels = TRUE)   ## Get row coordinates head(get_coordinates(X, margin = 1)) #>                  F1          F2           F3  .sup #> marron   -0.4921577 -0.08832151  0.021611305 FALSE #> noisette -0.2125969  0.16739109 -0.100518284 FALSE #> vert      0.1617534  0.33903957  0.087597437 FALSE #> bleu      0.5474139 -0.08295428 -0.004709408 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                 F1          F2          F3  .sup #> brun    -0.5045624 -0.21482046  0.05550909 FALSE #> chatain -0.1482527  0.03266635 -0.04880414 FALSE #> roux    -0.1295233  0.31964240  0.08315117 FALSE #> blond    0.8353478 -0.06957934  0.01621471 FALSE  ## Get row distances to centroid head(get_distances(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.25048691 0.08332116 0.14878530 0.30656555   ## Get row inertias head(get_inertia(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.09308635 0.01308930 0.01608490 0.11133715   ## Get row contributions head(get_contributions(X, margin = 1)) #>                 F1       F2         F3 #> marron   43.115744 13.04249  6.6795992 #> noisette  3.400961 19.80398 61.0855951 #> vert      1.354851 55.90952 31.9248234 #> bleu     52.128445 11.24401  0.3099822  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1 0.208772652 89.372732   89.37273 #> F2 0.022226615  9.514911   98.88764 #> F3 0.002598439  1.112356  100.00000","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateAnalysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Output of Multivariate Data Analysis \u2014 MultivariateAnalysis","title":"Output of Multivariate Data Analysis \u2014 MultivariateAnalysis","text":"virtual S4 class store output multivariate data analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateAnalysis.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Output of Multivariate Data Analysis \u2014 MultivariateAnalysis","text":"data numeric matrix. dimension integer giving dimension solution. singular_values numeric vector giving singular values. rows MultivariateResults object. columns MultivariateResults object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateAnalysis.html","id":"subset","dir":"Reference","previous_headings":"","what":"Subset","title":"Output of Multivariate Data Analysis \u2014 MultivariateAnalysis","text":"code snippets , x MultivariateAnalysis object. x[[]] Extracts information slot selected subscript . length-one character vector.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateAnalysis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Output of Multivariate Data Analysis \u2014 MultivariateAnalysis","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateBootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Output of Bootstrap Replications \u2014 MultivariateBootstrap","title":"Output of Bootstrap Replications \u2014 MultivariateBootstrap","text":"virtual S4 class store output bootstrap analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateBootstrap.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Output of Bootstrap Replications \u2014 MultivariateBootstrap","text":"replications integer giving number bootstrap replications.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateBootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Output of Bootstrap Replications \u2014 MultivariateBootstrap","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateResults.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Data Analysis Results \u2014 MultivariateResults","title":"Multivariate Data Analysis Results \u2014 MultivariateResults","text":"S4 class store results multivariate data analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateResults.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Multivariate Data Analysis Results \u2014 MultivariateResults","text":"names character vector specifying row names. principal numeric matrix giving principal coordinates. standard numeric matrix giving standard coordinates. contributions numeric matrix giving contributions definition dimensions. cosine numeric matrix giving \\(cos^2\\) values. distances numeric vector giving distances centroid. weights numeric vector giving masses/weights. supplement logical vector specifying supplementary points. order integer vector giving original indices data (computation moves supplementary points end results). groups character vector specifying class observation.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateResults.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Multivariate Data Analysis Results \u2014 MultivariateResults","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateSummary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary of Multivariate Data Analysis \u2014 MultivariateSummary","title":"Summary of Multivariate Data Analysis \u2014 MultivariateSummary","text":"virtual S4 class store summary multivariate data analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateSummary.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Summary of Multivariate Data Analysis \u2014 MultivariateSummary","text":"data numeric matrix. eigenvalues numeric matrix. results numeric matrix. supplement logical vector specifying supplementary points. margin integer.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/MultivariateSummary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary of Multivariate Data Analysis \u2014 MultivariateSummary","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/PCA-class.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Results \u2014 PCA-class","title":"PCA Results \u2014 PCA-class","text":"S4 class store results principal components analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/PCA-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"PCA Results \u2014 PCA-class","text":"center numeric vector giving column mean initial dataset (active individuals ). scale numeric vector giving column standard deviations initial dataset (active individuals ).","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/PCA-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"PCA Results \u2014 PCA-class","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/PCA-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Results \u2014 PCA-class","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = sample(150, 10), sup_col = 2) #> 1 qualitative variable was removed: Species.  ## Get row coordinates head(get_coordinates(X, margin = 1)) #>          F1          F2  .sup #> 1 -2.093090  0.29090646 FALSE #> 2 -2.230345  0.09157279 FALSE #> 3 -2.401333 -0.09328414 FALSE #> 4 -2.402495 -0.22190447 FALSE #> 5 -2.161718  0.19123962 FALSE #> 6 -1.633770  0.40889077 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                      F1         F2  .sup #> Sepal.Length  0.9308275  0.3643076 FALSE #> Petal.Length  0.9841342 -0.1138202 FALSE #> Petal.Width   0.9656191 -0.2351788 FALSE #> Sepal.Width  -0.3360454  0.4364270  TRUE  ## Get correlations between variables and dimensions head(get_correlations(X)) #>                      F1         F2  .sup #> Sepal.Length  0.9308275  0.3643076 FALSE #> Petal.Length  0.9841342 -0.1138202 FALSE #> Petal.Width   0.9656191 -0.2351788 FALSE #> Sepal.Width  -0.3360454  0.4364270  TRUE  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.7673802 93.229129   93.22913 #> F2   0.2009841  6.770871  100.00000","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/benthos.html","id":null,"dir":"Reference","previous_headings":"","what":"Benthos \u2014 benthos","title":"Benthos \u2014 benthos","text":"Abundances Marine Species Sea-Bed Samples","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/benthos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Benthos \u2014 benthos","text":"","code":"benthos","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/benthos.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Benthos \u2014 benthos","text":"data.frame 13 columns (sites) 92 rows (species).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/benthos.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Benthos \u2014 benthos","text":"http://www.carme-n.org/?sec=data7","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Biplot \u2014 biplot","title":"Biplot \u2014 biplot","text":"Biplot","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Biplot \u2014 biplot","text":"","code":"# S4 method for CA biplot(   x,   axes = c(1, 2),   type = c(\"rows\", \"columns\", \"contributions\"),   active = TRUE,   sup = TRUE,   labels = \"columns\",   col.rows = \"#004488\",   col.columns = \"#BB5566\",   cex.rows = graphics::par(\"cex\"),   cex.columns = graphics::par(\"cex\"),   pch.rows = 16,   pch.columns = 17,   main = NULL,   sub = NULL,   ... )  # S4 method for PCA biplot(   x,   axes = c(1, 2),   type = c(\"form\", \"covariance\"),   active = TRUE,   sup = TRUE,   labels = \"variables\",   col.rows = \"#004488\",   col.columns = \"#BB5566\",   pch.rows = 16,   pch.columns = 17,   lty = \"solid\",   lwd = 2,   main = NULL,   sub = NULL,   ... )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Biplot \u2014 biplot","text":"x CA PCA object. axes length-two numeric vector giving dimensions plotted. type character string specifying biplot plotted (see ). must one \"rows\", \"columns\", \"contribution\" (CA), \"form\" \"covariance\" (PCA). unambiguous substring can given. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels character vector specifying whether \"rows\"/\"individuals\" /\"columns\"/\"variables\" names must drawn. unambiguous substring can given. col.rows, col.columns color specification. cex.rows, cex.columns numerical vector giving amount plotting characters symbols scaled relative default. pch.rows, pch.columns symbol specification. main character string giving main title plot. sub character string giving subtitle plot. ... parameters passed wordcloud::wordlayout(). lty, lwd specification line type width.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Biplot \u2014 biplot","text":"biplot() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Biplot \u2014 biplot","text":"biplot simultaneous representation rows columns rectangular dataset. generalization scatterplot case mutlivariate data: allows visualize much information possible single graph (Greenacre 2010). Biplots drawbacks advantages: can quickly become difficult read display lot information . may preferable visualize results individuals variables separately.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"pca-biplots","dir":"Reference","previous_headings":"","what":"PCA Biplots","title":"Biplot \u2014 biplot","text":"form Form biplot (row-metric-preserving). form biplot favors representation individuals: distance individuals approximates Euclidean distance rows. form biplot length vector approximates quality representation variable. covariance Covariance biplot (column-metric-preserving). covariance biplot favors representation variables: length vector approximates standard deviation variable cosine angle formed two vectors approximates correlation two variables. covariance biplot distance individuals approximates Mahalanobis distance rows.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"ca-biplots","dir":"Reference","previous_headings":"","what":"CA Biplots","title":"Biplot \u2014 biplot","text":"rows Row principal biplot. columns Column principal biplot. contribution Contribution biplot","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Biplot \u2014 biplot","text":"Aitchison, J. Greenacre, M. (2002). Biplots Compositional Data. Journal Royal Statistical Society: Series C (Applied Statistics), 51(4): 375-92. doi:10.1111/1467-9876.00275 . Greenacre, M. J. Biplots Practice. Bilbao: Fundaci\u00f3n BBVA, 2010.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Biplot \u2014 biplot","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/biplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Biplot \u2014 biplot","text":"","code":"## Replicate examples from Greenacre 2007, p. 59-68 data(\"countries\")  ## Compute principal components analysis ## All rows and all columns obtain the same weight row_w <- rep(1 / nrow(countries), nrow(countries)) # 1/13 col_w <- rep(1 / ncol(countries), ncol(countries)) # 1/6 Y <- pca(countries, scale = FALSE, weight_row = row_w, weight_col = col_w)  ## Row-metric-preserving biplot (form biplot) biplot(Y, type = \"form\")   ## Column-metric-preserving biplot (covariance biplot) biplot(Y, type = \"covariance\")   ## Replicate examples from Greenacre 2007, p. 79-88 data(\"benthos\")  ## Compute correspondence analysis X <- ca(benthos)  ## Row principal CA biplot biplot(X, type = \"row\", labels = \"columns\")   ## Column principal CA biplot biplot(X, type = \"column\", labels = \"columns\")   ## Contribution CA biplot biplot(X, type = \"contrib\", labels = NULL)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial Bootstrap Analysis \u2014 bootstrap","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"Checks analysis partial bootstrap resampling.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"","code":"bootstrap(object, ...)  # S4 method for CA bootstrap(object, n = 30)  # S4 method for PCA bootstrap(object, n = 30)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"object CA PCA object. ... Currently used. n non-negative integer giving number bootstrap replications.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"#' Returns BootstrapCA BootstrapPCA object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"Greenacre, Michael J. Theory Applications Correspondence Analysis. London: Academic Press, 1984. Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inf\u00e9rence en fouille de donn\u00e9es. Paris: Dunod, 2006.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial Bootstrap Analysis \u2014 bootstrap","text":"","code":"## Bootstrap on CA ## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## Compute correspondence analysis X <- ca(colours)  ## Bootstrap (30 replicates) Y <- bootstrap(X, n = 30)  if (FALSE) { ## Get replicated coordinates get_replications(Y, margin = 1) get_replications(Y, margin = 2) }  ## Plot with ellipses viz_rows(Y) viz_tolerance(Y, margin = 1, level = c(0.68, 0.95))   viz_columns(Y) viz_tolerance(Y, margin = 2, level = c(0.68, 0.95))   ## Plot with convex hulls viz_columns(Y) viz_hull(Y, margin = 2)   ## Bootstrap on PCA ## Compute principal components analysis data(\"iris\") X <- pca(iris) #> 1 qualitative variable was removed: Species.  ## Bootstrap (30 replicates) Y <- bootstrap(X, n = 30)  ## Plot with ellipses viz_variables(Y) viz_tolerance(Y, margin = 2, level = c(0.68, 0.95))","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":null,"dir":"Reference","previous_headings":"","what":"Correspondence Analysis \u2014 ca","title":"Correspondence Analysis \u2014 ca","text":"Computes simple correspondence analysis based singular value decomposition.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correspondence Analysis \u2014 ca","text":"","code":"ca(object, ...)  # S4 method for data.frame ca(object, rank = NULL, sup_row = NULL, sup_col = NULL)  # S4 method for matrix ca(object, rank = NULL, sup_row = NULL, sup_col = NULL)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correspondence Analysis \u2014 ca","text":"object \\(m \\times p\\) numeric matrix data.frame. ... Currently used. rank integer value specifying maximal number components kept results. NULL (default), \\(min(m, p) - 1\\) components returned. sup_row numeric logical vector specifying indices supplementary rows. sup_col numeric logical vector specifying indices supplementary columns.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correspondence Analysis \u2014 ca","text":"CA object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correspondence Analysis \u2014 ca","text":"Greenacre, M. J. Theory Applications Correspondence Analysis. London: Academic Press, 1984. Greenacre, M. J. Correspondence Analysis Practice. Seconde edition. Interdisciplinary Statistics Series. Boca Raton: Chapman & Hall/CRC, 2007. Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inf\u00e9rence en fouille de donn\u00e9es. Paris: Dunod, 2006.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correspondence Analysis \u2014 ca","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/ca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correspondence Analysis \u2014 ca","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## The chi square of independence between the two variables stats::chisq.test(colours) #>  #> \tPearson's Chi-squared test #>  #> data:  colours #> X-squared = 138.29, df = 9, p-value < 2.2e-16 #>   ## Compute correspondence analysis X <- ca(colours)  ## Plot rows viz_rows(X, labels = TRUE)   ## Plot columns viz_columns(X, labels = TRUE)   ## Get row coordinates head(get_coordinates(X, margin = 1)) #>                  F1          F2           F3  .sup #> marron   -0.4921577 -0.08832151  0.021611305 FALSE #> noisette -0.2125969  0.16739109 -0.100518284 FALSE #> vert      0.1617534  0.33903957  0.087597437 FALSE #> bleu      0.5474139 -0.08295428 -0.004709408 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                 F1          F2          F3  .sup #> brun    -0.5045624 -0.21482046  0.05550909 FALSE #> chatain -0.1482527  0.03266635 -0.04880414 FALSE #> roux    -0.1295233  0.31964240  0.08315117 FALSE #> blond    0.8353478 -0.06957934  0.01621471 FALSE  ## Get row distances to centroid head(get_distances(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.25048691 0.08332116 0.14878530 0.30656555   ## Get row inertias head(get_inertia(X, margin = 1)) #>     marron   noisette       vert       bleu  #> 0.09308635 0.01308930 0.01608490 0.11133715   ## Get row contributions head(get_contributions(X, margin = 1)) #>                 F1       F2         F3 #> marron   43.115744 13.04249  6.6795992 #> noisette  3.400961 19.80398 61.0855951 #> vert      1.354851 55.90952 31.9248234 #> bleu     52.128445 11.24401  0.3099822  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1 0.208772652 89.372732   89.37273 #> F2 0.022226615  9.514911   98.88764 #> F3 0.002598439  1.112356  100.00000","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/colours.html","id":null,"dir":"Reference","previous_headings":"","what":"Colours \u2014 colours","title":"Colours \u2014 colours","text":"Contingency table eye hair colours different individuals.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/colours.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Colours \u2014 colours","text":"","code":"colours","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/colours.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Colours \u2014 colours","text":"data.frame 4 columns (hair colours) 4 rows (eye colours).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/colours.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Colours \u2014 colours","text":"Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inf\u00e9rence en fouille de donn\u00e9es. Paris: Dunod, 2006, p. 170-172","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/countries.html","id":null,"dir":"Reference","previous_headings":"","what":"Countries \u2014 countries","title":"Countries \u2014 countries","text":"Student ratings 13 countries six attributes.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/countries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Countries \u2014 countries","text":"","code":"countries","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/countries.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Countries \u2014 countries","text":"data.frame 6 columns (attributes) 13 rows (countries).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/countries.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Countries \u2014 countries","text":"Greenacre, M. J. Biplots Practice. Bilbao: Fundaci\u00f3n BBVA, 2010.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimensio-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in dimensio \u2014 dimensio-deprecated","title":"Deprecated Functions in dimensio \u2014 dimensio-deprecated","text":"functions still work removed (defunct) next version.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimensio-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions in dimensio \u2014 dimensio-deprecated","text":"","code":"stat_hull(   mapping = NULL,   data = NULL,   geom = \"polygon\",   position = \"identity\",   na.rm = FALSE,   show.legend = NA,   inherit.aes = TRUE,   ... )  plot_rows(object, ...)  # S4 method for MultivariateAnalysis plot_rows(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_columns(object, ...)  # S4 method for MultivariateAnalysis plot_columns(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  # S4 method for BootstrapPCA plot_columns(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_individuals(object, ...)  # S4 method for PCA plot_individuals(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   fill = NULL,   shape = NULL,   size = NULL,   group = NULL )  plot_variables(object, ...)  # S4 method for PCA plot_variables(   object,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   alpha = NULL,   colour = NULL,   linetype = NULL,   size = NULL,   group = NULL )  plot_variance(object, ...)  # S4 method for MultivariateAnalysis plot_variance(   object,   select = NULL,   variance = TRUE,   cumulative = TRUE,   fill = \"grey30\",   border = \"grey10\",   colour = \"red\" )  plot_contributions(object, ...)  # S4 method for MultivariateAnalysis plot_contributions(   object,   margin = 2,   axes = 1,   sort = TRUE,   decreasing = TRUE,   limit = 10,   fill = \"grey30\",   border = \"grey10\" )  plot_cos2(object, ...)  # S4 method for MultivariateAnalysis plot_cos2(   object,   margin = 2,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   sort = TRUE,   decreasing = TRUE,   limit = 10,   fill = \"grey30\",   border = \"grey10\" )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimensio-package.html","id":null,"dir":"Reference","previous_headings":"","what":"dimensio: Multivariate Data Analysis \u2014 dimensio-package","title":"dimensio: Multivariate Data Analysis \u2014 dimensio-package","text":"Simple Principal Components Analysis (PCA) Correspondence Analysis (CA) based Singular Value Decomposition (SVD). package provides S4 classes methods compute, extract, summarize visualize results multivariate data analysis. also includes methods partial bootstrap validation described Greenacre (1984) <isbn:978-0-12-299050-2> Lebart et al. (2006) <isbn:978-2-10-049616-7>.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimensio-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"dimensio: Multivariate Data Analysis \u2014 dimensio-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimnames.html","id":null,"dir":"Reference","previous_headings":"","what":"Dimnames of an Object \u2014 dimnames","title":"Dimnames of an Object \u2014 dimnames","text":"Retrieve set dimnames object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimnames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dimnames of an Object \u2014 dimnames","text":"","code":"# S4 method for MultivariateAnalysis dim(x)  # S4 method for MultivariateAnalysis rownames(x, do.NULL = TRUE, prefix = \"row\")  # S4 method for MultivariateAnalysis colnames(x, do.NULL = TRUE, prefix = \"col\")  # S4 method for MultivariateAnalysis dimnames(x)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimnames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dimnames of an Object \u2014 dimnames","text":"x object retrieve row column names (CA PCA object). .NULL logical scalar. FALSE names NULL, names created. prefix character string specifying prefix created names.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/dimnames.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dimnames of an Object \u2014 dimnames","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_contributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Contributions \u2014 get_contributions","title":"Get Contributions \u2014 get_contributions","text":"Get Contributions","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_contributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Contributions \u2014 get_contributions","text":"","code":"get_contributions(x, ...)  get_correlations(x, ...)  get_cos2(x, ...)  # S4 method for MultivariateAnalysis get_contributions(x, margin = 1)  # S4 method for PCA get_correlations(x, sup_name = \".sup\")  # S4 method for MultivariateAnalysis get_cos2(x, margin = 1, sup_name = \".sup\")","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_contributions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Contributions \u2014 get_contributions","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. sup_name character string specifying name column create supplementary points attribution (see ).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_contributions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Contributions \u2014 get_contributions","text":"get_contributions() returns data.frame contributions definition principal dimensions. get_correlations() returns data.frame correlations variables dimensions (PCA). extra column (named sup_name) added specifying whether observation supplementary point . get_cos2() returns data.frame \\(cos^2\\) values (.e. quality representation points factor map). extra column (named sup_name) added specifying whether observation supplementary point .","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_contributions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Contributions \u2014 get_contributions","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Coordinates \u2014 get_coordinates","title":"Get Coordinates \u2014 get_coordinates","text":"Get Coordinates","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Coordinates \u2014 get_coordinates","text":"","code":"get_coordinates(x, ...)  get_replications(x, ...)  # S4 method for MultivariateAnalysis get_coordinates(x, margin = 1, principal = TRUE, sup_name = \".sup\")  # S4 method for MultivariateBootstrap get_replications(x, margin = 1)  # S4 method for BootstrapPCA get_replications(x)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Coordinates \u2014 get_coordinates","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. principal logical scalar: principal coordinates returned? FALSE, standard coordinates returned. sup_name character string specifying name column create supplementary points attribution (see ).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Coordinates \u2014 get_coordinates","text":"get_coordinates() returns data.frame coordinates. extra column (named sup_name) added specifying whether observation supplementary point . get_replications() returns array coordinates.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Coordinates \u2014 get_coordinates","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_coordinates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Coordinates \u2014 get_coordinates","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 5:10) #> 1 qualitative variable was removed: Species.  ## Get row principal coordinates head(get_coordinates(X, margin = 1, principal = TRUE)) #>           F1         F2          F3  .sup #> 1  -2.385846  0.5084067 -0.12383101 FALSE #> 2  -2.215654 -0.6515155 -0.23313750 FALSE #> 3  -2.496566 -0.3169280  0.04974232 FALSE #> 4  -2.433607 -0.5744432  0.09690301 FALSE #> 11 -2.279483  1.0742447 -0.26629760 FALSE #> 12 -2.450871  0.1591839  0.09988982 FALSE  ## Get row standard coordinates head(get_coordinates(X, margin = 1, principal = FALSE)) #>          F1         F2         F3  .sup #> 1 -1.403075  0.5268995 -0.3142569 FALSE #> 2 -1.302987 -0.6752138 -0.5916536 FALSE #> 3 -1.468187 -0.3284560  0.1262355 FALSE #> 4 -1.431162 -0.5953380  0.2459193 FALSE #> 5 -1.340525  1.1133193 -0.6758069 FALSE #> 6 -1.441315  0.1649741  0.2534992 FALSE  ## Tidy principal coordinates head(tidy(X, margin = 1)) #>   label component supplementary coordinate contribution        cos2 #> 1     1        F1         FALSE -2.3858463   1.36709655 0.954033073 #> 2     1        F2         FALSE  0.5084067   0.19279380 0.043321256 #> 3     1        F3         FALSE -0.1238310   0.06858151 0.002570025 #> 4    10        F1          TRUE -2.3173283           NA 0.953213276 #> 5    10        F2          TRUE -0.4448514           NA 0.035127301 #> 6    10        F3          TRUE -0.2525218           NA 0.011319124 head(tidy(X, margin = 2)) #>          label component supplementary coordinate contribution         cos2 #> 1 Petal.Length        F1         FALSE 0.99121597  33.97921607 0.9825091061 #> 2 Petal.Length        F2         FALSE 0.01655680   0.02944324 0.0002741275 #> 3 Petal.Length        F3         FALSE 0.05477927   1.93260521 0.0030007681 #> 4  Petal.Width        F1         FALSE 0.96350955  32.10619376 0.9283506618 #> 5  Petal.Width        F2         FALSE 0.05756108   0.35586957 0.0033132775 #> 6  Petal.Width        F3         FALSE 0.24948796  40.08758618 0.0622442430  head(augment(X, margin = 1, axes = c(1, 2))) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.385846  0.5084067     1         FALSE 0.006944444 5.950740     4.132458 #> 2 -2.215654 -0.6515155     2         FALSE 0.006944444 5.333593     3.703884 #> 3 -2.496566 -0.3169280     3         FALSE 0.006944444 6.333284     4.398114 #> 4 -2.433607 -0.5744432     4         FALSE 0.006944444 6.252426     4.341962 #> 5 -2.279483  1.0742447    11         FALSE          NA 6.350046     4.409754 #> 6 -2.450871  0.1591839    12         FALSE          NA 6.032107     4.188963 #>        cos2 #> 1 0.9973543 #> 2 0.9880287 #> 3 0.9995093 #> 4 0.9977072 #> 5 0.9889258 #> 6 0.9951651 head(augment(X, margin = 2, axes = c(1, 2))) #>           F1         F2        label supplementary mass       sum contribution #> 1  0.8875399 0.36087785 Sepal.Length         FALSE    1 0.9179599     91.79599 #> 2 -0.4392190 0.89286998  Sepal.Width         FALSE    1 0.9901302     99.01302 #> 3  0.9912160 0.01655680 Petal.Length         FALSE    1 0.9827832     98.27832 #> 4  0.9635096 0.05756108  Petal.Width         FALSE    1 0.9316639     93.16639 #>        cos2 #> 1 0.9179599 #> 2 0.9901302 #> 3 0.9827832 #> 4 0.9316639","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Original Data \u2014 get_data","title":"Get Original Data \u2014 get_data","text":"Get Original Data","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Original Data \u2014 get_data","text":"","code":"get_data(x, ...)  # S4 method for MultivariateAnalysis get_data(x)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Original Data \u2014 get_data","text":"x object get element(s) (CA PCA object). ... Currently used.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Original Data \u2014 get_data","text":"Returns data.frame original data.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Original Data \u2014 get_data","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_distances.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Distances \u2014 get_distances","title":"Get Distances \u2014 get_distances","text":"Get Distances","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_distances.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Distances \u2014 get_distances","text":"","code":"get_distances(x, ...)  # S4 method for MultivariateAnalysis get_distances(x, margin = 1)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_distances.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Distances \u2014 get_distances","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_distances.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Distances \u2014 get_distances","text":"get_distances() returns numeric vector squared distance centroide.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_distances.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Distances \u2014 get_distances","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_eigenvalues.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Eigenvalues \u2014 get_eigenvalues","title":"Get Eigenvalues \u2014 get_eigenvalues","text":"Get Eigenvalues","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_eigenvalues.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Eigenvalues \u2014 get_eigenvalues","text":"","code":"get_eigenvalues(x)  get_inertia(x, ...)  get_variance(x, ...)  # S4 method for MultivariateAnalysis get_eigenvalues(x)  # S4 method for MultivariateAnalysis get_inertia(x, margin = 1)  # S4 method for MultivariateAnalysis get_variance(x, digits = 2)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_eigenvalues.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Eigenvalues \u2014 get_eigenvalues","text":"x object get element(s) (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. digits integer indicating number decimal places used.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_eigenvalues.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Eigenvalues \u2014 get_eigenvalues","text":"get_eigenvalues() returns data.frame following columns: eigenvalues, variance (percentage variance) cumulative (cumulative percentage variance). get_variance() returns numeric vector giving percentage explained variance dimension. get_inertia() returns numeric vector.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/get_eigenvalues.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Eigenvalues \u2014 get_eigenvalues","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Loadings \u2014 loadings","title":"Extract Loadings \u2014 loadings","text":"Extract loadingsin principal components analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Loadings \u2014 loadings","text":"","code":"# S4 method for PCA loadings(x)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Loadings \u2014 loadings","text":"x PCA object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Loadings \u2014 loadings","text":"Returns variable loadings (.e. coefficients linear combination original variables).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Extract Loadings \u2014 loadings","text":"loadings() implemented consistency [stats][stats::loadings].","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/loadings.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Loadings \u2014 loadings","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Components Analysis \u2014 pca","title":"Principal Components Analysis \u2014 pca","text":"Computes principal components analysis based singular value decomposition.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Components Analysis \u2014 pca","text":"","code":"pca(object, ...)  # S4 method for data.frame pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )  # S4 method for matrix pca(   object,   center = TRUE,   scale = TRUE,   rank = NULL,   sup_row = NULL,   sup_col = NULL,   weight_row = NULL,   weight_col = NULL )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Components Analysis \u2014 pca","text":"object \\(m \\times p\\) numeric matrix data.frame. ... Currently used. center logical scalar: variables shifted zero centered? scale logical scalar: variables scaled unit variance? rank integer value specifying maximal number components kept results. NULL (default), \\(p - 1\\) components returned. sup_row numeric logical vector specifying indices supplementary rows (individuals). sup_col numeric logical vector specifying indices supplementary columns (variables). weight_row numeric vector specifying active row (individual) weights. NULL (default), uniform weights used. Row weights internally normalized sum 1 weight_col numeric vector specifying active column (variable) weights. NULL (default), uniform weights (1) used.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Components Analysis \u2014 pca","text":"PCA object.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Principal Components Analysis \u2014 pca","text":"Lebart, L., Piron, M. Morineau, . Statistique exploratoire multidimensionnelle: visualisation et inf\u00e9rence en fouille de donn\u00e9es. Paris: Dunod, 2006.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Components Analysis \u2014 pca","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Components Analysis \u2014 pca","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = sample(150, 10), sup_col = 2) #> 1 qualitative variable was removed: Species.  ## Get row coordinates head(get_coordinates(X, margin = 1)) #>          F1          F2  .sup #> 1 -2.032409  0.28085613 FALSE #> 2 -2.170509  0.08045204 FALSE #> 3 -2.342144 -0.10541898 FALSE #> 4 -2.344124 -0.23468718 FALSE #> 5 -2.101459  0.18065408 FALSE #> 6 -1.571063  0.39938987 FALSE  ## Get column coordinates head(get_coordinates(X, margin = 2)) #>                      F1         F2  .sup #> Sepal.Length  0.9327177  0.3595041 FALSE #> Petal.Length  0.9844532 -0.1133177 FALSE #> Petal.Width   0.9666608 -0.2314771 FALSE #> Sepal.Width  -0.3298748  0.4616406  TRUE  ## Get correlations between variables and dimensions head(get_correlations(X)) #>                      F1         F2  .sup #> Sepal.Length  0.9327177  0.3595041 FALSE #> Petal.Length  0.9844532 -0.1133177 FALSE #> Petal.Width   0.9666608 -0.2314771 FALSE #> Sepal.Width  -0.3298748  0.4616406  TRUE  ## Get eigenvalues get_eigenvalues(X) #>    eigenvalues  variance cumulative #> F1   2.7735436 93.410174   93.41017 #> F2   0.1956657  6.589826  100.00000","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict New Coordinates \u2014 predict","title":"Predict New Coordinates \u2014 predict","text":"Predict projection new individuals/rows variables/columns.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict New Coordinates \u2014 predict","text":"","code":"# S4 method for CA predict(object, newdata, margin = 1)  # S4 method for PCA predict(object, newdata, margin = 1)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict New Coordinates \u2014 predict","text":"object CA PCA object. newdata object supplementary points coercible matrix compute principal coordinates. margin length-one numeric vector giving subscript data predicted: 1 indicates individuals/rows (default), 2 indicates variables/columns.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict New Coordinates \u2014 predict","text":"data.frame coordinates.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict New Coordinates \u2014 predict","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict New Coordinates \u2014 predict","text":"","code":"## Create a matrix A <- matrix(data = sample(1:10, 100, TRUE), nrow = 10, ncol = 10)  ## Compute correspondence analysis X <- ca(A, sup_row = 8:10, sup_col = 7:10)  ## Predict new row coordinates Y <- matrix(data = sample(1:10, 120, TRUE), nrow = 20, ncol = 6) predict(X, Y, margin = 1) #>             F1          F2          F3          F4          F5 #> 1  -0.44396581 -0.50960821  0.44521328 -0.09770053  0.19779361 #> 2   0.19836692  0.28394335 -0.08031012 -0.51922149  0.28705168 #> 3  -0.14828872 -0.07140818  0.68064737  0.26048664  0.08963952 #> 4   0.04632205  0.40020759  0.15806088  0.03129221 -0.12639647 #> 5   0.33755045  0.17157959 -0.01355668 -0.20094255  0.15179069 #> 6   0.21722785  0.04239695  0.24485768 -0.06068072  0.31685301 #> 7   0.45136318  0.26013909  0.24718420  0.33535267  0.34573930 #> 8  -0.05059758 -0.18276555  0.14466080  0.16930475  0.17045871 #> 9   0.32840651  0.21439863  0.65551072 -0.07000846 -0.19508160 #> 10  0.15328381 -0.06551832 -0.29128753 -0.30113902 -0.06946447 #> 11 -0.02630774  0.18617672  0.28477312  0.21727088  0.06039769 #> 12  0.33902976  0.23489949 -0.06362375  0.01758543 -0.29224505 #> 13  0.27388911  0.11625301  0.50640086  0.06492469  0.24160926 #> 14  0.15696908 -0.11715021  0.38554698 -0.01077783  0.08172497 #> 15  0.21872640 -0.08520612  0.08375413  0.49815924 -0.22868599 #> 16 -0.16272667  0.12336792  0.42885241  0.06363075 -0.07825940 #> 17  0.26248106  0.33507917  0.20327736  0.32884948  0.37402828 #> 18  0.24615051  0.40284117  0.49777530 -0.06869328  0.26444720 #> 19  0.42466799  0.07774676  0.42707271  0.26049874  0.07735685 #> 20 -0.16884380  0.22022691  0.36517303  0.15716462 -0.03671480  ## Predict new column coordinates Z <- matrix(data = sample(1:10, 140, TRUE), nrow = 7, ncol = 20) predict(X, Z, margin = 2) #>               F1          F2          F3          F4           F5 #> 1  -0.1018982365  0.29882807 -0.27140920 -0.18039316  0.052531985 #> 2   0.2940803070  0.34894911 -0.19760172 -0.31854592  0.147804092 #> 3  -0.0866274641 -0.04704703  0.36507461  0.16909461  0.288678190 #> 4   0.2264308353 -0.45054827 -0.14049575 -0.34590057  0.389833718 #> 5   0.0151806240  0.39716068 -0.12193430  0.31923682  0.279931589 #> 6  -0.2636794882  0.13111318 -0.59403188 -0.17977473  0.295699739 #> 7   0.2120152064 -0.20202350  0.39028813 -0.28120128  0.470056421 #> 8  -0.2960293331  0.27368247  0.20664072  0.26069896 -0.212168230 #> 9  -0.1205503901  0.24424172  0.02458879 -0.31233567  0.368516988 #> 10 -0.2653849039 -0.02381985 -0.32195073  0.17872148 -0.008980461 #> 11 -0.0721349372 -0.08015807 -0.22080949  0.18964535 -0.013628829 #> 12 -0.1374204174  0.04208102 -0.23254432 -0.22256464  0.142539719 #> 13 -0.1385523930 -0.04779562 -0.43994198 -0.05507586  0.303967597 #> 14  0.2118293055  0.35526431  0.03897595 -0.24048200 -0.069765068 #> 15  0.1622483149  0.04367921  0.26549873  0.58227678 -0.112120438 #> 16 -0.2645058157 -0.13938609 -0.60611914 -0.14292183  0.317423370 #> 17 -0.0003190798 -0.49058572 -0.30606841 -0.10380016  0.390737894 #> 18 -0.1525116212  0.38692138 -0.10264325  0.11090332 -0.123887174 #> 19  0.3838415162 -0.08328281 -0.17426651  0.10062214  0.434387259 #> 20  0.3058252336 -0.23074533 -0.13948352  0.01718978  0.260282581","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Scree Plot \u2014 screeplot","title":"Scree Plot \u2014 screeplot","text":"Plot eigenvalues (scree plot) variances histogram.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scree Plot \u2014 screeplot","text":"","code":"# S4 method for MultivariateAnalysis screeplot(   x,   eigenvalues = FALSE,   cumulative = FALSE,   labels = TRUE,   limit = 10,   col = \"grey90\",   border = \"grey10\",   col.cumulative = \"red\",   lty.cumulative = \"solid\",   lwd.cumulative = 2,   ... )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scree Plot \u2014 screeplot","text":"x CA PCA object. eigenvalues logical scalar: eigenvalues plotted instead variance/inertia? cumulative logical scalar: cumulative percentages variance plotted? labels logical scalar: text labels drawn top bars? limit integer specifying number top elements displayed. col, border character string specifying bars infilling border colors. col.cumulative specification line color. lty.cumulative specification line type. lwd.cumulative specification line width. ... Extra parameters passed graphics::barplot().","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scree Plot \u2014 screeplot","text":"screeplot() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Scree Plot \u2014 screeplot","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/screeplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scree Plot \u2014 screeplot","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Screeplot screeplot(X)  screeplot(X, cumulative = TRUE)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Parts of an Object \u2014 subset","title":"Extract Parts of an Object \u2014 subset","text":"Operators acting objects extract parts.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Parts of an Object \u2014 subset","text":"","code":"# S4 method for CA,ANY,missing [[(x, i)  # S4 method for PCA,ANY,missing [[(x, i)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Parts of an Object \u2014 subset","text":"x object extract element(s) replace element(s). character string specifying elements extract. unambiguous substring can given (see details).","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract Parts of an Object \u2014 subset","text":"list.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Parts of an Object \u2014 subset","text":"\"data\", returns list following elements: data numeric matrix raw data. mean numeric vector giving variables means (PCA). sd numeric vector giving variables standard deviations (PCA). \"rows\", returns list following elements: coord numeric matrix rows/individuals coordinates. cos2 numeric matrix rows/individuals squared cosine. masses numeric vector giving rows masses/individual weights. sup logical vector specifying whether point supplementary observation . \"columns\", returns list following elements: coord numeric matrix columns/variables coordinates. cor numeric matrix correlation variables dimensions (PCA). cos2 numeric matrix columns/variables squared cosine. masses numeric vector giving columns masses/variable weights. sup logical vector specifying whether point supplementary observation . \"eigenvalues\", returns numeric vector eigenvalues.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Parts of an Object \u2014 subset","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Parts of an Object \u2014 subset","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 8:10, sup_col = 1) #> 1 qualitative variable was removed: Species.  ## Get results for the individuals X[[\"rows\"]] #> $coordinates #>             F1           F2 #> 1   -2.1799607  0.107860619 #> 2   -1.6996139 -0.926588874 #> 3   -1.9288393 -0.527485410 #> 4   -1.7585967 -0.705022642 #> 5   -2.2760301  0.314750517 #> 6   -2.2841520  1.069266219 #> 7   -1.9994783 -0.054120776 #> 11  -2.3350128  0.536316749 #> 12  -2.0097181 -0.069676614 #> 13  -1.7840271 -0.971497378 #> 14  -1.8952870 -1.015526377 #> 15  -2.7344808  1.112957446 #> 16  -2.8386721  2.074363046 #> 17  -2.4324986  1.010560888 #> 18  -2.0955476  0.152769123 #> 19  -2.2724958  0.817467817 #> 20  -2.3466691  0.788115151 #> 21  -1.9726315 -0.055000281 #> 22  -2.1661866  0.626133757 #> 23  -2.4243767  0.256045186 #> 24  -1.6233228 -0.127164668 #> 25  -1.8984582 -0.025647615 #> 26  -1.6254407 -0.897236208 #> 27  -1.8408919  0.020140394 #> 28  -2.1428741  0.122536952 #> 29  -2.0838914 -0.099029280 #> 30  -1.8175794 -0.483456411 #> 31  -1.7215100 -0.690346310 #> 32  -1.8779785  0.005464061 #> 33  -2.8037034  1.318967839 #> 34  -2.8524463  1.556089908 #> 35  -1.7585967 -0.705022642 #> 36  -1.9659259 -0.542161743 #> 37  -2.2170474  0.093184286 #> 38  -2.3604432  0.269842013 #> 39  -1.7367006 -0.941265207 #> 40  -2.0468047 -0.084352947 #> 41  -2.1326343  0.138092790 #> 42  -0.9798020 -2.344585993 #> 43  -1.9288393 -0.527485410 #> 44  -1.7681350  0.316847300 #> 45  -2.1139094  0.891728987 #> 46  -1.6152008 -0.881680370 #> 47  -2.3939955  0.757882980 #> 48  -1.8917527 -0.512809077 #> 49  -2.3350128  0.536316749 #> 50  -1.9878220 -0.305919178 #> 51   0.3450638  0.510411957 #> 52   0.3553036  0.525967795 #> 53   0.5997195  0.377783228 #> 54   0.8656684 -1.499239964 #> 55   0.7766677 -0.286915466 #> 56   0.5707548 -0.391408807 #> 57   0.4178206  0.807118863 #> 58   0.2567533 -1.529809908 #> 59   0.5117721 -0.169842576 #> 60   0.5287175 -0.641448199 #> 61   0.7152040 -2.328016836 #> 62   0.4361824  0.068158999 #> 63   0.7084985 -1.840855375 #> 64   0.6332719 -0.110257739 #> 65   0.1409057 -0.316605905 #> 66   0.3298732  0.259493060 #> 67   0.5474423  0.112187998 #> 68   0.2652383 -0.791729549 #> 69   1.3159972 -1.542931190 #> 70   0.4676168 -1.189953508 #> 71   0.7198029  0.704722305 #> 72   0.3853216 -0.464790472 #> 73   1.1761357 -0.863556163 #> 74   0.5605150 -0.406964645 #> 75   0.4005122 -0.213871574 #> 76   0.4259426  0.052603161 #> 77   0.7664279 -0.302471304 #> 78   0.9017017  0.275386670 #> 79   0.6435117 -0.094701901 #> 80   0.1387878 -1.086677445 #> 81   0.5265996 -1.411519740 #> 82   0.4050998 -1.471104576 #> 83   0.3598912 -0.731265207 #> 84   1.1425834 -0.375515196 #> 85   0.5474423  0.112187998 #> 86   0.2475780  0.984656096 #> 87   0.5255462  0.348430562 #> 88   1.0140150 -1.440534633 #> 89   0.2302695 -0.036334342 #> 90   0.6735297 -1.085460167 #> 91   0.6413938 -0.864773441 #> 92   0.5001159  0.081955827 #> 93   0.4930472 -0.923478773 #> 94   0.3528226 -1.736699807 #> 95   0.5555643 -0.642327704 #> 96   0.1829431 -0.066566513 #> 97   0.3634255 -0.228547907 #> 98   0.4005122 -0.213871574 #> 99   0.1338371 -1.322040504 #> 100  0.4224083 -0.450114139 #> 101  1.6596649  1.402087727 #> 102  1.3958227 -0.240789684 #> 103  1.5731339  0.587107682 #> 104  1.3047041  0.201463273 #> 105  1.6204604  0.617339853 #> 106  1.8327404  0.689842013 #> 107  1.1966153 -0.832444487 #> 108  1.5643105  0.304197604 #> 109  1.7631548 -0.596743655 #> 110  1.4085435  2.037433755 #> 111  0.9998890  0.838568312 #> 112  1.4699960 -0.211437019 #> 113  1.4247874  0.528402351 #> 114  1.6352879 -0.624337311 #> 115  1.7218189  0.190642734 #> 116  1.3273016  1.002646490 #> 117  1.1715481  0.393676839 #> 118  1.1856853  2.404546038 #> 119  2.4971040 -0.003871575 #> 120  1.5014304 -1.469549526 #> 121  1.4756482  1.061351821 #> 122  1.3099931 -0.018343948 #> 123  1.9775527  0.245830045 #> 124  1.2372363 -0.315050854 #> 125  1.2107526  1.178424712 #> 126  1.1648425  0.880838301 #> 127  1.1040803 -0.122837289 #> 128  0.9490282  0.305618841 #> 129  1.6540127  0.129298887 #> 130  1.1139818  0.347888830 #> 131  1.6706197  0.112863543 #> 132  0.9055992  2.270700031 #> 133  1.7384258  0.174207390 #> 134  0.9621009 -0.213533802 #> 135  1.2552597 -0.598840438 #> 136  1.8161334  0.706277356 #> 137  1.3308359  1.505363789 #> 138  1.0754787  0.600566737 #> 139  0.9119416  0.290942508 #> 140  1.2916314  0.720615916 #> 141  1.6190440  0.884694094 #> 142  1.3491977  0.766403925 #> 143  1.3958227 -0.240789684 #> 144  1.5498214  1.090704487 #> 145  1.5484050  1.358058728 #> 146  1.4823537  0.574190360 #> 147  1.5508748 -0.669245814 #> 148  1.2291144  0.439464848 #> 149  1.1722495  1.431102620 #> 150  1.0232015  0.334971507 #> 8   -2.0468047 -0.084352947 #> 9   -1.6035446 -1.133478772 #> 10  -1.8430098 -0.749931146 #>  #> $contributions #>              F1           F2 #> 1   1.453343684 0.0106993487 #> 2   0.883428800 0.7895973615 #> 3   1.137792525 0.2558894786 #> 4   0.945809064 0.4571278240 #> 5   1.584261926 0.0911096611 #> 6   1.595588876 1.0514853344 #> 7   1.222656219 0.0026937641 #> 11  1.667437334 0.2645295822 #> 12  1.235211341 0.0044648369 #> 13  0.973360803 0.8679901399 #> 14  1.098552806 0.9484489111 #> 15  2.286760836 1.1391702764 #> 16  2.464344658 3.9573190026 #> 17  1.809573309 0.9391965689 #> 18  1.342969212 0.0214636064 #> 19  1.579345535 0.6145721192 #> 20  1.684126390 0.5712298316 #> 21  1.190043653 0.0027820270 #> 22  1.435035728 0.3605502185 #> 23  1.797509409 0.0602927030 #> 24  0.805899240 0.0148718312 #> 25  1.102232091 0.0006049584 #> 26  0.808003463 0.7403636858 #> 27  1.036400344 0.0003730503 #> 28  1.404314222 0.0138091094 #> 29  1.328070529 0.0090190079 #> 30  1.010317225 0.2149543180 #> 31  0.906337800 0.4382940161 #> 32  1.078579659 0.0000274576 #> 33  2.404003572 1.5999261560 #> 34  2.488318246 2.2269009410 #> 35  0.945809064 0.4571278240 #> 36  1.181966834 0.2703268996 #> 37  1.503214416 0.0079857717 #> 38  1.703954895 0.0669654302 #> 39  0.922403375 0.8148084750 #> 40  1.281220300 0.0065438305 #> 41  1.390925123 0.0175377362 #> 42  0.293594279 5.0554970160 #> 43  1.137792525 0.2558894786 #> 44  0.956096741 0.0923276004 #> 45  1.366607256 0.7313028984 #> 46  0.797855133 0.7149141115 #> 47  1.752740637 0.5282455348 #> 48  1.094459487 0.2418482414 #> 49  1.667437334 0.2645295822 #> 50  1.208442464 0.0860686388 #> 51  0.036414113 0.2395924925 #> 52  0.038607369 0.2544191706 #> 53  0.109993587 0.1312553072 #> 54  0.229178638 2.0671587361 #> 55  0.184476671 0.0757075942 #> 56  0.099625421 0.1408940695 #> 57  0.053388873 0.5991099343 #> 58  0.020160574 2.1523181354 #> 59  0.080098468 0.0265292309 #> 60  0.085490582 0.3784031185 #> 61  0.156433881 4.9842952461 #> 62  0.058184494 0.0042724593 #> 63  0.153514287 3.1165268007 #> 64  0.122645427 0.0111802030 #> 65  0.006071949 0.0921869714 #> 66  0.033278603 0.0619274246 #> 67  0.091653220 0.0115750885 #> 68  0.021515104 0.5764813136 #> 69  0.529640167 2.1893976274 #> 70  0.066873073 1.3022414468 #> 71  0.158452140 0.4567384375 #> 72  0.045406471 0.1986762522 #> 73  0.423044289 0.6858240370 #> 74  0.096082758 0.1523157768 #> 75  0.049057166 0.0420666239 #> 76  0.055484680 0.0025448091 #> 77  0.179644343 0.0841394918 #> 78  0.248654774 0.0697456870 #> 79  0.126643778 0.0082480050 #> 80  0.005890792 1.0860075196 #> 81  0.084807053 1.8323373666 #> 82  0.050187444 1.9903003497 #> 83  0.039610792 0.4917918728 #> 84  0.399251696 0.1296840484 #> 85  0.091653220 0.0115750885 #> 86  0.018745415 0.8916628530 #> 87  0.084468126 0.1116513580 #> 88  0.314455886 1.9084418605 #> 89  0.016216010 0.0012141326 #> 90  0.138734505 1.0835758275 #> 91  0.125811545 0.6877588893 #> 92  0.076491334 0.0061771934 #> 93  0.074344362 0.7843056805 #> 94  0.038070081 2.7738376469 #> 95  0.094392957 0.3794415052 #> 96  0.010235356 0.0040751457 #> 97  0.040392607 0.0480381224 #> 98  0.049057166 0.0420666239 #> 99  0.005478023 1.6073892100 #> 100 0.054567719 0.1863274482 #> 101 0.842387320 1.8079312085 #> 102 0.595842510 0.0533221045 #> 103 0.756837054 0.3170056865 #> 104 0.520589025 0.0373270182 #> 105 0.803059702 0.3504936509 #> 106 1.027242319 0.4376539041 #> 107 0.437905211 0.6372973156 #> 108 0.748370960 0.0851026540 #> 109 0.950718311 0.3274968644 #> 110 0.606752411 3.8176711884 #> 111 0.305755696 0.6467082790 #> 112 0.660850565 0.0411143642 #> 113 0.620827724 0.2567798899 #> 114 0.817823172 0.3584842707 #> 115 0.906663024 0.0334250480 #> 116 0.538778450 0.9245431850 #> 117 0.419750477 0.1425316314 #> 118 0.429941982 5.3173802664 #> 119 1.906971687 0.0000137850 #> 120 0.689416059 1.9860948268 #> 121 0.665942333 1.0359773621 #> 122 0.524818373 0.0003094690 #> 123 1.195988781 0.0555778122 #> 124 0.468140574 0.0912836189 #> 125 0.448313474 1.2771303006 #> 126 0.414959229 0.7135491729 #> 127 0.372796904 0.0138768844 #> 128 0.275441407 0.0858997255 #> 129 0.836659379 0.0153752114 #> 130 0.379513426 0.1113044416 #> 131 0.853544550 0.0117149082 #> 132 0.250808937 4.7418854471 #> 133 0.924236944 0.0279103111 #> 134 0.283081973 0.0419338551 #> 135 0.481879173 0.3298023644 #> 136 1.008710388 0.4587563529 #> 137 0.541651566 2.0840803395 #> 138 0.353732210 0.3317065721 #> 139 0.254334345 0.0778477176 #> 140 0.510209050 0.4775724073 #> 141 0.801656426 0.7198098450 #> 142 0.556701191 0.5401905310 #> 143 0.595842510 0.0533221045 #> 144 0.734571902 1.0940715530 #> 145 0.733229824 1.6961670731 #> 146 0.672008322 0.3032098599 #> 147 0.735570728 0.4119104916 #> 148 0.462014453 0.1776150699 #> 149 0.420253277 1.8835323329 #> 150 0.320179346 0.1031922926 #>  #> $cos2 #>              F1           F2 #> 1   0.997537883 2.442069e-03 #> 2   0.770275213 2.289389e-01 #> 3   0.929285621 6.949892e-02 #> 4   0.861533164 1.384668e-01 #> 5   0.981140144 1.876324e-02 #> 6   0.820250332 1.797497e-01 #> 7   0.997006440 7.304536e-04 #> 11  0.948921305 5.006041e-02 #> 12  0.997254638 1.198698e-03 #> 13  0.771015900 2.286355e-01 #> 14  0.775718588 2.227084e-01 #> 15  0.857879117 1.421129e-01 #> 16  0.651874030 3.480990e-01 #> 17  0.849498051 1.466163e-01 #> 18  0.993184069 5.278443e-03 #> 19  0.884472859 1.144510e-01 #> 20  0.898639841 1.013588e-01 #> 21  0.995527356 7.739112e-04 #> 22  0.920782450 7.693067e-02 #> 23  0.985634960 1.099383e-02 #> 24  0.983278582 6.033923e-03 #> 25  0.988580295 1.804280e-04 #> 26  0.766287189 2.334871e-01 #> 27  0.996619952 1.192913e-04 #> 28  0.996181238 3.257461e-03 #> 29  0.997744694 2.253183e-03 #> 30  0.933168940 6.602192e-02 #> 31  0.861052131 1.384664e-01 #> 32  0.993951874 8.414246e-06 #> 33  0.814686002 1.802995e-01 #> 34  0.769931500 2.291324e-01 #> 35  0.861533164 1.384668e-01 #> 36  0.926583440 7.047060e-02 #> 37  0.998037699 1.763122e-03 #> 38  0.984800233 1.287005e-02 #> 39  0.771150422 2.265235e-01 #> 40  0.997960094 1.694966e-03 #> 41  0.992507874 4.161436e-03 #> 42  0.146925900 8.413062e-01 #> 43  0.929285621 6.949892e-02 #> 44  0.946393484 3.039071e-02 #> 45  0.848161656 1.509286e-01 #> 46  0.765582021 2.281188e-01 #> 47  0.906464208 9.084652e-02 #> 48  0.931348538 6.843763e-02 #> 49  0.948921305 5.006041e-02 #> 50  0.976803096 2.313478e-02 #> 51  0.277995466 6.082479e-01 #> 52  0.311683132 6.830184e-01 #> 53  0.664146963 2.635440e-01 #> 54  0.249361414 7.479428e-01 #> 55  0.878241702 1.198537e-01 #> 56  0.637260992 2.996949e-01 #> 57  0.210729871 7.863598e-01 #> 58  0.027308118 9.694709e-01 #> 59  0.758623302 8.355378e-02 #> 60  0.387926000 5.709851e-01 #> 61  0.086194377 9.132535e-01 #> 62  0.926162213 2.261503e-02 #> 63  0.128022840 8.642691e-01 #> 64  0.898327863 2.723157e-02 #> 65  0.130964651 6.612032e-01 #> 66  0.593595505 3.673229e-01 #> 67  0.958380776 4.024891e-02 #> 68  0.090772461 8.087892e-01 #> 69  0.420435646 5.779399e-01 #> 70  0.133260226 8.629393e-01 #> 71  0.504882110 4.839482e-01 #> 72  0.406578554 5.915782e-01 #> 73  0.645293645 3.478754e-01 #> 74  0.519247169 2.737241e-01 #> 75  0.734870489 2.095489e-01 #> 76  0.958138539 1.461335e-02 #> 77  0.812191218 1.264981e-01 #> 78  0.912896321 8.514938e-02 #> 79  0.978657862 2.119510e-02 #> 80  0.016044824 9.836341e-01 #> 81  0.122144323 8.775790e-01 #> 82  0.070309187 9.272035e-01 #> 83  0.194938448 8.048312e-01 #> 84  0.891022209 9.624269e-02 #> 85  0.958380776 4.024891e-02 #> 86  0.059437245 9.401643e-01 #> 87  0.672034771 2.953945e-01 #> 88  0.330736771 6.674844e-01 #> 89  0.946744678 2.357189e-02 #> 90  0.277285564 7.201808e-01 #> 91  0.342463747 6.225436e-01 #> 92  0.891508689 2.394109e-02 #> 93  0.221487022 7.770072e-01 #> 94  0.039508517 9.572545e-01 #> 95  0.426831711 5.705601e-01 #> 96  0.490691231 6.496627e-02 #> 97  0.698527791 2.762532e-01 #> 98  0.734870489 2.095489e-01 #> 99  0.009680752 9.445952e-01 #> 100 0.467996371 5.314018e-01 #> 101 0.576108690 4.111629e-01 #> 102 0.961696898 2.861893e-02 #> 103 0.877177165 1.221776e-01 #> 104 0.958249977 2.284791e-02 #> 105 0.870879776 1.263950e-01 #> 106 0.851672810 1.206619e-01 #> 107 0.657810722 3.183482e-01 #> 108 0.886186912 3.351128e-02 #> 109 0.885499558 1.014339e-01 #> 110 0.321916434 6.735496e-01 #> 111 0.577480507 4.061726e-01 #> 112 0.978244776 2.023845e-02 #> 113 0.873487112 1.201395e-01 #> 114 0.848034409 1.236129e-01 #> 115 0.885718143 1.085828e-02 #> 116 0.607788840 3.468240e-01 #> 117 0.884632536 9.989009e-02 #> 118 0.191705611 7.884282e-01 #> 119 0.992440547 2.385649e-06 #> 120 0.508966080 4.875811e-01 #> 121 0.651305390 3.369285e-01 #> 122 0.949464886 1.861772e-04 #> 123 0.939398567 1.451656e-02 #> 124 0.929464957 6.026831e-02 #> 125 0.513528277 4.864713e-01 #> 126 0.594651369 3.400324e-01 #> 127 0.968111697 1.198353e-02 #> 128 0.898455967 9.317487e-02 #> 129 0.989867285 6.049073e-03 #> 130 0.787377903 7.679059e-02 #> 131 0.967682818 4.416571e-03 #> 132 0.132869218 8.353566e-01 #> 133 0.977445867 9.815525e-03 #> 134 0.899008289 4.428491e-02 #> 135 0.716813201 1.631402e-01 #> 136 0.867820065 1.312456e-01 #> 137 0.428750425 5.485781e-01 #> 138 0.748409494 2.333771e-01 #> 139 0.890507966 9.063963e-02 #> 140 0.754859896 2.349616e-01 #> 141 0.744129523 2.221864e-01 #> 142 0.695525700 2.244282e-01 #> 143 0.961696898 2.861893e-02 #> 144 0.666268263 3.299894e-01 #> 145 0.547542725 4.211977e-01 #> 146 0.810397292 1.215923e-01 #> 147 0.830568366 1.546654e-01 #> 148 0.874671814 1.118173e-01 #> 149 0.391519529 5.835188e-01 #> 150 0.903130625 9.679300e-02 #> 8   0.997960094 1.694966e-03 #> 9   0.666045138 3.327885e-01 #> 10  0.856123843 1.417503e-01 #>  #> $masses #>           1           2           3           4           5           6  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>           7          14          15          16          17          18  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          19          20          21          22          23          24  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          25          26          27          28          29          30  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          31          32          33          34          35          36  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          37          38          39          40          41          42  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          43          44          45          46          47          48  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          49          50          51          52          53          54  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          55          56          57          58          59          60  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          61          62          63          64          65          66  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          67          68          69          70          71          72  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          73          74          75          76          77          78  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          79          80          81          82          83          84  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          85          86          87          88          89          90  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          91          92          93          94          95          96  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>          97          98          99         100         101         102  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         103         104         105         106         107         108  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         109         110         111         112         113         114  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         115         116         117         118         119         120  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         121         122         123         124         125         126  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         127         128         129         130         131         132  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         133         134         135         136         137         138  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         139         140         141         142         143         144  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>         145         146         147         148         149         150  #> 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721 0.006802721  #>           8           9          10  #> 0.006802721 0.006802721 0.006802721  #>  #> $supplement #>   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #>  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [145] FALSE FALSE FALSE  TRUE  TRUE  TRUE #>","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Object Summaries \u2014 summary","title":"Object Summaries \u2014 summary","text":"Provides summary results multivariate data analysis.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Object Summaries \u2014 summary","text":"","code":"# S4 method for CA summary(object, margin = 1, active = TRUE, sup = TRUE, rank = 3)  # S4 method for PCA summary(object, margin = 1, active = TRUE, sup = TRUE, rank = 3)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Object Summaries \u2014 summary","text":"object CA PCA object. margin length-one numeric vector giving subscript data summarized: 1 indicates individuals/rows (default), 2 indicates variables/columns. active logical scalar: active observations summarized? sup logical scalar: supplementary observations summarized? rank integer value specifying maximal number components kept results.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/summary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Object Summaries \u2014 summary","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Object Summaries \u2014 summary","text":"","code":"## Data from Lebart et al. 2006, p. 170-172 data(\"colours\")  ## Compute correspondence analysis X <- ca(colours)  ## Rows summary summary(X, margin = 1) #> --- Correspondence Analysis (CA) ------------------------------------------------ #>  #> Eigenvalues: #>    eigenvalues variance cumulative #> F1       0.209   89.373     89.373 #> F2       0.022    9.515     98.888 #> F3       0.003    1.112    100.000 #>  #> Active rows: #>          inertia CA1_coord CA1_contrib CA1_cos2 CA2_coord CA2_contrib CA2_cos2 #> marron    93.086    -0.492      43.116    0.967    -0.088      13.042    0.031 #> noisette  13.089    -0.213       3.401    0.542     0.167      19.804    0.336 #> vert      16.085     0.162       1.355    0.176     0.339      55.910    0.773 #> bleu     111.337     0.547      52.128    0.977    -0.083      11.244    0.022 #>          CA3_coord CA3_contrib CA3_cos2 #> marron       0.022       6.680    0.002 #> noisette    -0.101      61.086    0.121 #> vert         0.088      31.925    0.052 #> bleu        -0.005       0.310    0.000  ## Columns summary summary(X, margin = 2) #> --- Correspondence Analysis (CA) ------------------------------------------------ #>  #> Eigenvalues: #>    eigenvalues variance cumulative #> F1       0.209   89.373     89.373 #> F2       0.022    9.515     98.888 #> F3       0.003    1.112    100.000 #>  #> Active columns: #>         inertia CA1_coord CA1_contrib CA1_cos2 CA2_coord CA2_contrib CA2_cos2 #> brun     55.425    -0.505      22.246    0.838    -0.215      37.877    0.152 #> chatain  12.284    -0.148       5.086    0.864     0.033       2.319    0.042 #> roux     15.095    -0.130       0.964    0.133     0.320      55.131    0.812 #> blond   150.793     0.835      71.704    0.993    -0.070       4.673    0.007 #>         CA3_coord CA3_contrib CA3_cos2 #> brun        0.056      21.633    0.010 #> chatain    -0.049      44.284    0.094 #> roux        0.083      31.913    0.055 #> blond       0.016       2.171    0.000","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy Coordinates \u2014 tidy","title":"Tidy Coordinates \u2014 tidy","text":"Tidy Coordinates","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy Coordinates \u2014 tidy","text":"","code":"tidy(x, ...)  augment(x, ...)  # S4 method for MultivariateAnalysis augment(x, margin = 1, axes = c(1, 2), principal = TRUE, ...)  # S4 method for MultivariateAnalysis tidy(x, margin = 1, principal = TRUE, ...)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy Coordinates \u2014 tidy","text":"x CA PCA object. ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. principal logical scalar: principal coordinates returned? FALSE, standard coordinates returned.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tidy Coordinates \u2014 tidy","text":"tidy() returns long data.frame following columns: label Row/column names original data. component Component. supplementary Whether observation active supplementary. coordinate Coordinates. contribution Contributions definition components. cos2 \\(cos^2\\). augment() returns wide data.frame row/column coordinates along axes following columns: label Row/column names original data. supplementary Whether observation active supplementary. mass Weight/mass observation. sum Sum squared coordinates along axes. contribution Joint contributions definition axes. cos2 Joint \\(cos^2\\) along axes.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tidy Coordinates \u2014 tidy","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/tidy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy Coordinates \u2014 tidy","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE, sup_row = 5:10) #> 1 qualitative variable was removed: Species.  ## Get row principal coordinates head(get_coordinates(X, margin = 1, principal = TRUE)) #>           F1         F2          F3  .sup #> 1  -2.385846  0.5084067 -0.12383101 FALSE #> 2  -2.215654 -0.6515155 -0.23313750 FALSE #> 3  -2.496566 -0.3169280  0.04974232 FALSE #> 4  -2.433607 -0.5744432  0.09690301 FALSE #> 11 -2.279483  1.0742447 -0.26629760 FALSE #> 12 -2.450871  0.1591839  0.09988982 FALSE  ## Get row standard coordinates head(get_coordinates(X, margin = 1, principal = FALSE)) #>          F1         F2         F3  .sup #> 1 -1.403075  0.5268995 -0.3142569 FALSE #> 2 -1.302987 -0.6752138 -0.5916536 FALSE #> 3 -1.468187 -0.3284560  0.1262355 FALSE #> 4 -1.431162 -0.5953380  0.2459193 FALSE #> 5 -1.340525  1.1133193 -0.6758069 FALSE #> 6 -1.441315  0.1649741  0.2534992 FALSE  ## Tidy principal coordinates head(tidy(X, margin = 1)) #>   label component supplementary coordinate contribution        cos2 #> 1     1        F1         FALSE -2.3858463   1.36709655 0.954033073 #> 2     1        F2         FALSE  0.5084067   0.19279380 0.043321256 #> 3     1        F3         FALSE -0.1238310   0.06858151 0.002570025 #> 4    10        F1          TRUE -2.3173283           NA 0.953213276 #> 5    10        F2          TRUE -0.4448514           NA 0.035127301 #> 6    10        F3          TRUE -0.2525218           NA 0.011319124 head(tidy(X, margin = 2)) #>          label component supplementary coordinate contribution         cos2 #> 1 Petal.Length        F1         FALSE 0.99121597  33.97921607 0.9825091061 #> 2 Petal.Length        F2         FALSE 0.01655680   0.02944324 0.0002741275 #> 3 Petal.Length        F3         FALSE 0.05477927   1.93260521 0.0030007681 #> 4  Petal.Width        F1         FALSE 0.96350955  32.10619376 0.9283506618 #> 5  Petal.Width        F2         FALSE 0.05756108   0.35586957 0.0033132775 #> 6  Petal.Width        F3         FALSE 0.24948796  40.08758618 0.0622442430  head(augment(X, margin = 1, axes = c(1, 2))) #>          F1         F2 label supplementary        mass      sum contribution #> 1 -2.385846  0.5084067     1         FALSE 0.006944444 5.950740     4.132458 #> 2 -2.215654 -0.6515155     2         FALSE 0.006944444 5.333593     3.703884 #> 3 -2.496566 -0.3169280     3         FALSE 0.006944444 6.333284     4.398114 #> 4 -2.433607 -0.5744432     4         FALSE 0.006944444 6.252426     4.341962 #> 5 -2.279483  1.0742447    11         FALSE          NA 6.350046     4.409754 #> 6 -2.450871  0.1591839    12         FALSE          NA 6.032107     4.188963 #>        cos2 #> 1 0.9973543 #> 2 0.9880287 #> 3 0.9995093 #> 4 0.9977072 #> 5 0.9889258 #> 6 0.9951651 head(augment(X, margin = 2, axes = c(1, 2))) #>           F1         F2        label supplementary mass       sum contribution #> 1  0.8875399 0.36087785 Sepal.Length         FALSE    1 0.9179599     91.79599 #> 2 -0.4392190 0.89286998  Sepal.Width         FALSE    1 0.9901302     99.01302 #> 3  0.9912160 0.01655680 Petal.Length         FALSE    1 0.9827832     98.27832 #> 4  0.9635096 0.05756108  Petal.Width         FALSE    1 0.9316639     93.16639 #>        cos2 #> 1 0.9179599 #> 2 0.9901302 #> 3 0.9827832 #> 4 0.9316639","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Contributions and cos2 \u2014 viz_contributions","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"Plots contributions histogram \\(cos^2\\) scatterplot.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"","code":"viz_contributions(x, ...)  viz_cos2(x, ...)  # S4 method for MultivariateAnalysis viz_contributions(   x,   margin = 2,   axes = 1,   sort = TRUE,   decreasing = TRUE,   limit = 10,   horiz = FALSE,   col = \"grey90\",   border = \"grey10\",   ... )  # S4 method for MultivariateAnalysis viz_cos2(   x,   margin = 2,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   sort = TRUE,   decreasing = TRUE,   limit = 10,   horiz = FALSE,   col = \"grey90\",   border = \"grey10\",   ... )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"x CA PCA object. ... Extra parameters passed graphics::barplot(). margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-one numeric vector giving dimensions plotted. sort logical scalar: data sorted? decreasing logical scalar: sort order decreasing? used sort TRUE. limit integer specifying number top elements displayed. horiz logical scalar: bars drawn horizontally first bottom? col, border character string specifying bars infilling border colors. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted?","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"viz_contributions() viz_cos2() called side-effects: result graphic displayed. Invisibly return x.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_contributions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Contributions and cos2 \u2014 viz_contributions","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Get row contributions head(get_contributions(X, margin = 1)) #>          F1         F2          F3 #> 1 1.1715796 0.16806554 0.074085470 #> 2 0.9891845 0.33146674 0.250034006 #> 3 1.2768164 0.08526419 0.008875320 #> 4 1.2077372 0.26029781 0.037858004 #> 5 1.3046313 0.30516562 0.001125175 #> 6 0.9841236 1.61748779 0.003303827  ## Plot contributions viz_contributions(X)   ## Plot cos2 viz_cos2(X)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Individuals Factor Map \u2014 viz_individuals","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"Plots row/individual principal coordinates.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"","code":"viz_individuals(x, ...)  viz_rows(x, ...)  # S4 method for MultivariateAnalysis viz_rows(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapCA viz_rows(x, axes = c(1, 2), ...)  # S4 method for PCA viz_individuals(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"x CA PCA object. ... graphical parameters (see details). axes length-two numeric vector giving dimensions plotted. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels logical scalar: labels drawn? highlight vector specifying information highlighted. NULL (default), highlighting applied. mapped least one graphical parameters explicitly specified (see examples). single character string passed, must one \"observation\", \"mass\", \"sum\", \"contribution\" \"cos2\" (see augment()). unambiguous substring can given. main character string giving main title plot. sub character string giving subtitle plot. panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"viz_*() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"Commonly used graphical parameters : pch vector plotting characters symbols. can either single character integer code one set graphics symbols. cex numerical vector giving amount plotting characters symbols scaled relative default. col colors lines points. Multiple colors can specified point can given color. bg background color open plot symbols given pch = 21:25.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"careful: graphical parameters silently recycled.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_individuals.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Individuals Factor Map \u2014 viz_individuals","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Plot individuals viz_individuals(X, panel.last = graphics::grid())   ## Plot variables viz_variables(X, panel.last = graphics::grid())   ## Graphical parameters ## Continuous values viz_individuals(X, highlight = iris$Petal.Length, pch = 16)  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"))  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"),                 cex = c(1, 2))   viz_variables(X, highlight = \"contribution\",               col = grDevices::hcl.colors(12, \"BluGrn\", rev = TRUE),               lwd = c(1, 5))   ## Discrete values viz_individuals(X, highlight = iris$Species, pch = 21:23)  viz_individuals(X, highlight = iris$Species, pch = 21:23,                 bg = c(\"#004488\", \"#DDAA33\", \"#BB5566\"),                 col = \"black\")   viz_variables(X, highlight = c(\"Petal\", \"Petal\", \"Sepal\", \"Sepal\"),               col = c(\"#EE7733\", \"#0077BB\"),               lty = c(1, 3))","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize Variables Factor Map \u2014 viz_variables","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"Plots column/variable principal coordinates.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"","code":"viz_variables(x, ...)  viz_columns(x, ...)  # S4 method for MultivariateAnalysis viz_columns(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = FALSE,   highlight = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapCA viz_columns(x, axes = c(1, 2), ...)  # S4 method for PCA viz_variables(   x,   axes = c(1, 2),   active = TRUE,   sup = TRUE,   labels = TRUE,   highlight = NULL,   main = NULL,   sub = NULL,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for BootstrapPCA viz_variables(x, axes = c(1, 2), ...)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"x CA PCA object. ... graphical parameters (see details). axes length-two numeric vector giving dimensions plotted. active logical scalar: active observations plotted? sup logical scalar: supplementary observations plotted? labels logical scalar: labels drawn? highlight vector specifying information highlighted. NULL (default), highlighting applied. mapped least one graphical parameters explicitly specified (see examples). single character string passed, must one \"observation\", \"mass\", \"sum\", \"contribution\" \"cos2\" (see augment()). unambiguous substring can given. main character string giving main title plot. sub character string giving subtitle plot. panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"viz_*() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"Commonly used graphical parameters : pch vector plotting characters symbols. can either single character integer code one set graphics symbols. cex numerical vector giving amount plotting characters symbols scaled relative default. lty vector line types. lwd vector line widths. col colors lines points. Multiple colors can specified point can given color. bg background color open plot symbols given pch = 21:25.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"careful: graphical parameters silently recycled.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize Variables Factor Map \u2014 viz_variables","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Plot individuals viz_individuals(X, panel.last = graphics::grid())   ## Plot variables viz_variables(X, panel.last = graphics::grid())   ## Graphical parameters ## Continuous values viz_individuals(X, highlight = iris$Petal.Length, pch = 16)  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"))  viz_individuals(X, highlight = iris$Petal.Length, pch = 16,                 col = grDevices::hcl.colors(12, \"RdPu\"),                 cex = c(1, 2))   viz_variables(X, highlight = \"contribution\",               col = grDevices::hcl.colors(12, \"BluGrn\", rev = TRUE),               lwd = c(1, 5))   ## Discrete values viz_individuals(X, highlight = iris$Species, pch = 21:23)  viz_individuals(X, highlight = iris$Species, pch = 21:23,                 bg = c(\"#004488\", \"#DDAA33\", \"#BB5566\"),                 col = \"black\")   viz_variables(X, highlight = c(\"Petal\", \"Petal\", \"Sepal\", \"Sepal\"),               col = c(\"#EE7733\", \"#0077BB\"),               lty = c(1, 3))","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Envelopes \u2014 viz_wrap","title":"Plot Envelopes \u2014 viz_wrap","text":"Plot Envelopes","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Envelopes \u2014 viz_wrap","text":"","code":"viz_hull(x, ...)  viz_confidence(x, ...)  viz_tolerance(x, ...)  # S4 method for MultivariateAnalysis viz_tolerance(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95, ...)  # S4 method for BootstrapCA viz_tolerance(x, margin = 1, axes = c(1, 2), level = 0.95, ...)  # S4 method for MultivariateAnalysis viz_confidence(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95, ...)  # S4 method for BootstrapCA viz_confidence(x, margin = 1, axes = c(1, 2), level = 0.95, ...)  # S4 method for MultivariateAnalysis viz_hull(x, margin = 1, axes = c(1, 2), group = NULL, ...)  # S4 method for BootstrapCA viz_hull(x, margin = 1, axes = c(1, 2), ...)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Envelopes \u2014 viz_wrap","text":"x object wrap observations (CA PCA object). ... graphical parameters passed graphics::polygon(). margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. group vector specifying group observation belongs . level numeric vector specifying confidence/tolerance level.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Envelopes \u2014 viz_wrap","text":"viz_*()called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Envelopes \u2014 viz_wrap","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/viz_wrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Envelopes \u2014 viz_wrap","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Convex hull coordinates hulls <- wrap_hull(X, margin = 1, group = iris$Species)  ## Confidence ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species,                         level = c(0.68, 0.95))  ## Tolerance ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species, level = 0.95)  ## Plot with convex hulls col <- c(\"#004488\", \"#DDAA33\", \"#BB5566\") viz_rows(X, highlight = iris$Species, col = col) viz_hull(X, group = iris$Species, border = col)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrap Observations \u2014 wrap","title":"Wrap Observations \u2014 wrap","text":"wrap_hull() computes convex hull set observations. wrap_confidence() computes confidence ellipse. wrap_tolerance() computes tolerance ellipse.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrap Observations \u2014 wrap","text":"","code":"wrap_hull(x, ...)  wrap_confidence(x, ...)  wrap_tolerance(x, ...)  # S4 method for MultivariateAnalysis wrap_confidence(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95)  # S4 method for MultivariateAnalysis wrap_tolerance(x, margin = 1, axes = c(1, 2), group = NULL, level = 0.95)  # S4 method for MultivariateAnalysis wrap_hull(x, margin = 1, axes = c(1, 2), group = NULL)","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrap Observations \u2014 wrap","text":"x object wrap observations (CA PCA object). ... Currently used. margin length-one numeric vector giving subscript data returned: 1 indicates individuals/rows (default), 2 indicates variables/columns. axes length-two numeric vector giving dimensions compute results. group vector specifying group observation belongs . level numeric vector specifying confidence/tolerance level.","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrap Observations \u2014 wrap","text":"wrap_*() returns data.frame envelope x y coordinates. extra column named group added specifying group observation belongs .","code":"","package":"dimensio"},{"path":[],"package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Wrap Observations \u2014 wrap","text":"N. Frerebeau","code":"","package":"dimensio"},{"path":"https://packages.tesselle.org/dimensio/reference/wrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrap Observations \u2014 wrap","text":"","code":"## Load data data(\"iris\")  ## Compute principal components analysis X <- pca(iris, scale = TRUE) #> 1 qualitative variable was removed: Species.  ## Convex hull coordinates hulls <- wrap_hull(X, margin = 1, group = iris$Species)  ## Confidence ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species,                         level = c(0.68, 0.95))  ## Tolerance ellipse coordinates conf <- wrap_confidence(X, margin = 1, group = iris$Species, level = 0.95)  ## Plot with convex hulls col <- c(\"#004488\", \"#DDAA33\", \"#BB5566\") viz_rows(X, highlight = iris$Species, col = col) viz_hull(X, group = iris$Species, border = col)","package":"dimensio"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"heterogeneity-and-evenness","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity","what":"Heterogeneity and Evenness","title":"Diversity Measures","text":"Diversity measurement assumes individuals specific taxa equivalent types equally different (Peet 1974). measure diversity can achieved using indices built relative abundance taxa. indices (sometimes referred non-parametric indices) benefit making assumptions underlying distribution taxa abundance: take relative abundances species present species richness account. Peet (1974) refers indices heterogeneity (\\(H\\)). Diversity indices focus one aspect taxa abundance emphasize either richness (weighting towards uncommon taxa) dominance [weighting towards abundant taxa; Magurran (1988)]. Evenness (\\(E\\)) measure evenly individuals distributed across sample.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"shannon-wiener-diversity-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness > Information theory index","what":"Shannon-Wiener diversity index","title":"Diversity Measures","text":"Shannon-Wiener index (Shannon 1948) assumes individuals randomly sampled infinite population taxa represented sample (reflect sample size). main source error arises failure include taxa sample: error increases proportion species discovered sample declines (Peet 1974; Magurran 1988). maximum likelihood estimator (MLE) used relative abundance, known negatively biased sample size. Heterogeneity infinite sample: \\[ H' = - \\sum_{= 1}^{S} p_i \\ln p_i \\] Heterogeneity finite sample: \\[ H' = - \\sum_{= 1}^{S} \\frac{n_i}{N} \\ln \\frac{n_i}{N} \\] Evenness: \\[ E = \\frac{H}{H_{max}} = \\frac{H'}{\\ln S} = - \\sum_{= 1}^{S} p_i \\log_S p_i \\] \\(p_i\\) unknown population, estimate given \\(\\hat{p}_i =\\frac{n_i}{N}\\) (maximum likelihood estimator - MLE). use \\(\\hat{p}_i\\) results biased estimate, Hutcheson (1970) Bowman et al. (1971) suggest use : \\[ \\hat{H}' = - \\sum_{= 1}^{S} \\hat{p}_i \\ln \\hat{p}_i - \\frac{S - 1}{N} + \\frac{1 - \\sum_{= 1}^{S} \\hat{p}_i^{-1}}{12N^2} + \\frac{\\sum_{= 1}^{S} (\\hat{p}_i^{-1} - \\hat{p}_i^{-2})}{12N^3} + \\cdots \\] error rarely significant (Peet 1974), unbiased form implemented (now).","code":"heterogeneity(mississippi, method = \"shannon\") #>  [1] 1.2027955 0.7646565 0.9293974 0.8228576 0.7901428 0.9998430 1.2051989 #>  [8] 1.1776226 1.1533432 1.2884172 1.1725355 1.5296294 1.7952443 1.1627477 #> [15] 1.0718463 0.9205717 1.1751002 0.7307620 1.1270126 1.0270291 evenness(mississippi, method = \"shannon\") #>  [1] 0.8676335 0.5515831 0.5187066 0.5112702 0.4909433 0.9100964 0.7488322 #>  [8] 0.7316981 0.6436931 0.7190793 0.5638704 0.7860740 0.8633300 0.5049749 #> [15] 0.4654969 0.4427014 0.5651037 0.3514222 0.4894554 0.4938966","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"brillouin-diversity-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness > Information theory index","what":"Brillouin diversity index","title":"Diversity Measures","text":"Brillouin index (Brillouin 1956) describes known collection: assume random sampling infinite population. Pielou (1975) Laxton (1978) argues use Brillouin index circumstances, especially preference Shannon index. Diversity: \\[ H' = \\frac{\\ln (N!) - \\sum_{= 1}^{S} \\ln (n_i!)}{N} \\] Evenness: \\[ E = \\frac{H'}{H'_{max}} \\] : \\[ H'_{max} = \\frac{1}{N} \\ln \\frac{N!}{\\left( \\lfloor \\frac{N}{S} \\rfloor! \\right)^{S - r} \\left[ \\left( \\lfloor \\frac{N}{S} \\rfloor + 1 \\right)! \\right]^{r}} \\] : \\(r = N - S \\lfloor \\frac{N}{S} \\rfloor\\).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"dominance-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness","what":"Dominance index","title":"Diversity Measures","text":"following methods return dominance index, reciprocal inverse form usually adopted, increase value index accompanies decrease diversity.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"simpson-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness > Dominance index","what":"Simpson index","title":"Diversity Measures","text":"Simpson index (Simpson 1949) expresses probability two individuals randomly picked finite sample belong two different types. can interpreted weighted mean proportional abundances. metric true probability value, ranges \\(0\\) (perfectly uneven) \\(1\\) (perfectly even). Dominance infinite sample: \\[ D = \\sum_{= 1}^{S} p_i^2 \\] Dominance finite sample: \\[ D = \\sum_{= 1}^{S} \\frac{n_i \\left( n_i - 1 \\right)}{N \\left( N - 1 \\right)} \\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"mcintosh-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness > Dominance index","what":"McIntosh index","title":"Diversity Measures","text":"McIntosh index (McIntosh 1967) expresses heterogeneity sample geometric terms. describes sample point \\(S\\)-dimensional hypervolume uses Euclidean distance point origin. Dominance: \\[ D = \\frac{N - U}{N - \\sqrt{N}} \\] Evenness: \\[ E = \\frac{N - U}{N - \\frac{N}{\\sqrt{S}}} \\] \\(U\\) distance sample origin \\(S\\) dimensional hypervolume: \\[U = \\sqrt{\\sum_{= 1}^{S} n_i^2}\\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"berger-parker-index","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Heterogeneity and Evenness > Dominance index","what":"Berger-Parker index","title":"Diversity Measures","text":"Berger-Parker index (Berger Parker 1970) expresses proportional importance abundant type. metric highly biased sample size richness, moreover make use information available sample. Dominance: \\[ D = \\frac{n_{max}}{N} \\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"richness-and-rarefaction","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity","what":"Richness and Rarefaction","title":"Diversity Measures","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa [e.g.\u00a0open system; Peet (1974)]. alternative, richness (\\(R\\)) can used concept taxa number (McIntosh 1967). Richness refers variety taxa/species/types present assemblage community (Bobrowsky Ball 1989) \u201cnumber species present collection containing specified number individuals\u201d (Hurlbert 1971). always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (\\(\\hat{S}\\)) number taxa expected samples standard size \\(n\\) (.e.\u00a0taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.  Richness measures (Bobrowsky Ball 1989). *: implemented. Rarefaction measures (Bobrowsky Ball 1989). *: implemented. : \\(S\\) number observed species/types, \\(N_i\\) number individuals \\(\\)-th species/type, \\(N = \\sum_{= 1}^{S} N_i\\) total number individuals, \\(\\) area isolate collection. \\(m\\) number individuals rarest species/type. \\(\\alpha\\) Fisher\u2019s slope constant, \\(y_{0}\\) number species/types modal class interval, \\(\\hat{\\sigma}\\) estimate standard deviation, \\(n\\) sub-sample size, \\(R\\) constant rate increment, \\(\\hat{S}\\) number expected predicted species/types, \\(k\\), \\(d\\), \\(\\) \\(b\\) empirically derived coefficients regression.","code":"richness(mississippi, method = \"margalef\") #>  [1] 0.5963696 0.4524421 0.6971143 0.6193544 0.5599404 0.4577237 0.7292886 #>  [8] 0.7779583 1.0304965 0.9224182 1.1892416 1.1412278 1.5518107 1.2645413 #> [15] 1.2090820 1.0903435 1.1570758 1.1892416 1.2552092 1.0158754 composition(mississippi, method = \"chao1\") #>  [1]  4.000000  4.000000  6.000000  5.000000  5.000000  3.000000  5.000000 #>  [8]  5.000000  7.984375  6.000000  8.000000  7.000000  8.494505 10.000000 #> [15] 10.000000  8.998371  8.498821  8.249306 10.000000  8.499491 ## Baxter rarefaction RA <- rarefaction(mississippi, sample = 100, method = \"baxter\") plot(RA)","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"chao1-estimator-chao1984","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Richness and Rarefaction > Asymptotic Species Richness","what":"Chao1 estimator (Chao 1984)","title":"Diversity Measures","text":"\\[ \\hat{S}_{Chao1} = \\begin{cases}   S + \\frac{N - 1}{N} \\frac{s_1^2}{2 s_2} & s_2 > 0 \\\\   S + \\frac{N - 1}{N} \\frac{s_1 (s_1 - 1)}{2} & s_2 = 0 \\end{cases} \\] special case homogeneous case, bias-corrected estimator : \\[ \\hat{S}_{bcChao1} = S + \\frac{N - 1}{N} \\frac{s_1 (s_1 - 1)}{2 s_2 + 1}\\] improved Chao1 estimator makes use additional information tripletons quadrupletons (Chiu et al. 2014): \\[ \\hat{S}_{iChao1} = \\hat{S}_{Chao1} + \\frac{N - 3}{4 N} \\frac{s_3}{s_4} \\times \\max\\left(s_1 - \\frac{N - 3}{N - 1} \\frac{s_2 s_3}{2 s_4} , 0\\right)\\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"abundance-based-coverage-estimator-chao1992","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Richness and Rarefaction > Asymptotic Species Richness","what":"Abundance-based Coverage Estimator (Chao and Lee 1992)","title":"Diversity Measures","text":"\\[ \\hat{S}_{ACE} = \\hat{S}_{abun} + \\frac{\\hat{S}_{rare}}{\\hat{C}_{rare}} + \\frac{s_1}{\\hat{C}_{rare}} \\times \\hat{\\gamma}^2_{rare} \\] \\(\\hat{S}_{rare} = \\sum_{= 1}^{k} s_i\\) number rare taxa, \\(\\hat{S}_{abun} = \\sum_{> k}^{N} s_i\\) number abundant taxa (given cut-value \\(k\\)), \\(\\hat{C}_{rare} = 1 - \\frac{s_1}{N_{rare}}\\) Turing\u2019s coverage estimate : \\[ \\hat{\\gamma}^2_{rare} = \\max\\left[\\frac{\\hat{S}_{rare}}{\\hat{C}_{rare}} \\frac{\\sum_{= 1}^{k} (- 1)s_i}{\\left(\\sum_{= 1}^{k} is_i\\right)\\left(\\sum_{= 1}^{k} is_i - 1\\right)} - 1, 0\\right]  \\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"chao2-estimator-chao1987","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Richness and Rarefaction > Asymptotic Species Richness","what":"Chao2 estimator (Chao 1987)","title":"Diversity Measures","text":"replicated incidence data (.e.\u00a0\\(m \\times p\\) logical matrix), Chao2 estimator : \\[ \\hat{S}_{Chao2} = \\begin{cases}   S + \\frac{m - 1}{m} \\frac{q_1^2}{2 q_2} & q_2 > 0 \\\\   S + \\frac{m - 1}{m} \\frac{q_1 (q_1 - 1)}{2} & q_2 = 0 \\end{cases} \\] Improved Chao2 estimator (Chiu et al. 2014): \\[ \\hat{S}_{iChao2} = \\hat{S}_{Chao2} + \\frac{m - 3}{4 m} \\frac{q_3}{q_4} \\times \\max\\left(q_1 - \\frac{m - 3}{m - 1} \\frac{q_2 q_3}{2 q_4} , 0\\right)\\]","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"incidence-based-coverage-estimator-chao2016","dir":"Articles","previous_headings":"\\(\\alpha\\)-diversity > Richness and Rarefaction > Asymptotic Species Richness","what":"Incidence-based Coverage Estimator (Chao and Chiu 2016)","title":"Diversity Measures","text":"\\[ \\hat{S}_{ICE} = \\hat{S}_{freq} + \\frac{\\hat{S}_{infreq}}{\\hat{C}_{infreq}} + \\frac{q_1}{\\hat{C}_{infreq}} \\times \\hat{\\gamma}^2_{infreq} \\] \\(\\hat{S}_{infreq} = \\sum_{= 1}^{k} q_i\\) number infrequent taxa, \\(\\hat{S}_{freq} = \\sum_{> k}^{N} q_i\\) number frequent taxa (given cut-value \\(k\\)), \\(\\hat{C}_{infreq} = 1 - \\frac{Q_1}{\\sum_{= 1}^{k} iq_i}\\) Turing\u2019s coverage estimate : \\[ \\hat{\\gamma}^2_{infreq} = \\max\\left[\\frac{\\hat{S}_{infreq}}{\\hat{C}_{infreq}} \\frac{m_{infreq}}{m_{infreq} - 1} \\frac{\\sum_{= 1}^{k} (- 1)q_i}{\\left(\\sum_{= 1}^{k} iq_i\\right)\\left(\\sum_{= 1}^{k} iq_i - 1\\right)} - 1, 0\\right]  \\] \\(m_{infreq}\\) number sampling units include least one infrequent species.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"turnover","dir":"Articles","previous_headings":"\\(\\beta\\)-diversity","what":"Turnover","title":"Diversity Measures","text":"following methods can used ascertain degree turnover taxa composition along gradient qualitative (presence/absence) data. assumes order matrix rows (1 \\(m\\)) follows progression along gradient/transect. denote \\(m \\times p\\) incidence matrix \\(X = \\left[ x_{ij} \\right] ~\\forall \\\\left[ 1,m \\right], j \\\\left[ 1,p \\right]\\) \\(p \\times p\\) corresponding co-occurrence matrix \\(Y = \\left[ y_{ij} \\right] ~\\forall ,j \\\\left[ 1,p \\right]\\), row column sums: \\[\\begin{align} x_{\\cdot} = \\sum_{j = 1}^{p} x_{ij} && x_{\\cdot j} = \\sum_{= 1}^{m} x_{ij} && x_{\\cdot \\cdot} = \\sum_{j = 1}^{p} \\sum_{= 1}^{m} x_{ij} && \\forall x_{ij} \\\\lbrace 0,1 \\rbrace \\\\  y_{\\cdot} = \\sum_{j \\geqslant }^{p} y_{ij} && y_{\\cdot j} = \\sum_{\\leqslant j}^{p} y_{ij} && y_{\\cdot \\cdot} = \\sum_{= 1}^{p} \\sum_{j \\geqslant }^{p} y_{ij} && \\forall y_{ij} \\\\lbrace 0,1 \\rbrace \\end{align}\\] Turnover measures. *: implemented. : \\(\\alpha\\) mean sample diversity: \\(\\alpha = \\frac{x_{\\cdot \\cdot}}{m}\\), \\(g(H)\\) number taxa gained along transect, \\(l(H)\\) number taxa lost along transect.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/articles/diversity.html","id":"similarity","dir":"Articles","previous_headings":"\\(\\beta\\)-diversity","what":"Similarity","title":"Diversity Measures","text":"Similarity two samples \\(\\) \\(b\\) two types \\(x\\) \\(y\\) can measured follow. indices provide scale similarity \\(0\\)-\\(1\\) \\(1\\) perfect similarity \\(0\\) similarity, exception Brainerd-Robinson index scaled \\(0\\) \\(200\\). Qualitative similarity measures (samples). *: implemented. Quantitative similarity measures (samples). *: implemented. Quantitative similarity measures (types). *: implemented. : \\(S_a\\) \\(S_b\\) denote total number taxa observed samples \\(\\) \\(b\\), respectively, \\(N_a\\) \\(N_b\\) denote total number individuals samples \\(\\) \\(b\\), respectively, \\(a_j\\) \\(b_j\\) denote number individuals \\(j\\)-th type/taxon, \\(j \\\\left[ 1,S \\right]\\), \\(x_i\\) \\(y_i\\) denote number individuals \\(\\)-th sample/case, \\(\\\\left[ 1,m \\right]\\), \\(o_i\\) denotes number sample/case common type/taxon: \\(o_i = \\sum_{k = 1}^{m} x_k \\cap y_k\\), \\(o_j\\) denotes number type/taxon common sample/case: \\(o_j = \\sum_{k = 1}^{S} a_k \\cap b_k\\).","code":"## Brainerd-Robinson (similarity between assemblages) BR <- similarity(mississippi, method = \"brainerd\") plot_spot(BR, col = khroma::colour(\"YlOrBr\")(12)) ## Binomial co-occurrence (similarity between types) BI <- similarity(mississippi, method = \"binomial\") plot_spot(BI, col = khroma::colour(\"PRGn\")(12))","package":"tabula"},{"path":[],"package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Analysis and Visualization of Archaeological Count Data","text":"easy way examine archaeological count data. package provides several tests measures diversity: heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). allows easily visualize count data statistical thresholds: rank vs.\u00a0abundance plots, heatmaps, Ford (1962) Bertin (1977) diagrams, etc. tabula provides methods : Diversity measurement: heterogeneity(), evenness(), richness(), rarefaction(), turnover() Similarity measurement co-occurrence: similarity(), occurrence() Assessing sample size significance: bootstrap(), jackknife(), simulate() Bertin (1977) Ford (1962) (battleship curve) diagrams: plot_bertin(), plot_ford() Seriograph (Desachy 2004): seriograph() Heatmaps: plot_heatmap(), plot_spot() kairos companion package tabula provides functions chronological modeling dating archaeological assemblages count data.","code":"To cite tabula in publications use:    Frerebeau N (2019). \"tabula: An R Package for Analysis, Seriation,   and Visualization of Archaeological Count Data.\" _Journal of Open   Source Software_, *4*(44). doi:10.21105/joss.01821   <https://doi.org/10.21105/joss.01821>.    Frerebeau N (2023). _tabula: Analysis and Visualization of   Archaeological Count Data_. Universit\u00e9 Bordeaux Montaigne, Pessac,   France. doi:10.5281/zenodo.1489944   <https://doi.org/10.5281/zenodo.1489944>, R package version 3.0.0,   <https://packages.tesselle.org/tabula/>.  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analysis and Visualization of Archaeological Count Data","text":"can install released version tabula CRAN : development version GitHub :","code":"install.packages(\"tabula\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/tabula\")","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Analysis and Visualization of Archaeological Count Data","text":"assumes keep data tidy: variable (type/taxa) must saved column observation (sample/case) must saved row.","code":"## Install extra packages (if needed) # install.packages(\"folio\")  ## Load packages library(tabula) ## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\")  ## Ford diagram plot_ford(mississippi) ## Co-occurrence of ceramic types mississippi |>    occurrence() |>    plot_spot() ## Data from Conkey 1980, Kintigh 1989, p. 28 data(\"chevelon\", package = \"folio\")  ## Measure diversity by comparing to simulated assemblages set.seed(12345)  chevelon |>   heterogeneity(method = \"shannon\") |>   simulate() |>   plot()  chevelon |>   richness(method = \"count\") |>   simulate() |>   plot()","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Analysis and Visualization of Archaeological Count Data","text":"Please note tabula project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/paper.html","id":null,"dir":"","previous_headings":"","what":"Background","title":"Background","text":"Detecting quantifying material cultural variations time space important methodological issues archaeology. solve issues, need construct reliable chronologies quantitative descriptions archaeological assemblages, . e. archaeological sites intrasite units, described set p different objects. Building chronologies involves distinguishing relative (providing chronological sequence) absolute dating methods (yield calendric indicators) [@obrien2002]. Within relative dating, matrix seriation long-established method\u2014first formulated @petrie1899\u2014allowed construction reference chronologies [@ihm2005]. set X n archaeological assemblages, seriation problem comes discovering X order inferred chronological. approach relies set well-defined statistical archaeological assumptions [@dunnell1970]. may use priori information, e.g., absolute dates stratigraphical constraints [@poblome2003] allows analysis chronological patterns socio-economic cultural perspective (e.g., [@bellanger2012], [@lipo2015]). quantitative analysis archaeological assemblages can thus carried synchronic, e.g., diversity measurements, diachronic, e.g., evolutionary studies: selection process, patterns cultural transmission, etc., way. approaches cover wide range applications led development multitude statistical models, none systematically implemented enable deployment reproducible workflows.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/paper.html","id":"summary","dir":"","previous_headings":"","what":"Summary","title":"Background","text":"tabula provides convenient reproducible toolkit analyzing, seriating, visualizing archaeological count data, artifacts faunal remains. Several R packages, e.g.\u00a0ade4 [@dray2007], SpadeR [@chao2016] vegan [@oksanen2019], allow estimation diversity indices implement seriation/ordination methods, packages mainly oriented towards ecological issues. tabula provides archaeologically-orientated implementations allow integration specific data (dates, stratigraphy, etc.) offers consistent framework. latter particular value since tabula designed used archaeologists students little background courses dating methods applied statistics archaeology. package uses set S4 classes archaeological data matrices extend matrix data type. new classes represent different specialized matrices: incidence, abundance, co-occurrence, (dis)similarity. Methods variety functions applied objects classes provide tools relative absolute dating analysis (chronological) patterns. tabula includes functions matrix seriation (seriate_*), well chronological modeling dating (date_*) archaeological assemblages objects. Resulting models can checked stability refined resampling methods (refine_*). Estimated dates can displayed tempo activity plot [@dye2016] assess rhythms long periods. Beyond , tabula provides several tests (test_*) measures diversity within archaeological assemblages (index_*): heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). Finally, package makes easy visualize count data statistical thresholds (plot_*): rank vs.\u00a0abundance plots, heatmaps, @ford1962 @bertin1977 diagrams.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/paper.html","id":"acknowledgements","dir":"","previous_headings":"","what":"Acknowledgements","title":"Background","text":"following contributors made possible develop project helpful discussion bringing new ideas: Jean-Baptiste Fourvel, Brice Lebrun, Ben Marwick, Matthew Peeples, Anne Philippe.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":[],"package":"tabula"},{"path":[],"package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Composition Index \u2014 CompositionIndex-class","title":"Composition Index \u2014 CompositionIndex-class","text":"S4 class represent composition measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Composition Index \u2014 CompositionIndex-class","text":"class inherits DiversityIndex.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Composition Index \u2014 CompositionIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Index \u2014 DiversityIndex-class","title":"Diversity Index \u2014 DiversityIndex-class","text":"S4 class represent diversity measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Diversity Index \u2014 DiversityIndex-class","text":".Data numeric vector diversity measures. labels character vector giving sample names. size integer vector giving sample sizes. data numeric matrix count data. method character string specifying method used. simulation four columns numeric matrix giving diversity measures simulated assemblage (sample size, mean estimate, lower upper boundaries confidence interval).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Diversity Index \u2014 DiversityIndex-class","text":"class inherits base numeric.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Diversity Index \u2014 DiversityIndex-class","text":"code snippets , x DiversityIndex object. .data.frame(x) Coerces data.frame.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Index \u2014 DiversityIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Evenness Index \u2014 EvennessIndex-class","title":"Evenness Index \u2014 EvennessIndex-class","text":"S4 class represent evenness measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Evenness Index \u2014 EvennessIndex-class","text":"class inherits DiversityIndex.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Evenness Index \u2014 EvennessIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity Index \u2014 HeterogeneityIndex-class","title":"Heterogeneity Index \u2014 HeterogeneityIndex-class","text":"S4 class represent heterogeneity measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Heterogeneity Index \u2014 HeterogeneityIndex-class","text":"class inherits DiversityIndex.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity Index \u2014 HeterogeneityIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Index \u2014 RarefactionIndex-class","title":"Rarefaction Index \u2014 RarefactionIndex-class","text":"S4 class represent rarefaction measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Rarefaction Index \u2014 RarefactionIndex-class","text":".Data numeric matrix count data (expected taxa per fixed number individuals). labels character vector giving sample names. size integer vector giving sample sizes. method character string specifying method used.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rarefaction Index \u2014 RarefactionIndex-class","text":"class inherits base matrix.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Index \u2014 RarefactionIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness Index \u2014 RichnessIndex-class","title":"Richness Index \u2014 RichnessIndex-class","text":"S4 class represent richness measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Richness Index \u2014 RichnessIndex-class","text":"class inherits DiversityIndex.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness Index \u2014 RichnessIndex-class","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":null,"dir":"Reference","previous_headings":"","what":"Birds Species and Abundances \u2014 aves","title":"Birds Species and Abundances \u2014 aves","text":"dataset birds species abundances managed unmanaged areas along River Wye (UK).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Birds Species and Abundances \u2014 aves","text":"","code":"aves","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Birds Species and Abundances \u2014 aves","text":"data.frame 2 rows 26 variables (bird species).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Birds Species and Abundances \u2014 aves","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Estimation \u2014 bootstrap_diversity","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"Samples randomly elements object replacement.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"","code":"# S4 method for DiversityIndex bootstrap(object, n = 1000, f = NULL)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"object R object (typically DiversityIndex object). n non-negative integer giving number bootstrap replications. f function takes single numeric vector (result ) argument.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"f NULL (default), bootstrap() returns named numeric vector following elements: original observed value applied object. mean bootstrap estimate mean . bias bootstrap estimate bias . error bootstrap estimate standard error . f function, bootstrap() returns result f applied n values .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Estimation \u2014 bootstrap_diversity","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187  ## Bootstrap resampling bootstrap(h, f = NULL) #>                  original     mean         bias      error #> Altamira         3.269200 3.281415  0.012215108 0.08094605 #> Cueto de la Mina 2.955298 2.976502  0.021203835 0.14867535 #> El Juyo          2.491683 2.533877  0.042194118 0.19348027 #> El Cierro        2.485604 2.492820  0.007216276 0.21902418 #> La Paloma        2.329187 2.322210 -0.006976781 0.26597814  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         2.980130 3.227869 3.285167 3.278950 3.332218 3.526771 #> Cueto de la Mina 2.379574 2.877482 2.974412 2.969057 3.075389 3.376285 #> El Juyo          1.364750 2.402382 2.550036 2.532691 2.673939 3.090122 #> El Cierro        1.349792 2.341877 2.485570 2.472341 2.624417 3.106595 #> La Paloma        1.329661 2.176777 2.345239 2.323145 2.493576 2.986485  quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant) #>                       25%      50% #> Altamira         3.218863 3.279698 #> Cueto de la Mina 2.878694 2.981459 #> El Juyo          2.406119 2.541281 #> El Cierro        2.353266 2.509887 #> La Paloma        2.143952 2.331598  ## Jackknife resampling jackknife(h) #>                  original     mean       bias      error #> Altamira         3.269200 3.246457 -0.9779158 0.08145529 #> Cueto de la Mina 2.955298 2.932856 -0.9649994 0.15907033 #> El Juyo          2.491683 2.469588 -0.9500688 0.19670002 #> El Cierro        2.485604 2.462723 -0.9838709 0.22195573 #> La Paloma        2.329187 2.305876 -1.0023704 0.25266291  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         2.907398 3.227045 3.285983 3.280298 3.334200 3.475782 #> Cueto de la Mina 2.422481 2.887438 2.982113 2.980356 3.077014 3.442116 #> El Juyo          1.718474 2.400674 2.534033 2.520185 2.659175 3.121564 #> El Cierro        1.524290 2.347204 2.498433 2.487297 2.647794 3.099865 #> La Paloma        1.098612 2.163956 2.347580 2.319130 2.506859 2.916372","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":null,"dir":"Reference","previous_headings":"","what":"Early Magdalenian Engraved Bones \u2014 cantabria","title":"Early Magdalenian Engraved Bones \u2014 cantabria","text":"dataset design elements engraved bones Cantabrian Spain.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Early Magdalenian Engraved Bones \u2014 cantabria","text":"","code":"cantabria","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Early Magdalenian Engraved Bones \u2014 cantabria","text":"data.frame 5 rows 44 variables (designs).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Early Magdalenian Engraved Bones \u2014 cantabria","text":"Conkey, M. W. (1980). Identification prehistoric hunter-gatherer aggregation sites: case Altamira. Current Anthropology, 21(5), 609-630. Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity and Evenness \u2014 heterogeneity","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"heterogeneity() returns heterogeneity dominance index. evenness() returns evenness measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"","code":"heterogeneity(object, ...)  evenness(object, ...)  index_berger(x, ...)  index_boone(x, ...)  index_brillouin(x, ...)  index_mcintosh(x, ...)  index_shannon(x, ...)  index_simpson(x, ...)  # S4 method for matrix heterogeneity(   object,   method = c(\"berger\", \"boone\", \"brillouin\", \"mcintosh\", \"shannon\", \"simpson\"),   j = NULL )  # S4 method for data.frame heterogeneity(   object,   method = c(\"berger\", \"boone\", \"brillouin\", \"mcintosh\", \"shannon\", \"simpson\"),   j = NULL )  # S4 method for matrix evenness(object, method = c(\"shannon\", \"brillouin\", \"mcintosh\", \"simpson\"))  # S4 method for data.frame evenness(object, method = c(\"shannon\", \"brillouin\", \"mcintosh\", \"simpson\"))  # S4 method for numeric index_berger(x, na.rm = FALSE, ...)  # S4 method for matrix index_boone(x, j = NULL, na.rm = FALSE, ...)  # S4 method for numeric index_brillouin(x, evenness = FALSE, na.rm = FALSE, ...)  # S4 method for numeric index_mcintosh(x, evenness = FALSE, na.rm = FALSE, ...)  # S4 method for numeric index_shannon(x, evenness = FALSE, base = exp(1), na.rm = FALSE, ...)  # S4 method for numeric index_simpson(x, evenness = FALSE, na.rm = FALSE, ...)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. x numeric vector count data (absolute frequencies). method character string specifying index computed (see details). unambiguous substring can given. j integer giving index reference type/taxa. NULL (default), frequent type/taxa assemblage used. na.rm numeric scalar: missing values (including NaN) removed? evenness logical scalar: evenness measure computed instead heterogeneity/dominance index? base positive numeric value specifying base respect logarithms computed.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"heterogeneity() returns HeterogeneityIndex object. evenness() returns EvennessIndex object. index_*() return numeric vector.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"Diversity measurement assumes individuals specific taxa equivalent types equally different (Peet 1974). measure diversity can achieved using indices built relative abundance taxa. indices (sometimes referred non-parametric indices) benefit making assumptions underlying distribution taxa abundance: take relative abundances species present species richness account. Peet (1974) refers indices heterogeneity. Diversity indices focus one aspect taxa abundance emphasize either richness (weighting towards uncommon taxa) dominance (weighting towards abundant taxa; Magurran 1988). Evenness measure evenly individuals distributed across sample.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"Ramanujan approximation used \\(x!\\) computation \\(x > 170\\).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"heterogeneity-and-evenness-measures","dir":"Reference","previous_headings":"","what":"Heterogeneity and Evenness Measures","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"following heterogeneity index corresponding evenness measures available (see Magurran 1988 details): berger Berger-Parker dominance index. Berger-Parker index expresses proportional importance abundant type. metric highly biased sample size richness, moreover make use information available sample. boone Boone heterogeneity measure. brillouin Brillouin diversity index. Brillouin index describes known collection: assume random sampling infinite population. Pielou (1975) Laxton (1978) argues use Brillouin index circumstances, especially preference Shannon index. mcintosh McIntosh dominance index. McIntosh index expresses heterogeneity sample geometric terms. describes sample point \\(S\\)-dimensional hypervolume uses Euclidean distance point origin. shannon Shannon-Wiener diversity index. Shannon index assumes individuals randomly sampled infinite population taxa represented sample (reflect sample size). main source error arises failure include taxa sample: error increases proportion species discovered sample declines (Peet 1974, Magurran 1988). maximum likelihood estimator (MLE) used relative abundance, known negatively biased sample size. simpson Simpson dominance index finite sample. Simpson index expresses probability two individuals randomly picked finite sample belong two different types. can interpreted weighted mean proportional abundances. metric true probability value, ranges \\(0\\) (perfectly uneven) \\(1\\) (perfectly even). berger, mcintosh simpson methods return dominance index, reciprocal inverse form usually adopted, increase value index accompanies decrease diversity.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"Berger, W. H. & Parker, F. L. (1970). Diversity Planktonic Foraminifera Deep-Sea Sediments. Science, 168(3937), 1345-1347. doi:10.1126/science.168.3937.1345 . Boone, J. L. (1987). Defining Measuring Midden Catchment. American Antiquity, 52(2), 336-45. doi:10.2307/281785 . Brillouin, L. (1956). Science information theory. New York: Academic Press. Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36. Laxton, R. R. (1978). measure diversity. Journal Theoretical Biology, 70(1), 51-67. doi:10.1016/0022-5193(78)90302-8 . Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . McIntosh, R. P. (1967). Index Diversity Relation Certain Concepts Diversity. Ecology, 48(3), 392-404. doi:10.2307/1932674 . Peet, R. K. (1974). Measurement Species Diversity. Annual Review Ecology Systematics, 5(1), 285-307. doi:10.1146/annurev.es.05.110174.001441 . Pielou, E. C. (1975). Ecological Diversity. New York: Wiley. doi:10.4319/lo.1977.22.1.0174b Shannon, C. E. (1948). Mathematical Theory Communication. Bell System Technical Journal, 27, 379-423. doi:10.1002/j.1538-7305.1948.tb01338.x . Simpson, E. H. (1949). Measurement Diversity. Nature, 163(4148), 688-688. doi:10.1038/163688a0 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heterogeneity and Evenness \u2014 heterogeneity","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187 (e <- evenness(cantabria, method = \"shannon\")) #> [1] 0.8987278 0.8966760 0.8462335 0.9178574 0.9373336  plot(h)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":null,"dir":"Reference","previous_headings":"","what":"Jackknife Estimation \u2014 jackknife_diversity","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"Jackknife Estimation","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"","code":"# S4 method for DiversityIndex jackknife(object, f = NULL)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"object R object (typically DiversityIndex object). f function takes single numeric vector (leave-one-values ) argument.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"f NULL (default), jackknife() returns named numeric vector following elements: original observed value applied object. mean jackknife estimate mean . bias jackknife estimate bias . error jackknife estimate standard error . f function, jackknife() returns result f applied leave-one-values .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Jackknife Estimation \u2014 jackknife_diversity","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Shannon diversity index (h <- heterogeneity(cantabria, method = \"shannon\")) #> [1] 3.269200 2.955298 2.491683 2.485604 2.329187  ## Bootstrap resampling bootstrap(h, f = NULL) #>                  original     mean         bias      error #> Altamira         3.269200 3.278206  0.009006676 0.08307194 #> Cueto de la Mina 2.955298 2.972727  0.017429550 0.15398977 #> El Juyo          2.491683 2.512902  0.021218780 0.20477828 #> El Cierro        2.485604 2.489387  0.003782870 0.21801177 #> La Paloma        2.329187 2.304147 -0.025039501 0.26161788  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         3.025561 3.223493 3.274640 3.274596 3.331732 3.510869 #> Cueto de la Mina 2.510647 2.879026 2.986806 2.977599 3.078147 3.392700 #> El Juyo          1.867719 2.400420 2.540724 2.524350 2.667965 3.082037 #> El Cierro        1.671531 2.369194 2.515860 2.495118 2.651108 3.035360 #> La Paloma        1.213008 2.163956 2.343947 2.316331 2.499333 3.061994  quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant) #>                       25%      50% #> Altamira         3.229519 3.285076 #> Cueto de la Mina 2.878630 2.980912 #> El Juyo          2.396635 2.532030 #> El Cierro        2.366988 2.504375 #> La Paloma        2.145842 2.329187  ## Jackknife resampling jackknife(h) #>                  original     mean       bias      error #> Altamira         3.269200 3.246457 -0.9779158 0.08145529 #> Cueto de la Mina 2.955298 2.932856 -0.9649994 0.15907033 #> El Juyo          2.491683 2.469588 -0.9500688 0.19670002 #> El Cierro        2.485604 2.462723 -0.9838709 0.22195573 #> La Paloma        2.329187 2.305876 -1.0023704 0.25266291  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         3.045532 3.229899 3.283922 3.281962 3.334989 3.514534 #> Cueto de la Mina 2.301774 2.872228 2.977945 2.968116 3.075886 3.332967 #> El Juyo          1.695724 2.396411 2.537224 2.526913 2.674774 3.222411 #> El Cierro        1.723392 2.333030 2.496161 2.472532 2.637416 2.985356 #> La Paloma        1.277034 2.163956 2.338372 2.313336 2.491497 2.959830","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrigraph \u2014 matrigraph","title":"Matrigraph \u2014 matrigraph","text":"matrigraph() produces heatmap highlighting deviations independence. pvi() computes cell numeric matrix percentage column theoretical independence value.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrigraph \u2014 matrigraph","text":"","code":"matrigraph(object, ...)  pvi(object, ...)  # S4 method for matrix pvi(object)  # S4 method for data.frame pvi(object)  # S4 method for matrix matrigraph(object, reverse = FALSE, axes = TRUE, ...)  # S4 method for data.frame matrigraph(object, reverse = FALSE, ...)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrigraph \u2014 matrigraph","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. reverse logical scalar: negative deviations centered (see details)? axes logical scalar: axes drawn plot?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrigraph \u2014 matrigraph","text":"matrigraph() called side-effects: results graphic displayed (invisibly returns object). pvi() returns numeric matrix.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Matrigraph \u2014 matrigraph","text":"PVI (french \"pourcentages de valeur d'ind\u00e9pendance\") calculated cell percentage column theoretical independence value: PVI greater \\(1\\) represent positive deviations independence, whereas PVI smaller \\(1\\) represent negative deviations (Desachy 2004). PVI matrix allows explore deviations independence (intuitive approach \\(\\chi^2\\)), way high-contrast matrix quite significant deviations, low risk due randomness (Desachy 2004). matrigraph() displays deviations independence: PVI equal \\(1\\) (statistical independence), cell matrix filled grey. PVI less \\(1\\) (negative deviation independence), size grey square proportional PVI (white margin thus represents fraction negative deviation). PVI greater \\(1\\) (positive deviation), black square representing fraction positive deviations superimposed. large positive deviations (PVI greater \\(2\\)), cell filled black. reverse TRUE, fraction negative deviations displayed white square.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Matrigraph \u2014 matrigraph","text":"Desachy, B. (2004). Le s\u00e9riographe EPPM: un outil informatis\u00e9 de s\u00e9riation graphique pour tableaux de comptages. Revue arch\u00e9ologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Matrigraph \u2014 matrigraph","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrigraph \u2014 matrigraph","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Matrigraph matrigraph(compiegne)  matrigraph(compiegne, reverse = TRUE)   ## Compute PVI counts_pvi <- pvi(compiegne) plot_heatmap(counts_pvi, col = khroma::color(\"iridescent\")(12))","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or Set Parts of an Object \u2014 mutators","title":"Get or Set Parts of an Object \u2014 mutators","text":"Getters setters extract replace parts object.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get or Set Parts of an Object \u2014 mutators","text":"","code":"get_method(x)  # S4 method for DiversityIndex labels(object)  # S4 method for RarefactionIndex labels(object)  # S4 method for DiversityIndex get_method(x)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or Set Parts of an Object \u2014 mutators","text":"object, x R object get set element(s).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get or Set Parts of an Object \u2014 mutators","text":"labels() returns suitable set labels object use printing plotting.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get or Set Parts of an Object \u2014 mutators","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Co-Occurrence \u2014 occurrence","title":"Co-Occurrence \u2014 occurrence","text":"Co-Occurrence","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Co-Occurrence \u2014 occurrence","text":"","code":"occurrence(object, ...)  # S4 method for matrix occurrence(object)  # S4 method for data.frame occurrence(object)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Co-Occurrence \u2014 occurrence","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Co-Occurrence \u2014 occurrence","text":"stats::dist object.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Co-Occurrence \u2014 occurrence","text":"co-occurrence matrix symmetric matrix zeros main diagonal, works many times pairs taxa/types occur together least one sample.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Co-Occurrence \u2014 occurrence","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Co-Occurrence \u2014 occurrence","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Plot spot diagram of a co-occurrence matrix occ <- occurrence(cantabria) plot_spot(occ)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":null,"dir":"Reference","previous_headings":"","what":"Bertin Diagram \u2014 plot_bertin","title":"Bertin Diagram \u2014 plot_bertin","text":"Plots Bertin diagram.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bertin Diagram \u2014 plot_bertin","text":"","code":"plot_bertin(object, ...)  # S4 method for matrix plot_bertin(   object,   threshold = NULL,   freq = FALSE,   margin = 1,   col = c(\"white\", \"black\"),   flip = TRUE,   axes = TRUE,   ... )  # S4 method for data.frame plot_bertin(   object,   threshold = NULL,   freq = FALSE,   margin = 1,   col = c(\"white\", \"black\"),   flip = TRUE,   axes = TRUE,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bertin Diagram \u2014 plot_bertin","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. threshold function takes numeric vector argument returns numeric threshold value (see ). NULL (default), threshold computed. used freq FALSE. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. col vector colors. flip logical scalar: x y axis flipped? Defaults TRUE. axes logical scalar: axes drawn plot?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bertin Diagram \u2014 plot_bertin","text":"plot_bertin() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"bertin-matrix","dir":"Reference","previous_headings":"","what":"Bertin Matrix","title":"Bertin Diagram \u2014 plot_bertin","text":"de Falguerolles et al. (1997) points : \"abstract terms, Bertin matrix matrix  displays. ... fix ideas, think data matrix, variable case, real valued variables. variable, draw bar chart variable value case. High-light bars representing value sample threshold variable.\"","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bertin Diagram \u2014 plot_bertin","text":"Bertin, J. (1977). La graphique et le traitement graphique de l'information. Paris: Flammarion. Nouvelle Biblioth\u00e8que Scientifique. de Falguerolles, ., Friedrich, F. & Sawitzki, G. (1997). Tribute J. Bertin's Graphical Data Analysis. W. Badilla & F. Faulbaum (eds.), SoftStat '97: Advances Statistical Software 6. Stuttgart: Lucius & Lucius, p. 11-20.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bertin Diagram \u2014 plot_bertin","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bertin Diagram \u2014 plot_bertin","text":"","code":"## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\")  ## Plot a Bertin diagram... ## ...without threshold plot_bertin(mississippi)   ## ...with the variable mean as threshold plot_bertin(mississippi, threshold = mean)   ## Plot conditional proportions plot_bertin(mississippi, freq = TRUE, margin = 1)  plot_bertin(mississippi, freq = TRUE, margin = 2)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":null,"dir":"Reference","previous_headings":"","what":"Dice-Leraas Diagram \u2014 plot_diceleraas","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"Plots Dice-Leraas diagram.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"","code":"plot_diceleraas(object, ...)  # S4 method for matrix plot_diceleraas(   object,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = FALSE,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for data.frame plot_diceleraas(   object,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = FALSE,   panel.first = NULL,   panel.last = NULL,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"plot_diceleraas() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"Dice-Leraas diagram, horizontal line represents range data (min-max) small vertical line indicates mean. black rectangle twice standard error mean, white rectangle one standard deviation either side mean.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"Dice, L. R., & Leraas, H. J. (1936). Graphic Method Comparing Several Sets Measurements. Contributions Laboratory Vertebrate Genetics, 3: 1-3. Hubbs, C. L., & C. Hubbs (1953). Improved Graphical Analysis Comparison Series Samples. Systematic Biology, 2(2): 49-56. doi:10.2307/sysbio/2.2.49 . Simpson, G. G., Roe, ., & Lewontin, R. C. Quantitative Zoology. New York: Harcourt, Brace Company, 1960.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dice-Leraas Diagram \u2014 plot_diceleraas","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Plot a Dice-Leraas diagram plot_diceleraas(compiegne)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Plot \u2014 plot_diversity","title":"Diversity Plot \u2014 plot_diversity","text":"Diversity Plot","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Plot \u2014 plot_diversity","text":"","code":"# S4 method for DiversityIndex,missing plot(   x,   log = \"x\",   col.mean = \"#DDAA33\",   col.interval = \"#004488\",   lty.mean = \"solid\",   lty.interval = \"dashed\",   lwd.mean = 1,   lwd.interval = 1,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Plot \u2014 plot_diversity","text":"x DiversityIndex object plotted. log character string indicating axes log scale. Defaults x. col.mean, col.interval character string specifying color lines. lty.mean, lty.interval character string numeric value specifying line types. lwd.mean, lwd.interval non-negative numeric value specifying line widths. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters passed graphics::points(), particularly, cex, col pch.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Plot \u2014 plot_diversity","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Plot \u2014 plot_diversity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Plot \u2014 plot_diversity","text":"","code":"# \\donttest{ ## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = \"shannon\") h_sim <- simulate(h) plot(h_sim)   r <- richness(cantabria, method = \"count\") r_sim <- simulate(r) plot(r_sim)  # }","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":null,"dir":"Reference","previous_headings":"","what":"Ford Diagram \u2014 plot_ford","title":"Ford Diagram \u2014 plot_ford","text":"Plots Ford (battleship curve) diagram.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ford Diagram \u2014 plot_ford","text":"","code":"plot_ford(object, ...)  # S4 method for matrix plot_ford(   object,   weights = FALSE,   EPPM = FALSE,   fill = \"darkgrey\",   border = NA,   axes = TRUE,   ... )  # S4 method for data.frame plot_ford(object, weights = FALSE, EPPM = FALSE)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ford Diagram \u2014 plot_ford","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. weights logical scalar: row sums displayed? EPPM logical scalar: EPPM drawn? See seriograph(). fill color filling bars. border color draw borders. axes logical scalar: axes drawn plot?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ford Diagram \u2014 plot_ford","text":"plot_ford() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Ford Diagram \u2014 plot_ford","text":"Ford, J. . (1962). quantitative method deriving cultural chronology. Washington, DC: Pan American Union. Technical manual 1.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ford Diagram \u2014 plot_ford","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ford Diagram \u2014 plot_ford","text":"","code":"## Data from Lipo et al. 2015 data(\"mississippi\", package = \"folio\")  ## Plot a Ford diagram plot_ford(mississippi)   plot_ford(mississippi, weights = TRUE)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap \u2014 plot_heatmap","title":"Heatmap \u2014 plot_heatmap","text":"Plots heatmap.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap \u2014 plot_heatmap","text":"","code":"plot_heatmap(object, ...)  # S4 method for matrix plot_heatmap(   object,   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = TRUE,   upper = TRUE,   lower = TRUE,   freq = FALSE,   margin = 1,   axes = TRUE,   legend = TRUE,   ... )  # S4 method for data.frame plot_heatmap(   object,   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = TRUE,   upper = TRUE,   lower = TRUE,   freq = FALSE,   margin = 1,   axes = TRUE,   legend = TRUE,   ... )  # S4 method for dist plot_heatmap(   object,   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = FALSE,   upper = FALSE,   lower = !upper,   axes = TRUE,   legend = TRUE,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap \u2014 plot_heatmap","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. col vector colors. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. axes logical scalar: axes drawn plot? legend logical scalar: legend displayed?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap \u2014 plot_heatmap","text":"plot_heatmap() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heatmap \u2014 plot_heatmap","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heatmap \u2014 plot_heatmap","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Plot raw data plot_heatmap(cantabria)   ## Plot conditional proportions plot_heatmap(cantabria, freq = TRUE, margin = 1)  plot_heatmap(cantabria, freq = TRUE, margin = 2)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix Plot \u2014 plot_matrix","title":"Matrix Plot \u2014 plot_matrix","text":"Matrix Plot","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix Plot \u2014 plot_matrix","text":"","code":"plot_matrix(   object,   panel,   diag = TRUE,   upper = TRUE,   lower = TRUE,   freq = FALSE,   margin = 1,   scale = TRUE,   drop_zero = TRUE,   col = graphics::par(\"fg\"),   midpoint = NULL,   axes = TRUE,   legend = TRUE,   asp = 1,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix Plot \u2014 plot_matrix","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). panel function form function(x, y, z, color, ...) gives action carried panel display. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. scale logical scalar indicating whether data rescaled \\([-1,1]\\). used freq FALSE. drop_zero logical scalar: zeros discarded? col vector colors. midpoint numeric value specifying data midpoint. axes logical scalar: axes drawn plot? legend logical scalar: legend displayed? asp length-one numeric vector, giving aspect ratio \\(y/x\\). ... arguments passed panel.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Rank Plot \u2014 plot_rank","title":"Rank Plot \u2014 plot_rank","text":"Plots rank vs relative abundance diagram.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rank Plot \u2014 plot_rank","text":"","code":"plot_rank(object, ...)  # S4 method for matrix plot_rank(   object,   log = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   legend = list(x = \"topright\"),   ... )  # S4 method for data.frame plot_rank(   object,   log = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   legend = list(x = \"topright\"),   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rank Plot \u2014 plot_rank","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... graphical parameters. log character string contains \"x\" x axis logarithmic, \"y\" y axis logarithmic \"xy\" \"yx\" axes logarithmic (base 10). main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rank Plot \u2014 plot_rank","text":"plot_rank() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rank Plot \u2014 plot_rank","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rank Plot \u2014 plot_rank","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rank Plot \u2014 plot_rank","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Plot rank vs abundance plot_rank(cantabria)   ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot_rank(cantabria, col = col, pch = 15:19, lty = 2)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Plot \u2014 plot_rarefaction","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"Rarefaction Plot","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"","code":"# S4 method for RarefactionIndex,missing plot(   x,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   legend = list(x = \"topleft\"),   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"x RarefactionIndex object plotted. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. legend list additional arguments passed graphics::legend(); names list used argument names. NULL, legend displayed. ... graphical parameters passed graphics::lines().","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction Plot \u2014 plot_rarefaction","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = \"baxter\") plot(rare, panel.first = graphics::grid())   ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot(rare, col = col, lty = 1:5)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":null,"dir":"Reference","previous_headings":"","what":"Spot Plot \u2014 plot_spot","title":"Spot Plot \u2014 plot_spot","text":"Plots spot matrix.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spot Plot \u2014 plot_spot","text":"","code":"plot_spot(object, ...)  # S4 method for matrix plot_spot(   object,   type = c(\"ring\", \"plain\"),   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = TRUE,   upper = TRUE,   lower = TRUE,   freq = FALSE,   margin = 1,   axes = TRUE,   legend = TRUE,   ... )  # S4 method for data.frame plot_spot(   object,   type = c(\"ring\", \"plain\"),   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = TRUE,   upper = TRUE,   lower = TRUE,   freq = FALSE,   margin = 1,   axes = TRUE,   legend = TRUE,   ... )  # S4 method for dist plot_spot(   object,   type = c(\"ring\", \"plain\"),   col = grDevices::hcl.colors(12, \"YlOrBr\", rev = TRUE),   diag = FALSE,   upper = FALSE,   lower = !upper,   axes = TRUE,   legend = TRUE,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spot Plot \u2014 plot_spot","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. type character string specifying graph plotted. must one \"ring\" (default) \"plain\". unambiguous substring can given. col vector colors. diag logical scalar indicating whether diagonal matrix plotted. used object symmetric matrix. upper logical scalar indicating whether upper triangle matrix plotted. used object symmetric matrix. lower logical scalar indicating whether lower triangle matrix plotted. used object symmetric matrix. freq logical scalar indicating whether conditional proportions given margins used (.e. entries object, divided appropriate marginal sums). margin integer vector giving margins split : 1 indicates individuals/rows (default), 2 indicates variables/columns. used freq TRUE. axes logical scalar: axes drawn plot? legend logical scalar: legend displayed?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spot Plot \u2014 plot_spot","text":"plot_spot() called side-effects: results graphic displayed (invisibly returns object).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spot Plot \u2014 plot_spot","text":"spot matrix can considered variant Bertin diagram data first transformed relative frequencies.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Spot Plot \u2014 plot_spot","text":"Adapted Dan Gopstein's original idea.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spot Plot \u2014 plot_spot","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spot Plot \u2014 plot_spot","text":"","code":"## Data from Huntley 2004, 2008 data(\"pueblo\")  ## Plot spot diagram of count data plot_spot(pueblo, type = \"ring\")  plot_spot(pueblo, type = \"plain\")   ## Plot conditional proportions plot_spot(pueblo, freq = TRUE, margin = 1)  plot_spot(pueblo, freq = TRUE, margin = 2)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":null,"dir":"Reference","previous_headings":"","what":"Pueblo IV Period Ceramics \u2014 pueblo","title":"Pueblo IV Period Ceramics \u2014 pueblo","text":"dataset ceramic counts Zuni region.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pueblo IV Period Ceramics \u2014 pueblo","text":"","code":"pueblo","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pueblo IV Period Ceramics \u2014 pueblo","text":"data.frame 9 rows 5 variables (compositional groups).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Pueblo IV Period Ceramics \u2014 pueblo","text":"Huntley, D. L. (2004). Interaction, Boundaries, Identities: Multiscalar Approach Organizational Scale Pueblo IV Zuni Society. Ph.D. Dissertation, Arizona State University. Huntley, D. L. (2022). Ancestral Zuni Glaze-Decorated Pottery: Viewing Pueblo IV Regional Organization Ceramic Production Exchange. Anthropological Papers University Arizona 72. Tucson: University Arizona Press. doi:10.2307/j.ctv2ngx5n8 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction \u2014 rarefaction","title":"Rarefaction \u2014 rarefaction","text":"Rarefaction","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction \u2014 rarefaction","text":"","code":"rarefaction(object, ...)  index_baxter(x, ...)  index_hurlbert(x, ...)  # S4 method for matrix rarefaction(object, sample = NULL, method = c(\"hurlbert\", \"baxter\"), step = 1)  # S4 method for data.frame rarefaction(object, sample = NULL, method = c(\"hurlbert\", \"baxter\"), step = 1)  # S4 method for numeric index_baxter(x, sample, ...)  # S4 method for numeric index_hurlbert(x, sample, ...)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction \u2014 rarefaction","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. x numeric vector count data (absolute frequencies). sample length-one numeric vector giving sub-sample size. size sample smaller total community size. method character string vector strings specifying index computed (see details). unambiguous substring can given. step integer giving increment sample size.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction \u2014 rarefaction","text":"rarefaction() returns RarefactionIndex object. index_*() return numeric vector.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"rarefaction-measures","dir":"Reference","previous_headings":"","what":"Rarefaction Measures","title":"Rarefaction \u2014 rarefaction","text":"following rarefaction measures available count data: baxter Baxter's rarefaction. hurlbert Hurlbert's unbiased estimate Sander's rarefaction.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rarefaction \u2014 rarefaction","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa (e.g. open system; Peet 1974). alternative, richness (\\(S\\)) can used concept taxa number (McIntosh 1967). always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (\\(E(S)\\)) number taxa expected samples standard size (.e. taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rarefaction \u2014 rarefaction","text":"Baxter, M. J. (2001). Methodological Issues Study Assemblage Diversity. American Antiquity, 66(4), 715-725. doi:10.2307/2694184 . Hurlbert, S. H. (1971). Nonconcept Species Diversity: Critique Alternative Parameters. Ecology, 52(4), 577-586. doi:10.2307/1934145 . Sander, H. L. (1968). Marine Benthic Diversity: Comparative Study. American Naturalist, 102(925), 243-282.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction \u2014 rarefaction","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction \u2014 rarefaction","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Replicate fig. 3 from Baxter 2011 rare <- rarefaction(cantabria, sample = 23, method = \"baxter\") plot(rare, panel.first = graphics::grid())   ## Change graphical parameters col <- khroma::color(\"bright\")(5) plot(rare, col = col, lty = 1:5)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages \u2014 reexports","title":"Objects exported from other packages \u2014 reexports","text":"objects imported packages. Follow links see documentation. arkhe bootstrap, jackknife, remove_NA, remove_zero, replace_NA, replace_zero","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample \u2014 resample","title":"Resample \u2014 resample","text":"Simulates observations multinomial distribution.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample \u2014 resample","text":"","code":"resample(object, ...)  # S4 method for numeric resample(object, do, n, size = sum(object), ..., f = NULL)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample \u2014 resample","text":"object numeric vector count data (absolute frequencies). ... Extra arguments passed . function takes object argument returns single numeric value. n non-negative integer specifying number bootstrap replications. size non-negative integer specifying sample size. f function takes single numeric vector (result ) argument.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample \u2014 resample","text":"f NULL, resample() returns n values . Else, returns result f applied n values .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample \u2014 resample","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resample \u2014 resample","text":"","code":"## Sample observations from a multinomial distribution x <- sample(1:100, 50, TRUE) resample(x, do = median, n = 100) #>   [1] 55.5 55.0 52.5 53.0 52.0 56.5 56.5 53.0 53.5 56.0 52.5 51.0 54.5 53.5 53.0 #>  [16] 53.0 54.0 57.5 56.0 56.5 56.0 54.5 55.5 57.0 56.5 54.0 55.0 57.0 55.5 56.0 #>  [31] 55.5 55.5 56.0 57.0 55.5 51.5 54.0 55.0 56.0 55.5 53.0 54.0 54.0 51.0 52.0 #>  [46] 51.5 53.0 53.5 54.0 56.0 52.0 51.5 49.5 54.0 55.5 56.5 53.0 55.5 54.0 54.5 #>  [61] 55.0 54.5 57.5 54.5 54.5 50.0 57.0 54.5 53.0 57.5 57.0 54.5 55.0 53.0 54.0 #>  [76] 53.0 49.0 51.5 53.5 57.0 53.0 54.0 53.0 50.5 54.0 57.0 50.5 58.0 54.5 56.0 #>  [91] 51.5 53.0 55.5 54.0 53.0 54.5 54.0 56.0 55.0 54.0  ## Estimate the 25th, 50th and 95th percentiles quant <- function(x) { quantile(x, probs = c(0.25, 0.50, 0.75)) } resample(x, n = 100, do = median, f = quant) #>    25%    50%    75%  #> 52.875 54.000 55.500","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness \u2014 richness","title":"Richness \u2014 richness","text":"richness() returns sample richness. composition() returns asymptotic species richness.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Richness \u2014 richness","text":"","code":"richness(object, ...)  composition(object, ...)  index_ace(x, ...)  index_ice(x, ...)  index_chao1(x, ...)  index_chao2(x, ...)  index_margalef(x, ...)  index_menhinick(x, ...)  # S4 method for matrix richness(object, method = c(\"count\", \"margalef\", \"menhinick\"))  # S4 method for data.frame richness(object, method = c(\"count\", \"margalef\", \"menhinick\"))  # S4 method for matrix composition(   object,   method = c(\"chao1\", \"ace\", \"chao2\", \"ice\"),   unbiased = FALSE,   improved = FALSE,   k = 10 )  # S4 method for data.frame composition(   object,   method = c(\"chao1\", \"ace\", \"chao2\", \"ice\"),   unbiased = FALSE,   improved = FALSE,   k = 10 )  # S4 method for numeric index_margalef(x, na.rm = FALSE, ...)  # S4 method for numeric index_menhinick(x, na.rm = FALSE, ...)  # S4 method for numeric index_ace(x, k = 10, ...)  # S4 method for numeric index_chao1(x, unbiased = FALSE, improved = FALSE, ...)  # S4 method for matrix index_ice(x, k = 10, ...)  # S4 method for matrix index_chao2(x, unbiased = FALSE, improved = FALSE, ...)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Richness \u2014 richness","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. x numeric vector matrix count data (absolute frequencies). method character string vector strings specifying index computed (see details). unambiguous substring can given. unbiased logical scalar. bias-corrected estimator used? used \"chao1\" \"chao2\" (improved) estimator. improved logical scalar. improved estimator used? used \"chao1\" \"chao2\". k length-one numeric vector giving threshold rare/infrequent abundant/frequent species. used method \"ace\" \"ice\". na.rm numeric scalar: missing values (including NaN) removed?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Richness \u2014 richness","text":"richness() returns RichnessIndex object. composition() returns CompositionIndex object. index_*() return numeric vector.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Richness \u2014 richness","text":"number different taxa, provides instantly comprehensible expression diversity. number taxa within sample easy ascertain, term, makes little sense: taxa may seen, may fixed number taxa (e.g. open system; Peet 1974). alternative, richness (\\(S\\)) can used concept taxa number (McIntosh 1967). always possible ensure sample sizes equal number different taxa increases sample size sampling effort (Magurran 1988). , rarefaction (\\(E(S)\\)) number taxa expected samples standard size (.e. taxa per fixed number individuals). Rarefaction assumes imbalances taxa due sampling differences actual abundances.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"richness-measures","dir":"Reference","previous_headings":"","what":"Richness Measures","title":"Richness \u2014 richness","text":"following richness measures available count data: count Returns number observed taxa/types. margalef Margalef richness index. menhinick Menhinick richness index.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"asymptotic-species-richness","dir":"Reference","previous_headings":"","what":"Asymptotic Species Richness","title":"Richness \u2014 richness","text":"following measures available count data: ace Abundance-based Coverage Estimator. chao1 (improved/unbiased) Chao1 estimator. following measures available replicated incidence data: ice Incidence-based Coverage Estimator. chao2 (improved/unbiased) Chao2 estimator.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Richness \u2014 richness","text":"Chao, . (1984). Nonparametric Estimation Number Classes Population. Scandinavian Journal Statistics, 11(4), 265-270. Chao, . (1987). Estimating Population Size Capture-Recapture Data Unequal Catchability. Biometrics 43(4), 783-791. doi:10.2307/2531532 . Chao, . & Chiu, C.-H. (2016). Species Richness: Estimation Comparison. Balakrishnan, N., Colton, T., Everitt, B., Piegorsch, B., Ruggeri, F. & Teugels, J. L. (Eds.), Wiley StatsRef: Statistics Reference Online. Chichester, UK: John Wiley & Sons, Ltd., 1-26. doi:10.1002/9781118445112.stat03432.pub2 Chao, . & Lee, S.-M. (1992). Estimating Number Classes via Sample Coverage. Journal American Statistical Association, 87(417), 210-217. doi:10.1080/01621459.1992.10475194 . Chiu, C.-H., Wang, Y.-T., Walther, B. . & Chao, . (2014). improved nonparametric lower bound species richness via modified good-turing frequency formula. Biometrics, 70(3), 671-682. doi:10.1111/biom.12200 . Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . Kintigh, K. W. (1989). Sample Size, Significance, Measures Diversity. Leonard, R. D. Jones, G. T., Quantifying Diversity Archaeology. New Directions Archaeology. Cambridge: Cambridge University Press, p. 25-36. Magurran, E. & Brian J. McGill (2011). Biological Diversity: Frontiers Measurement Assessment. Oxford: Oxford University Press. Margalef, R. (1958). Information Theory Ecology. General Systems, 3, 36-71. Menhinick, E. F. (1964). Comparison Species-Individuals Diversity Indices Applied Samples Field Insects. Ecology, 45(4), 859-861. doi:10.2307/1934933 . McIntosh, R. P. (1967). Index Diversity Relation Certain Concepts Diversity. Ecology, 48(3), 392-404. doi:10.2307/1932674 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness \u2014 richness","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Richness \u2014 richness","text":"","code":"## Data from Magurran 1988, p. 128-129 trap <- matrix(data = c(9, 3, 0, 4, 2, 1, 1, 0, 1, 0, 1, 1,                         1, 0, 1, 0, 0, 0, 1, 2, 0, 5, 3, 0),                nrow = 2, byrow = TRUE, dimnames = list(c(\"A\", \"B\"), NULL))  ## Margalef and Menhinick index richness(trap, method = \"margalef\") # 2.55 1.88 #> [1] 2.551432 1.949356 richness(trap, method = \"menhinick\") # 1.95 1.66 #> [1] 1.876630 1.664101  ## Data from Chao & Chiu 2016 brazil <- matrix(   data = rep(x = c(1:21, 23, 25, 27, 28, 30, 32, 34:37, 41,                    45, 46, 49, 52, 89, 110, 123, 140),              times = c(113, 50, 39, 29, 15, 11, 13, 5, 6, 6, 3, 4,                        3, 5, 2, 5, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1,                        0, 0, 2, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0)),   nrow = 1, byrow = TRUE )  ## Chao1-type estimators (asymptotic species richness) composition(brazil, method = c(\"chao1\"), unbiased = FALSE) # 461.625 #> [1] 461.6254 composition(brazil, method = c(\"ace\"), k = 10) # 445.822 #> [1] 445.8224","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":null,"dir":"Reference","previous_headings":"","what":"Seriograph \u2014 seriograph","title":"Seriograph \u2014 seriograph","text":"seriograph() produces Ford diagram highlighting relationships rows columns. eppm() computes cell numeric matrix positive difference column mean percentage.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Seriograph \u2014 seriograph","text":"","code":"seriograph(object, ...)  eppm(object, ...)  # S4 method for matrix eppm(object)  # S4 method for data.frame eppm(object)  # S4 method for matrix seriograph(   object,   weights = FALSE,   fill = \"darkgrey\",   border = NA,   axes = TRUE,   ... )  # S4 method for data.frame seriograph(   object,   weights = FALSE,   fill = \"darkgrey\",   border = NA,   axes = TRUE,   ... )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Seriograph \u2014 seriograph","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). ... Currently used. weights logical scalar: row sums displayed? fill color filling bars. border color draw borders. axes logical scalar: axes drawn plot?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Seriograph \u2014 seriograph","text":"seriograph() called side-effects: results graphic displayed (invisibly returns object). eppm() returns numeric matrix.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Seriograph \u2014 seriograph","text":"positive difference column mean percentage (french \"\u00e9cart positif au pourcentage moyen\", EPPM) represents deviation situation statistical independence. independence can interpreted absence relationships types chronological order assemblages, EPPM useful tool explore significance relationship rows columns related seriation (Desachy 2004). seriograph() superimposes frequencies (grey) EPPM values (black) row-column pair Ford diagram.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Seriograph \u2014 seriograph","text":"Desachy, B. (2004). Le s\u00e9riographe EPPM: un outil informatis\u00e9 de s\u00e9riation graphique pour tableaux de comptages. Revue arch\u00e9ologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Seriograph \u2014 seriograph","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seriograph \u2014 seriograph","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Seriograph seriograph(compiegne)  seriograph(compiegne, weights = TRUE)   ## Compute EPPM counts_eppm <- eppm(compiegne) plot_heatmap(counts_eppm, col = khroma::color(\"YlOrBr\")(12))","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Similarity \u2014 similarity","title":"Similarity \u2014 similarity","text":"Similarity","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Similarity \u2014 similarity","text":"","code":"similarity(object, ...)  index_jaccard(x, y, ...)  index_sorenson(x, y, ...)  index_bray(x, y, ...)  index_morisita(x, y, ...)  index_brainerd(x, y, ...)  index_binomial(x, y, ...)  # S4 method for matrix similarity(   object,   method = c(\"brainerd\", \"bray\", \"jaccard\", \"morisita\", \"sorenson\", \"binomial\") )  # S4 method for data.frame similarity(   object,   method = c(\"brainerd\", \"bray\", \"jaccard\", \"morisita\", \"sorenson\", \"binomial\") )  # S4 method for character,character index_jaccard(x, y)  # S4 method for logical,logical index_jaccard(x, y)  # S4 method for numeric,numeric index_jaccard(x, y)  # S4 method for logical,logical index_sorenson(x, y)  # S4 method for numeric,numeric index_sorenson(x, y)  # S4 method for numeric,numeric index_bray(x, y)  # S4 method for numeric,numeric index_morisita(x, y)  # S4 method for numeric,numeric index_brainerd(x, y)  # S4 method for numeric,numeric index_binomial(x, y)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Similarity \u2014 similarity","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. x, y length-\\(p\\) numeric vector count data. method character string specifying method used (see details). unambiguous substring can given.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Similarity \u2014 similarity","text":"similarity() returns stats::dist object. index_*() return numeric vector.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Similarity \u2014 similarity","text":"\\(\\beta\\)-diversity can measured addressing similarity pairs samples/cases (Brainerd-Robinson, Jaccard, Morisita-Horn Sorenson indices). Similarity pairs taxa/types can measured assessing degree co-occurrence (binomial co-occurrence). Jaccard, Morisita-Horn Sorenson indices provide scale similarity \\(0\\)-\\(1\\) \\(1\\) perfect similarity \\(0\\) similarity. Brainerd-Robinson index scaled \\(0\\) \\(200\\). Binomial co-occurrence assessment approximates Z-score. binomial Binomial co-occurrence assessment. assesses degree co-occurrence taxa/types within dataset. strongest associations shown large positive numbers, strongest segregations large negative numbers. brainerd Brainerd-Robinson quantitative index. city-block metric similarity pairs samples/cases. bray Sorenson quantitative index (Bray Curtis modified version Sorenson index). jaccard Jaccard qualitative index. morisita Morisita-Horn quantitative index. sorenson Sorenson qualitative index.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Similarity \u2014 similarity","text":"Brainerd, G. W. (1951). Place Chronological Ordering Archaeological Analysis. American Antiquity, 16(04), 301-313. doi:10.2307/276979 . Bray, J. R. & Curtis, J. T. (1957). Ordination Upland Forest Communities Southern Wisconsin. Ecological Monographs, 27(4), 325-349. doi:10.2307/1942268 . Kintigh, K. (2006). Ceramic Dating Type Associations. J. Hantman R. (eds.), Managing Archaeological Data: Essays Honor Sylvia W. Gaines. Anthropological Research Paper, 57. Tempe, AZ: Arizona State University, p. 17-26. Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 . Robinson, W. S. (1951). Method Chronologically Ordering Archaeological Deposits. American Antiquity, 16(04), 293-301. doi:10.2307/276978 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Similarity \u2014 similarity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Similarity \u2014 similarity","text":"","code":"## Data from Huntley 2004, 2008 data(\"pueblo\")  ## Brainerd-Robinson measure (C <- similarity(pueblo, \"brainerd\")) #>             Atsinna   Cienega   Mirabal PdMuertos      Hesh   LowPesc      BoxS #> Cienega   164.36782                                                             #> Mirabal   152.38095 138.09524                                                   #> PdMuertos 179.31034 150.57471 152.38095                                         #> Hesh       82.75862  55.55556  66.66667 103.44828                               #> LowPesc    89.21023  62.00717  73.11828 109.89989 193.54839                     #> BoxS       82.75862  55.55556 114.28571 102.17114 103.70370 103.70370           #> Ojo Bon    27.58621  22.22222  19.04762  20.68966  26.66667  25.80645  22.22222 #> S170       27.58621  22.22222  19.04762  20.68966  26.66667  25.80645  22.22222 #>             Ojo Bon #> Cienega             #> Mirabal             #> PdMuertos           #> Hesh                #> LowPesc             #> BoxS                #> Ojo Bon             #> S170      190.53030 plot_spot(C)   ## Data from Magurran 1988, p. 166 data(\"aves\")  ## Jaccard measure (presence/absence data) similarity(aves, \"jaccard\") # 0.46 #>         unmanaged #> managed 0.4615385  ## Sorenson measure (presence/absence data) similarity(aves, \"sorenson\") # 0.63 #>         unmanaged #> managed 0.6315789  # Jaccard measure (Bray's formula ; count data) similarity(aves, \"bray\") # 0.44 #>         unmanaged #> managed 0.4442754  # Morisita-Horn measure (count data) similarity(aves, \"morisita\") # 0.81 #>         unmanaged #> managed 0.8134497","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"Measure Diversity Comparing Simulated Assemblages","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"","code":"# S4 method for DiversityIndex simulate(   object,   n = 1000,   step = 1,   interval = c(\"percentiles\", \"student\", \"normal\"),   level = 0.8,   progress = getOption(\"tabula.progress\") )","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"object DiversityIndex object. n non-negative integer giving number bootstrap replications. step integer giving increment sample size. interval character string giving type confidence interval returned. must one \"percentiles\" (sample quantiles, described Kintigh 1984; default), \"student\" \"normal\". unambiguous substring can given. level length-one numeric vector giving confidence level. progress logical scalar: progress bar displayed?","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"Returns DiversityIndex object.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"Baxter, M. J. (2001). Methodological Issues Study Assemblage Diversity. American Antiquity, 66(4), 715-725. doi:10.2307/2694184 . Kintigh, K. W. (1984). Measuring Archaeological Diversity Comparison Simulated Assemblages. American Antiquity, 49(1), 44-54. doi:10.2307/280511 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Measure Diversity by Comparing to Simulated Assemblages \u2014 simulate","text":"","code":"# \\donttest{ ## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Assemblage diversity size comparison ## Warning: this may take a few seconds! h <- heterogeneity(cantabria, method = \"shannon\") h_sim <- simulate(h) plot(h_sim)   r <- richness(cantabria, method = \"count\") r_sim <- simulate(r) plot(r_sim)  # }","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/tabula-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in tabula \u2014 tabula-deprecated","title":"Deprecated Functions in tabula \u2014 tabula-deprecated","text":"functions still work removed (defunct) next version.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/tabula-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tabula: Analysis and Visualization of Archaeological Count Data \u2014 tabula-package","title":"tabula: Analysis and Visualization of Archaeological Count Data \u2014 tabula-package","text":"easy way examine archaeological count data. package provides several tests measures diversity: heterogeneity evenness (Brillouin, Shannon, Simpson, etc.), richness rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover similarity (Brainerd-Robinson, etc.). allows easily visualize count data statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) Bertin (1977) diagrams, etc.","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/tabula-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tabula: Analysis and Visualization of Archaeological Count Data \u2014 tabula-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne 33607 Pessac cedex France","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Test \u2014 test_diversity","title":"Diversity Test \u2014 test_diversity","text":"Compares Shannon diversity samples.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Test \u2014 test_diversity","text":"","code":"test_diversity(object, ...)  # S4 method for matrix test_diversity(object, adjust = \"holm\", ...)  # S4 method for data.frame test_diversity(object, adjust = \"holm\", ...)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Test \u2014 test_diversity","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. adjust character string specifying method adjusting \\(p\\) values (see stats::p.adjust()).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Test \u2014 test_diversity","text":"numeric  matrix.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diversity Test \u2014 test_diversity","text":"test produces two sided pairwise comparisons: returns matrix adjusted \\(p\\) values.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diversity Test \u2014 test_diversity","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Test \u2014 test_diversity","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Test \u2014 test_diversity","text":"","code":"## Data from Conkey 1980, Kintigh 1989 data(\"cantabria\")  ## Shannon diversity test test_diversity(cantabria) #>                      Altamira Cueto de la Mina El Juyo El Cierro #> Cueto de la Mina 8.268025e-02               NA      NA        NA #> El Juyo          3.754291e-05       0.06016855      NA        NA #> El Cierro        3.526160e-05       0.05708787       1        NA #> La Paloma        3.255839e-05       0.01313679       1         1","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":null,"dir":"Reference","previous_headings":"","what":"Turnover \u2014 turnover","title":"Turnover \u2014 turnover","text":"Returns degree turnover taxa composition along gradient transect.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turnover \u2014 turnover","text":"","code":"turnover(object, ...)  index_whittaker(x, ...)  index_cody(x, ...)  index_routledge1(x, ...)  index_routledge2(x, ...)  index_routledge3(x, ...)  index_wilson(x, ...)  # S4 method for matrix turnover(   object,   method = c(\"whittaker\", \"cody\", \"routledge1\", \"routledge2\", \"routledge3\", \"wilson\"),   ... )  # S4 method for data.frame turnover(   object,   method = c(\"whittaker\", \"cody\", \"routledge1\", \"routledge2\", \"routledge3\", \"wilson\"),   ... )  # S4 method for matrix index_whittaker(x)  # S4 method for matrix index_cody(x)  # S4 method for matrix index_routledge1(x)  # S4 method for matrix index_routledge2(x)  # S4 method for matrix index_routledge3(x)  # S4 method for matrix index_wilson(x)","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turnover \u2014 turnover","text":"object, x \\(m \\times p\\) numeric matrix data.frame count data incidence data. data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. method character string specifying method used (see details). unambiguous substring can given.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turnover \u2014 turnover","text":"numeric vector.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Turnover \u2014 turnover","text":"following methods can used ascertain degree turnover taxa composition along gradient (\\(\\beta\\)-diversity) qualitative (presence/absence) data. assumes order matrix rows (\\(1\\) \\(n\\)) follows progression along gradient/transect. whittaker Whittaker measure. cody Cody measure. routledge1 Routledge first measure. routledge2 Routledge second measure. routledge3 Routledge third measure. exponential form second measure. wilson Wilson measure.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Turnover \u2014 turnover","text":"Cody, M. L. (1975). Towards theory continental species diversity: Bird distributions Mediterranean habitat gradients. M. L. Cody & J. M. Diamond (Eds.), Ecology Evolution Communities. Cambridge, MA: Harvard University Press, p. 214-257. Routledge, R. D. (1977). Whittaker's Components Diversity. Ecology, 58(5), 1120-1127. doi:10.2307/1936932 . Whittaker, R. H. (1960). Vegetation Siskiyou Mountains, Oregon California. Ecological Monographs, 30(3), 279-338. doi:10.2307/1943563 . Wilson, M. V., & Shmida, . (1984). Measuring Beta Diversity Presence-Absence Data. Journal Ecology, 72(3), 1055-1064. doi:10.2307/2259551 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Turnover \u2014 turnover","text":"N. Frerebeau","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turnover \u2014 turnover","text":"","code":"## Data from Magurran 1988, p. 162 data(\"woodland\")  ## Whittaker's measure turnover(woodland, \"whittaker\") # 1 #> [1] 1  ## Cody's measure turnover(woodland, \"cody\") # 3 #> [1] 3  ## Routledge's measures turnover(woodland, \"routledge1\") # 0.29 #> [1] 0.2857143 turnover(woodland, \"routledge2\") # 0.56 #> [1] 0.5594978 turnover(woodland, \"routledge3\") # 1.75 #> [1] 1.749794  ## Wilson and Shmida's measure turnover(woodland, \"wilson\") # 1 #> [1] 1","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":null,"dir":"Reference","previous_headings":"","what":"Trees Incidences \u2014 woodland","title":"Trees Incidences \u2014 woodland","text":"dataset presence absence trees six (10 x 10 m) quadarts along transect deciduous woodland.","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trees Incidences \u2014 woodland","text":"","code":"woodland","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Trees Incidences \u2014 woodland","text":"data.frame 6 rows (quadarts) 6 variables (tree species).","code":"","package":"tabula"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Trees Incidences \u2014 woodland","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":"","package":"tabula"},{"path":[],"package":"tabula"},{"path":"https://packages.tesselle.org/aion/articles/aion.html","id":"calendars","dir":"Articles","previous_headings":"","what":"Calendars","title":"Introduction to aion","text":"aion currently supports Julian Gregorian calendars (common eras latter, e.g.\u00a0Present, Common Era\u2026). calendar can defined using calendar() function: using shortcuts: creating date vectors time series, must specify calendar corresponding data (see ). allows select correct method converting dates rata die. Outputs generated aion expressed rata die default (can modified per-function basis). two exceptions plot() format() functions, default calendar specified package options (see ). can change default calendar used throughout package modifying aion.calendar option, per-function basis.","code":"## Create a calendar object ## (Gregorian Common Era) calendar(\"CE\") #> Common Era (CE): Gregorian years counted forwards from 0. ## Common Era (Gregorian) CE() #> Common Era (CE): Gregorian years counted forwards from 0.  ## Before Present (Gregorian) BP() #> Before Present (BP): Gregorian years counted backwards from 1950. ## Get default calendar getOption(\"aion.calendar\") #> Common Era (CE): Gregorian years counted forwards from 0.  ## Change default calendar to BP options(aion.calendar = BP()) getOption(\"aion.calendar\") #> Before Present (BP): Gregorian years counted backwards from 1950.  ## Set it back to Gregorian Common Era options(aion.calendar = CE()) getOption(\"aion.calendar\") #> Common Era (CE): Gregorian years counted forwards from 0.","package":"aion"},{"path":"https://packages.tesselle.org/aion/articles/aion.html","id":"vectors-of-dates","dir":"Articles","previous_headings":"","what":"Vectors of dates","title":"Introduction to aion","text":"base R, dates represented default number days since 1970-01-01 (Gregorian), negative values earlier dates (see help(Date)). aion uses different approach: allows create date vectors represented rata die (Reingold Dershowitz 2018), .e.\u00a0number days since 01-01-01 (Gregorian)1. makes possible get rid specific calendar make calculations easier. possible convert vector rata die dates (decimal) years calendar. fixed() function allows create vector rata die dates belonging specific calendar: rata die vector can converted dates (years) particular calendar: Rata die can represented (nicely formated) character vectors: rata die vector provides internal time representation aion time-series (want work numeric vectors represent year-based time scales, may interested era package).","code":"## Convert 2000-02-29 (Gregorian) to rata die fixed(2000, 02, 29, calendar = calendar(\"CE\")) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179  ## If days and months are missing, decimal years are expected fixed(2000.161, calendar = calendar(\"CE\")) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 ## Create a vector of 10 years BP (Gregorian) ## (every 20 years starting from 2000 BP) (years <- seq(from = 20000, by = -20, length.out = 10)) #>  [1] 20000 19980 19960 19940 19920 19900 19880 19860 19840 19820  ## Convert years to rata die (rd <- fixed(years, calendar = calendar(\"BP\"))) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -6592992 -6585687 -6578382 -6571077 -6563772 -6556467 -6549162 -6541857 #>  [9] -6534553 -6527248  ## Convert back to Gregorian years as_year(rd, calendar = calendar(\"CE\"))  # Common Era #>  [1] -18050 -18030 -18010 -17990 -17970 -17950 -17930 -17910 -17890 -17870 as_year(rd, calendar = calendar(\"BP\"))  # Before Present #>  [1] 20000 19980 19960 19940 19920 19900 19880 19860 19840 19820 as_year(rd, calendar = calendar(\"b2k\")) # Before 2000 #>  [1] 20050 20030 20010 19990 19970 19950 19930 19910 19890 19870 format(rd) # Default calendar (Gregorian Common Era) #>  [1] \"-18050 CE\" \"-18030 CE\" \"-18010 CE\" \"-17990 CE\" \"-17970 CE\" \"-17950 CE\" #>  [7] \"-17930 CE\" \"-17910 CE\" \"-17890 CE\" \"-17870 CE\" format(rd, format = \"ka\", calendar = calendar(\"BP\")) #>  [1] \"20 ka BP\"    \"19.98 ka BP\" \"19.96 ka BP\" \"19.94 ka BP\" \"19.92 ka BP\" #>  [6] \"19.9 ka BP\"  \"19.88 ka BP\" \"19.86 ka BP\" \"19.84 ka BP\" \"19.82 ka BP\"","package":"aion"},{"path":"https://packages.tesselle.org/aion/articles/aion.html","id":"time-series","dir":"Articles","previous_headings":"","what":"Time series","title":"Introduction to aion","text":"time series sequence observation time value pairs strictly increasing observation times. time series object \\(n \\times m \\times p\\) array, \\(n\\) number observations, \\(m\\) number series \\(p\\) columns third dimension containing extra variables series. can created numeric vector, matrix array. Time series terminal sampling times can retrieved expressed according different calendars (remember outputs expressed rata die default): Plot one time series:   Note aion uses astronomical notation Gregorian years (year 0).","code":"## Set seed for reproductibility set.seed(12345)  ## 6 x 50 observations... obs <- matrix(rnorm(300), nrow = 50, ncol = 6)  ## ...sampled every two years starting from 2000 BP spl <- seq(from = 2000, by = -2, length.out = 50)  ## Create time series (X <- series(object = obs, time = spl, calendar = BP())) #> 50 x 6 x 1 time series observed between -18627 and 17167 r.d. ## Time series duration span(X) # Default: rata die #> [1] 35794 span(X, calendar = CE()) #> [1] 98.99726  ## Time of first observation start(X) # Default: rata die #> [1] -18627 start(X, calendar = CE()) #> [1] -50  ## Time of last observation end(X) # Default: rata die #> [1] 17167 end(X, calendar = CE()) #> [1] 48  ## Sampling times time(X, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 1980 1978 1976 1974 1972 #> [16] 1970 1968 1966 1964 1962 1960 1958 1956 1954 1952 1950 1948 1946 1944 1942 #> [31] 1940 1938 1936 1934 1932 1930 1928 1926 1924 1922 1920 1918 1916 1914 1912 #> [46] 1910 1908 1906 1904 1902 ## Multiple plot plot(X) # Default calendar ## Extract the first series Y <- X[, 1, ]  ## Plot a single series plot(   Y,   calendar = b2k(), # b2k time scale   panel.first = graphics::grid() # Add a grid ) year_axis(side = 3, calendar = CE()) # Add a secondary time axis mtext(format(CE()), side = 3, line = 3) # Add secondary axis title","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/articles/extending.html","id":"calendars-and-dates","dir":"Articles","previous_headings":"","what":"Calendars and Dates","title":"Extending aion","text":"following example used build simple solar calendar 365 days year leap-year rule. ancient Egyptian calendar. find full details calculations detailed explanations Reingold Dershowitz (2018) (p.\u00a029). can define additional calendars creating S4 classes inherit TimeScale class exported aion: calendar defined, need build methods converting rata die calendar: Now can use calendar: definition new calendars, combined Julian Gregorian calendars already included aion, allows build conversion tools:","code":"## Egyptian calendar E <- setClass(   Class = \"EgyptianCalendar\",   prototype = list(     name = \"Egyptian\",     fixed = -272787,     direction = 1L,     year = 365   ),   contains = \"TimeScale\" ) ## Convert Egyptian dates to rata die ## NB: this method MUST return a RataDie object setMethod(   f = \"fixed\",   signature = c(     year = \"numeric\",     month = \"numeric\",     day = \"numeric\",      calendar = \"EgyptianCalendar\"   ),   definition = function(year, month, day, calendar) {     rd <- calendar_fixed(calendar) +        365 * (year - 1) +        30 * (month - 1) +        day - 1          as_fixed(rd)   } )  ## Convert rata die to Egyptian dates ## NB: this method MUST return a data.frame setMethod(   f = \"as_date\",   signature = c(object = \"numeric\", calendar = \"EgyptianCalendar\"),   definition = function(object, calendar) {     day <- object - calendar_fixed(calendar)     year <- day %/% 365 + 1     month <- (day %% 365) %/% 30 + 1     day <- day - 365 * (year - 1) - 30 * (month - 1) + 1          data.frame(year = year, month = month, day = day)   } )  ## Convert rata die to Egyptian years setMethod(   f = \"as_year\",   signature = c(object = \"numeric\", calendar = \"EgyptianCalendar\"),   definition = function(object, calendar, ...) {     (object - calendar_fixed(calendar)) %/% 365 + 1   } ) ## Create a calendar object cal <- E()  ## Convert 161/7/15 in rata die fixed(   year = 161,   month = 7,   day = 15,    calendar = cal ) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] -214193  ## Convert -214193 r.d. to an Egyptian date as_date(-214193, calendar = cal) #>   year month day #> 1  161     7  15 ## Build a conversion function from Gregorian CE years to Egyptian years Gregorian_to_Egyptian <- convert(CE(), E())  ## Convert 2023 (Gregorian) to the Egyptian calendar Gregorian_to_Egyptian(2023) #> [1] 2771","package":"aion"},{"path":"https://packages.tesselle.org/aion/articles/extending.html","id":"time-series","dir":"Articles","previous_headings":"","what":"Time Series","title":"Extending aion","text":"time series object simply \\(n \\times m \\times p\\) array, \\(n\\) number observations, \\(m\\) number series \\(p\\) columns third dimension containing extra variables series. array comes extra time slot store observations times expressed rata die. can create classes inherits TimeSeries class. example, can create class represent results calibration radiocarbon dates (code comes ananke package): methods defined aion can used objects belonging new class (e.g.\u00a0plot()).","code":".CalibratedAges <- setClass(   Class = \"CalibratedAges\",   slots = c(     ages = \"numeric\",     # Stores the radiocarbon ages to be calibrated     errors = \"numeric\",   # Store the standard deviation of the radiocarbon ages     curves = \"character\"  # Store the name of the calibration curve   ),   contains = \"TimeSeries\" )","package":"aion"},{"path":[],"package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Archaeological Time Series","text":"Base R ships lot functionality useful time series, particular stats package. However, features adapted archaeological time series. indeed defined given calendar era, can involve dates far past sampling observation time (cases) constant. aion provides system classes methods represent work time-series. Dates represented rata die (Reingold Dershowitz 2018), .e.\u00a0number days since 01-01-01 (Gregorian), negative values earlier dates. allows represent dates independently calendar: makes calculations comparisons easier. time series created aion, calendar can used printing plotting data (defaults Gregorian Common Era; see vignette(\"aion\")). aion provide tools temporal modeling. Instead, offers simple API can used specialized packages.","code":"To cite aion in publications use:    Frerebeau N, Roe J (2023). _aion: Archaeological Time Series_.   Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.8032278 <https://doi.org/10.5281/zenodo.8032278>,   R package version 1.0.1, <https://packages.tesselle.org/aion/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau and Joe Roe},     title = {{aion: Archaeological Time Series}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.0.1},     url = {https://packages.tesselle.org/aion/},     doi = {10.5281/zenodo.8032278},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"aion"},{"path":"https://packages.tesselle.org/aion/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Archaeological Time Series","text":"can install released version aion CRAN : development version GitHub :","code":"install.packages(\"aion\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/aion\")","package":"aion"},{"path":"https://packages.tesselle.org/aion/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Archaeological Time Series","text":"","code":"## Load package library(aion) ## Set seed for reproductibility set.seed(12345)  ## Create 6 time-series of 50 observations ## Sampled every two years starting from 2000 BP X <- series(   object = matrix(rnorm(300), nrow = 50, ncol = 6),   time = seq(from = 2000, by = -2, length.out = 50),   calendar = calendar(\"BP\") )  ## Plot plot(X) # Default calendar","package":"aion"},{"path":"https://packages.tesselle.org/aion/index.html","id":"related-works","dir":"","previous_headings":"","what":"Related Works","title":"Archaeological Time Series","text":"era: provides consistent representation year-based time scales numeric vector associated era.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Archaeological Time Series","text":"Please note aion project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/paper.html","id":null,"dir":"","previous_headings":"","what":"Summary","title":"Summary","text":"Numerous R packages developped describe, analyze, model temporal data context archaeological studies broadest sense. packages encompass various functionalities, including handling radiocarbon data (e.g., \u2018Bchron\u2019 @R-Bchron \u2018rcarbon\u2019 @R-rcarbon), Optically Stimulated Luminescence dating (\u2018Luminescence\u2019 @R-Luminescence), Bayesian chronological modeling (\u2018ArchaeoPhases\u2019 @R-ArchaeoPhases), using paleoenvironmental proxies (e.g., \u2018shoredate\u2019 @R-shoredate), temporal data (e.g., \u2018kairos\u2019 @R-kairos). multitude packages underscores significance computational approaches archaeology [@schmidt2020]. However, also presents major challenge package employs representation temporal information. Consequently, exchanging data different packages within data workflow becomes even arduous. \u2018aion\u2019 designed provide consistent framework representing archaeological time series.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/paper.html","id":"statement-of-need","dir":"","previous_headings":"","what":"Statement of need","title":"Summary","text":"R ships lot functionality useful time series, particular base stats [@R-stats], zoo [@R-zoo] packages. However, features adapted archaeological time series. Archaeological data typically collected field excavations surveys, resulting irregularly spaced observation times. Although several packages can handle irregular time series, way represent dates means easily used archaeological series. indeed defined given calendar era importantly can involve dates far past. aion provides system classes methods represent work time series. package provide tools temporal analysis modeling. Instead, offers system classes methods represent work archaeological time series. API can extended used specialized packages.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/paper.html","id":"functionality","dir":"","previous_headings":"","what":"Functionality","title":"Summary","text":"base R, dates represented default number days since 1970-01-01 (Gregorian), negative values earlier dates. aion uses different approach: allows create date vectors represented rata die [@reingold2018], .e.\u00a0number days since 01-01-01 (Gregorian). allows represent dates independently calendar makes calculations comparisons easier. aion time series represented S4 class inherits base array. time series object n\u2005\u00d7\u2005m\u2005\u00d7\u2005p array, n number observations, m number series p columns third dimension containing extra variables series. array comes extra time slot store observations times expressed rata die. output produced aion can formatted (virtually) calendar, long calendar defined associated conversion methods available. aion natively supports Julian Gregorian calendars (common eras latter, e.g.\u00a02000, Present, () Common Era\u2026) allows create custom calendars.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":[],"package":"aion"},{"path":[],"package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/AD-class.html","id":null,"dir":"Reference","previous_headings":"","what":"AD \u2014 AD-class","title":"AD \u2014 AD-class","text":"S4 class represent (Gregorian) AD era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/AD-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"AD \u2014 AD-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BC-class.html","id":null,"dir":"Reference","previous_headings":"","what":"BC \u2014 BC-class","title":"BC \u2014 BC-class","text":"S4 class represent (Gregorian) BC era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BC-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BC \u2014 BC-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BCE-class.html","id":null,"dir":"Reference","previous_headings":"","what":"BCE \u2014 BCE-class","title":"BCE \u2014 BCE-class","text":"S4 class represent (Gregorian) BCE era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BCE-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BCE \u2014 BCE-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BP-class.html","id":null,"dir":"Reference","previous_headings":"","what":"BP \u2014 BP-class","title":"BP \u2014 BP-class","text":"S4 class represent (Gregorian) BP era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/BP-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BP \u2014 BP-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/CE-class.html","id":null,"dir":"Reference","previous_headings":"","what":"CE \u2014 CE-class","title":"CE \u2014 CE-class","text":"S4 class represent (Gregorian) CE era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/CE-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"CE \u2014 CE-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/GregorianCalendar-class.html","id":null,"dir":"Reference","previous_headings":"","what":"GregorianCalendar \u2014 GregorianCalendar-class","title":"GregorianCalendar \u2014 GregorianCalendar-class","text":"S4 class represent Gregorian calendar.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/GregorianCalendar-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"GregorianCalendar \u2014 GregorianCalendar-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/JulianCalendar-class.html","id":null,"dir":"Reference","previous_headings":"","what":"JulianCalendar \u2014 JulianCalendar-class","title":"JulianCalendar \u2014 JulianCalendar-class","text":"S4 class represent Julian calendar.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/JulianCalendar-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"JulianCalendar \u2014 JulianCalendar-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/RataDie-class.html","id":null,"dir":"Reference","previous_headings":"","what":"RataDie \u2014 RataDie-class","title":"RataDie \u2014 RataDie-class","text":"S4 class represent vector rata die.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/RataDie-class.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RataDie \u2014 RataDie-class","text":"Rata die (fixed date) represented number days since 01-01-01 (Gregorian), negative values earlier dates. intended date integer value, enforced internal representation.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/RataDie-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"RataDie \u2014 RataDie-class","text":".Data numeric vector giving rata die values.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/RataDie-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"RataDie \u2014 RataDie-class","text":"class inherits numeric.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/RataDie-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"RataDie \u2014 RataDie-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeScale-class.html","id":null,"dir":"Reference","previous_headings":"","what":"TimeScale \u2014 TimeScale-class","title":"TimeScale \u2014 TimeScale-class","text":"virtual S4 class represent calendar.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeScale-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"TimeScale \u2014 TimeScale-class","text":"label character string specifying abbreviated label time scale. name character string specifying name time scale. epoch numeric value specifying epoch year years counted (starting date calendar, years). Allows define multiple era calendar. fixed numeric value specifying reference date calendar (rata die). direction integer specifying years counted backwards (-1) forwards (1) epoch. year numeric value giving average length year solar days.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeScale-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"TimeScale \u2014 TimeScale-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeSeries-class.html","id":null,"dir":"Reference","previous_headings":"","what":"TimeSeries \u2014 TimeSeries-class","title":"TimeSeries \u2014 TimeSeries-class","text":"S4 class represent time series.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeSeries-class.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TimeSeries \u2014 TimeSeries-class","text":"time series object \\(n x m x p\\) array, \\(n\\) number observations, \\(m\\) number series \\(p\\) columns third dimension containing extra variables series.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeSeries-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"TimeSeries \u2014 TimeSeries-class","text":".Data \\(n x m x p\\) numeric array giving observed time-series values. .Time length-\\(n\\) RataDie object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeSeries-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"TimeSeries \u2014 TimeSeries-class","text":"class inherits array.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/TimeSeries-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"TimeSeries \u2014 TimeSeries-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/aion-package.html","id":null,"dir":"Reference","previous_headings":"","what":"aion: Archaeological Time Series \u2014 aion-package","title":"aion: Archaeological Time Series \u2014 aion-package","text":"toolkit archaeological time series. package provides system classes methods represent work archaeological time series. Dates represented \"rata die\" can converted (virtually) calendar defined Reingold Dershowitz (2018) doi:10.1017/9781107415058 . packages offers simple API can used specialized packages.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/aion-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"aion: Archaeological Time Series \u2014 aion-package","text":"aion uses following options() configure behaviour: aion.precision: integer indicating number decimal places (defaults NA). aion.calendar: TimeScale object (default calendar printing).","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/aion-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"aion: Archaeological Time Series \u2014 aion-package","text":"Full list authors contributors (alphabetic order): Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":null,"dir":"Reference","previous_headings":"","what":"Arithmetic Operators \u2014 arithmetic","title":"Arithmetic Operators \u2014 arithmetic","text":"Operators performing arithmetic operations.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arithmetic Operators \u2014 arithmetic","text":"","code":"# S4 method for RataDie,RataDie Arith(e1, e2)  # S4 method for numeric,RataDie Arith(e1, e2)  # S4 method for RataDie,numeric Arith(e1, e2)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arithmetic Operators \u2014 arithmetic","text":"e1, e2 RataDie object numeric vector.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Arithmetic Operators \u2014 arithmetic","text":"logical vector.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Arithmetic Operators \u2014 arithmetic","text":"Rata die converted plain numeric vector computation longer makes sense temporal terms.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Arithmetic Operators \u2014 arithmetic","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/arithmetic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arithmetic Operators \u2014 arithmetic","text":"","code":"## Vectors of years x <- fixed(c(-350, 31, 1072, 576, 1130), calendar = CE()) y <- fixed(c(1494, 1645, -869, 1440, 1851), calendar = CE())  ## Move forward in time x + y #> Rata die: number of days since 01-01-01 (Gregorian). #> [1]  417109  611418   73414  735599 1088058  ## Move backward in time x - y #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] -673507 -589502  708936 -315569 -263340  ## Not rata die anymore x * y #> [1]  -69907940292    6579840680 -124300159175  110380523760  278630563941","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":null,"dir":"Reference","previous_headings":"","what":"Date Conversion from Rata Die \u2014 as_date","title":"Date Conversion from Rata Die \u2014 as_date","text":"Date Conversion Rata Die","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Date Conversion from Rata Die \u2014 as_date","text":"","code":"as_date(object, calendar)  # S4 method for numeric,GregorianCalendar as_date(object, calendar)  # S4 method for numeric,JulianCalendar as_date(object, calendar)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Date Conversion from Rata Die \u2014 as_date","text":"object RataDie object (see fixed()). calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Date Conversion from Rata Die \u2014 as_date","text":"numeric vector (decimal) years.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Date Conversion from Rata Die \u2014 as_date","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Date Conversion from Rata Die \u2014 as_date","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_date.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Date Conversion from Rata Die \u2014 as_date","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts a Date to a Decimal of its Year \u2014 as_decimal","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"Converts Date Decimal Year","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"","code":"as_decimal(year, month, day, calendar)  # S4 method for numeric,numeric,numeric,TimeScale as_decimal(year, month, day, calendar)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"year numeric vector years. month day missing, decimal years expected. month numeric vector months. day numeric vector days. calendar TimeScale object specifying calendar year, month day (see calendar()).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"numeric vector (ecimal years.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_decimal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts a Date to a Decimal of its Year \u2014 as_decimal","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to Rata Die \u2014 as_fixed","title":"Coerce to Rata Die \u2014 as_fixed","text":"Coerce Rata Die","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to Rata Die \u2014 as_fixed","text":"","code":"as_fixed(from)  # S4 method for numeric as_fixed(from)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to Rata Die \u2014 as_fixed","text":"numeric vector rata die.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to Rata Die \u2014 as_fixed","text":"RataDie object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Coerce to Rata Die \u2014 as_fixed","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to Rata Die \u2014 as_fixed","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_fixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to Rata Die \u2014 as_fixed","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":null,"dir":"Reference","previous_headings":"","what":"Year Conversion from Rata Die \u2014 as_year","title":"Year Conversion from Rata Die \u2014 as_year","text":"Year Conversion Rata Die","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Year Conversion from Rata Die \u2014 as_year","text":"","code":"as_year(object, calendar, ...)  # S4 method for numeric,GregorianCalendar as_year(object, calendar, decimal = TRUE)  # S4 method for numeric,JulianCalendar as_year(object, calendar)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Year Conversion from Rata Die \u2014 as_year","text":"object RataDie object (see fixed()). calendar TimeScale object specifying target calendar (see calendar()). ... Currently used. decimal logical scalar: decimal years returned? FALSE, decimal part dropped.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Year Conversion from Rata Die \u2014 as_year","text":"numeric vector (decimal) years.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Year Conversion from Rata Die \u2014 as_year","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Year Conversion from Rata Die \u2014 as_year","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/as_year.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Year Conversion from Rata Die \u2014 as_year","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/b2k-class.html","id":null,"dir":"Reference","previous_headings":"","what":"b2k \u2014 b2k-class","title":"b2k \u2014 b2k-class","text":"S4 class represent (Gregorian) b2k era.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/b2k-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"b2k \u2014 b2k-class","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":null,"dir":"Reference","previous_headings":"","what":"Calendar \u2014 calendar","title":"Calendar \u2014 calendar","text":"Calendar","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calendar \u2014 calendar","text":"","code":"calendar(object)  # S4 method for character calendar(object)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calendar \u2014 calendar","text":"object character string specifying abbreviated label time scale (see details) object extract time scale.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calendar \u2014 calendar","text":"TimeScale object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calendar \u2014 calendar","text":"following time scales available:","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calendar \u2014 calendar","text":"Inspired era::era() Joe Roe.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calendar \u2014 calendar","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calendar \u2014 calendar","text":"","code":"## Define time scales calendar(\"BP\") #> Before Present (BP): Gregorian years counted backwards from 1950. calendar(\"AD\") #> Anno Domini (AD): Gregorian years counted forwards from 0. calendar(\"julian\") #> Julian years counted forwards from 1.  ## Shortcuts BP() #> Before Present (BP): Gregorian years counted backwards from 1950. AD() #> Anno Domini (AD): Gregorian years counted forwards from 0. J() #> Julian years counted forwards from 1.","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":null,"dir":"Reference","previous_headings":"","what":"Calendar Parameters \u2014 calendar_get","title":"Calendar Parameters \u2014 calendar_get","text":"Calendar Parameters","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calendar Parameters \u2014 calendar_get","text":"","code":"calendar_label(object)  calendar_name(object)  calendar_unit(object)  calendar_epoch(object)  calendar_fixed(object)  calendar_direction(object)  # S4 method for TimeScale calendar_label(object)  # S4 method for TimeScale calendar_name(object)  # S4 method for TimeScale calendar_unit(object)  # S4 method for TimeScale calendar_epoch(object)  # S4 method for TimeScale calendar_fixed(object)  # S4 method for TimeScale calendar_direction(object)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calendar Parameters \u2014 calendar_get","text":"object TimeScale object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calendar Parameters \u2014 calendar_get","text":"calendar_label() returns character string specifying abbreviated label time scale. calendar_name() returns character string specifying name time scale. calendar_unit() returns character string specifying name calendar. calendar_fixed() returns length-one numeric vector specifying reference date calendar (rata die). calendar_epoch() returns length-one numeric vector specifying epoch year years counted (starting date calendar, years). calendar_direction() returns length-one integer vector specifying years counted backwards (\\(-1\\)) forwards (\\(1\\)) epoch. sign calendar_direction() relevant.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calendar Parameters \u2014 calendar_get","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/calendar_get.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calendar Parameters \u2014 calendar_get","text":"","code":"## Define time scales calendar(\"BP\") #> Before Present (BP): Gregorian years counted backwards from 1950. calendar(\"AD\") #> Anno Domini (AD): Gregorian years counted forwards from 0. calendar(\"julian\") #> Julian years counted forwards from 1.  ## Shortcuts BP() #> Before Present (BP): Gregorian years counted backwards from 1950. AD() #> Anno Domini (AD): Gregorian years counted forwards from 0. J() #> Julian years counted forwards from 1.","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":null,"dir":"Reference","previous_headings":"","what":"Calendar Converter \u2014 convert","title":"Calendar Converter \u2014 convert","text":"Interconverts dates variety calendars.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calendar Converter \u2014 convert","text":"","code":"convert(from, to, ...)  # S4 method for character,character convert(from, to)  # S4 method for TimeScale,TimeScale convert(from, to)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calendar Converter \u2014 convert","text":"TimeScale object describing source calendar. TimeScale object describing target calendar. ... Currently used.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calendar Converter \u2014 convert","text":"function called single numeric argument (factional years) converts years one calendar another.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calendar Converter \u2014 convert","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/convert.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calendar Converter \u2014 convert","text":"","code":"## Define time scales BP <- calendar(\"BP\") AD <- calendar(\"AD\")  ## Make conversion functions BP_to_AD <- convert(BP, AD) AD_to_BP <- convert(AD, BP)  ## Convert years BP_to_AD(0) #> [1] 1950 AD_to_BP(1950) #> [1] 0","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Data Frame \u2014 data.frame","title":"Coerce to a Data Frame \u2014 data.frame","text":"Coerce Data Frame","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Data Frame \u2014 data.frame","text":"","code":"# S4 method for TimeSeries as.data.frame(x, ..., calendar = NULL)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Data Frame \u2014 data.frame","text":"x TimeSeries object. ... parameters passed data.frame(). calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Data Frame \u2014 data.frame","text":"long data.frame following columns: time (decimal) years time series sampled. series name time series. variable name variables. value observed value.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Data Frame \u2014 data.frame","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/data.frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce to a Data Frame \u2014 data.frame","text":"","code":"## Create time-series of 20 observations  ## Univariate ## Sampled every years starting from 1029 BCE (X <- series(rnorm(30), time = 1029:1000, calendar = BCE())) #> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.  ## Terminal and sampling times (returns rata die) start(X) #> [1] -376199 end(X) #> [1] -365607 time(X) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277 #> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990 #> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702 #> [28] -366337 -365972 -365607 span(X) #> [1] 10592  ## Multivariate ## Sampled every century starting from 1000 CE (Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE())) #> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.  ## Terminal and sampling times (returns Gregorian Common Era years) start(Y, calendar = CE()) #> [1] 1000 end(Y, calendar = CE()) #> [1] 1029 time(Y, calendar = CE()) #>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 span(Y, calendar = CE()) #> [1] 29.99726  ## Coerce to data frame df <- as.data.frame(Y, calendar = BP()) head(df) #>   time series variable      value #> 1  950     S1        A  2.0329711 #> 2  949     S1        A  1.5869806 #> 3  948     S1        A  1.0943139 #> 4  947     S1        A -0.6339700 #> 5  946     S1        A -0.9750187 #> 6  945     S1        A  0.7335285","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/dates.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample Data from Reingold and Dershowitz (2018) \u2014 dates","title":"Sample Data from Reingold and Dershowitz (2018) \u2014 dates","text":"dataset 33 dates years -1000 2100 equivalents differents calendars.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/dates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample Data from Reingold and Dershowitz (2018) \u2014 dates","text":"","code":"dates","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/dates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Sample Data from Reingold and Dershowitz (2018) \u2014 dates","text":"data.frame 33 rows 14 variables: rata_die Rata die. weekday Week day. jd Julian day. mjd Modified Julian day. unix Unix. gregorian_year, gregorian_month, gregorian_day Gregorian date. julian_year, julian_month, julian_day Julian date. egyptian_year, egyptian_month, egyptian_day Egyptian date.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/dates.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sample Data from Reingold and Dershowitz (2018) \u2014 dates","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Rata Die (Fixed Date) \u2014 fixed","title":"Rata Die (Fixed Date) \u2014 fixed","text":"Rata Die (Fixed Date)","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rata Die (Fixed Date) \u2014 fixed","text":"","code":"fixed(year, month, day, calendar, ...)  # S4 method for numeric,missing,missing,GregorianCalendar fixed(year, calendar, scale = 1)  # S4 method for numeric,numeric,numeric,GregorianCalendar fixed(year, month, day, calendar)  # S4 method for numeric,numeric,numeric,JulianCalendar fixed(year, month, day, calendar)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rata Die (Fixed Date) \u2014 fixed","text":"year numeric vector years. month day missing, decimal years expected. month numeric vector months. day numeric vector days. calendar TimeScale object specifying calendar year, month day (see calendar()). ... Currently used. scale length-one integer vector specifying number years represented one unit. power 10 (.e. 1000 means ka).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rata Die (Fixed Date) \u2014 fixed","text":"RataDie object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rata Die (Fixed Date) \u2014 fixed","text":"Rata die represented number days since 01-01-01 (Gregorian), negative values earlier dates.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rata Die (Fixed Date) \u2014 fixed","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rata Die (Fixed Date) \u2014 fixed","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rata Die (Fixed Date) \u2014 fixed","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":null,"dir":"Reference","previous_headings":"","what":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"Convenient functions conversion rata die given Gregorian era.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"","code":"fixed_from_BP(year, month, day)  fixed_to_BP(object)  fixed_from_BC(year, month, day)  fixed_to_BC(object)  fixed_from_BCE(year, month, day)  fixed_to_BCE(object)  fixed_from_AD(year, month, day)  fixed_to_AD(object)  fixed_from_CE(year, month, day)  fixed_to_CE(object)  fixed_from_b2k(year, month, day)  fixed_to_b2k(object)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"year numeric vector years. month day missing, decimal years expected. month numeric vector months. day numeric vector days. object RataDie object (see fixed()).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"fixed_from_*() returns RataDie object. fixed_to_*() returns numeric vector Gregorian years.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"astronomical notation used Gregorian years (year 0).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_gregorian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rata Die Conversion to and from Gregorian Years \u2014 fixed_gregorian","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":null,"dir":"Reference","previous_headings":"","what":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"Convenient functions conversion rata die.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"","code":"fixed_from_julian(year, month, day)  fixed_to_julian(object)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"year numeric vector years. month day missing, decimal years expected. month numeric vector months. day numeric vector days. object RataDie object (see fixed()).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"fixed_from_julian() returns RataDie object. fixed_to_julian() returns numeric vector Julian years.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"Reingold, E. M. Dershowitz, N. (2018). Calendrical Calculations: Ultimate Edition. Cambridge University Press. doi:10.1017/9781107415058 .","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/fixed_julian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rata Die Conversion to and from Julian Years \u2014 fixed_julian","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":null,"dir":"Reference","previous_headings":"","what":"Date Conversion to Character \u2014 format","title":"Date Conversion to Character \u2014 format","text":"Date Conversion Character","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Date Conversion to Character \u2014 format","text":"","code":"# S4 method for TimeScale format(x)  # S4 method for RataDie format(   x,   format = c(\"a\", \"ka\", \"Ma\", \"Ga\"),   label = TRUE,   calendar = getOption(\"aion.calendar\") )","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Date Conversion to Character \u2014 format","text":"x RataDie object. format character string specifying prefix. one \"\", \"ka\", \"Ma\" \"Ga\". TRUE, good guess appropriate format made. label logical scalar: label calendar displayed? calendar TimeScale object specifying target calendar (see calendar()).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Date Conversion to Character \u2014 format","text":"character vector representing date.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Date Conversion to Character \u2014 format","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Date Conversion to Character \u2014 format","text":"","code":"## R 1.0.0 (y <- fixed(year = 2000, month = 02, day = 29, calendar = CE())) #> Rata die: number of days since 01-01-01 (Gregorian). #> [1] 730179 as_date(y, calendar = CE()) #>   year month day #> 1 2000     2  29 as_year(y, calendar = CE()) #> [1] 2000.161  ## Create a vector of years BP (Gregorian) ## (every two years starting from 2000 BP) (years <- seq(from = 2000, by = -2, length.out = 10)) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982 ## Convert years to rata die (rd <- fixed(years, calendar = BP())) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 ## Convert back to Gregorian years BP as_year(rd, calendar = BP()) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982  ## More convenient (rd <- fixed_from_BP(years)) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -18627 -17897 -17166 -16436 -15705 -14975 -14244 -13514 -12783 -12053 fixed_to_BP(rd) #>  [1] 2000 1998 1996 1994 1992 1990 1988 1986 1984 1982","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":null,"dir":"Reference","previous_headings":"","what":"Gregorian Calendar \u2014 gregorian","title":"Gregorian Calendar \u2014 gregorian","text":"Gregorian Calendar","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gregorian Calendar \u2014 gregorian","text":"","code":"BP(...)  b2k(...)  BC(...)  BCE(...)  AD(...)  CE(...)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gregorian Calendar \u2014 gregorian","text":"... Currently used.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gregorian Calendar \u2014 gregorian","text":"GregorianCalendar object.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Gregorian Calendar \u2014 gregorian","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/gregorian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gregorian Calendar \u2014 gregorian","text":"","code":"## Define time scales calendar(\"BP\") #> Before Present (BP): Gregorian years counted backwards from 1950. calendar(\"AD\") #> Anno Domini (AD): Gregorian years counted forwards from 0. calendar(\"julian\") #> Julian years counted forwards from 1.  ## Shortcuts BP() #> Before Present (BP): Gregorian years counted backwards from 1950. AD() #> Anno Domini (AD): Gregorian years counted forwards from 0. J() #> Julian years counted forwards from 1.","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":null,"dir":"Reference","previous_headings":"","what":"Heat Map \u2014 image","title":"Heat Map \u2014 image","text":"Heat Map","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heat Map \u2014 image","text":"","code":"# S4 method for TimeSeries image(x, calendar = getOption(\"aion.calendar\"), k = 1, ...)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heat Map \u2014 image","text":"x TimeSeries object. calendar TimeScale object specifying target calendar (see calendar()). k integer specifying slice x along third dimension plotted. ... parameters passed graphics::image().","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heat Map \u2014 image","text":"image() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heat Map \u2014 image","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/image.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heat Map \u2014 image","text":"","code":"## Create 6 time-series of 50 observations ## Sampled every two years starting from 2000 BP X <- series(   object = matrix(rnorm(300), nrow = 50, ncol = 6),   time = seq(2000, by = -2, length.out = 50),   calendar = BP() )  ## Image image(X, calendar = CE())","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/is.html","id":null,"dir":"Reference","previous_headings":"","what":"Is an Object a Calendar? \u2014 is","title":"Is an Object a Calendar? \u2014 is","text":"Test inheritance relationships object calendar class.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/is.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is an Object a Calendar? \u2014 is","text":"","code":"is_gregorian(object)  is_julian(object)  # S4 method for ANY is_gregorian(object)  # S4 method for ANY is_julian(object)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/is.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is an Object a Calendar? \u2014 is","text":"object R object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/is.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Is an Object a Calendar? \u2014 is","text":"logical vector.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/is.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Is an Object a Calendar? \u2014 is","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":null,"dir":"Reference","previous_headings":"","what":"Julian Calendar \u2014 julian","title":"Julian Calendar \u2014 julian","text":"Julian Calendar","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Julian Calendar \u2014 julian","text":"","code":"J(...)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Julian Calendar \u2014 julian","text":"... Currently used.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Julian Calendar \u2014 julian","text":"JulianCalendar object.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Julian Calendar \u2014 julian","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/julian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Julian Calendar \u2014 julian","text":"","code":"## Define time scales calendar(\"BP\") #> Before Present (BP): Gregorian years counted backwards from 1950. calendar(\"AD\") #> Anno Domini (AD): Gregorian years counted forwards from 0. calendar(\"julian\") #> Julian years counted forwards from 1.  ## Shortcuts BP() #> Before Present (BP): Gregorian years counted backwards from 1950. AD() #> Anno Domini (AD): Gregorian years counted forwards from 0. J() #> Julian years counted forwards from 1.","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Time Series \u2014 plot","title":"Plot Time Series \u2014 plot","text":"Plot Time Series","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Time Series \u2014 plot","text":"","code":"# S4 method for TimeSeries,missing plot(   x,   facet = c(\"multiple\", \"single\"),   calendar = getOption(\"aion.calendar\"),   panel = graphics::lines,   flip = FALSE,   ncol = NULL,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Time Series \u2014 plot","text":"x TimeSeries object. facet character string specifying whether series plotted separately (common time axis) single plot? must one \"multiple\" \"single\". unambiguous substring can given. calendar TimeScale object specifying target calendar (see calendar()). panel function form function(x, y, ...) gives action carried panel display. default graphics::lines(). flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... parameters passed panel (e.g. graphical parameters).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Time Series \u2014 plot","text":"plot() called side-effects: results graphic displayed. Invisibly returns x.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Time Series \u2014 plot","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Time Series \u2014 plot","text":"","code":"## Create 6 time-series of 50 observations ## Sampled every two years starting from 2000 BP X <- series(   object = matrix(rnorm(300), nrow = 50, ncol = 6),   time = seq(2000, by = -2, length.out = 50),   calendar = BP() )  ## Multiple plot(X) # Default calendar  plot(X, calendar = BP(), flip = TRUE) # BP  plot(X, calendar = b2k(), ncol = 1) # b2k   ## Single plot(X, facet = \"single\") # CE  plot(X, facet = \"single\", calendar = BP()) # BP   ## Create 6 x 3 time-series of 50 observations ## Sampled every two years starting from 2000 BP X <- series(   object = array(rnorm(900), dim = c(50, 6, 3)),   time = seq(2000, by = 2, length.out = 50),   calendar = BP() ) plot(X, calendar = BP(), flip = TRUE) # BP  plot(X, calendar = b2k(), ncol = 1) # b2k   ## Graphical parameters plot(X, lwd = c(1, 2, 3), col = c(\"#004488\", \"#DDAA33\", \"#BB5566\"))  plot(X, type = \"b\", pch = 16, col = c(\"#004488\", \"#DDAA33\", \"#BB5566\"))  plot(X, type = \"p\", pch = c(16, 17, 18), cex = c(1, 2, 3))","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/pretty.html","id":null,"dir":"Reference","previous_headings":"","what":"Pretty Breakpoints \u2014 pretty","title":"Pretty Breakpoints \u2014 pretty","text":"Pretty Breakpoints","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/pretty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pretty Breakpoints \u2014 pretty","text":"","code":"# S4 method for RataDie pretty(x, calendar = getOption(\"aion.calendar\"), ...)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/pretty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pretty Breakpoints \u2014 pretty","text":"x RataDie object. calendar TimeScale object specifying target calendar (see calendar()). ... parameters passed base::pretty().","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/pretty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pretty Breakpoints \u2014 pretty","text":"RataDie object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/pretty.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pretty Breakpoints \u2014 pretty","text":"pretty() computes vector increasing numbers \"pretty\" decimal notation calendar. Pretty breakpoints converted rata die.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Time Series \u2014 series","title":"Create Time Series \u2014 series","text":"Create Time Series","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Time Series \u2014 series","text":"","code":"series(object, time, calendar, ...)  # S4 method for array,RataDie,missing series(object, time, names = NULL)  # S4 method for array,numeric,TimeScale series(object, time, calendar, scale = 1, names = NULL)  # S4 method for matrix,numeric,TimeScale series(object, time, calendar, scale = 1, names = NULL)  # S4 method for matrix,RataDie,missing series(object, time, names = NULL)  # S4 method for numeric,numeric,TimeScale series(object, time, calendar, scale = 1, names = NULL)  # S4 method for numeric,RataDie,missing series(object, time, names = NULL)  # S4 method for data.frame,numeric,TimeScale series(object, time, calendar, scale = 1, names = NULL)  # S4 method for data.frame,RataDie,missing series(object, time, names = NULL)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Time Series \u2014 series","text":"object numeric vector, matrix array observed time-series values. data.frame coerced numeric matrix via data.matrix(). time numeric vector (decimal) years RataDie object (see fixed()). calendar TimeScale object specifying calendar time (see calendar()). missing, time must RataDie object. ... Currently used. names character string specifying names time series. scale length-one numeric vector specifying number years represented one unit. power 10 (.e. 1000 means ka).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Time Series \u2014 series","text":"TimeSeries object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create Time Series \u2014 series","text":"Data sorted chronological order.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create Time Series \u2014 series","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/series.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create Time Series \u2014 series","text":"","code":"## Create time-series of 20 observations  ## Univariate ## Sampled every years starting from 1029 BCE (X <- series(rnorm(30), time = 1029:1000, calendar = BCE())) #> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.  ## Terminal and sampling times (returns rata die) start(X) #> [1] -376199 end(X) #> [1] -365607 time(X) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277 #> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990 #> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702 #> [28] -366337 -365972 -365607 span(X) #> [1] 10592  ## Multivariate ## Sampled every century starting from 1000 CE (Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE())) #> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.  ## Terminal and sampling times (returns Gregorian Common Era years) start(Y, calendar = CE()) #> [1] 1000 end(Y, calendar = CE()) #> [1] 1029 time(Y, calendar = CE()) #>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 span(Y, calendar = CE()) #> [1] 29.99726  ## Coerce to data frame df <- as.data.frame(Y, calendar = BP()) head(df) #>   time series variable      value #> 1  950     S1        A  0.6980847 #> 2  949     S1        A -0.9906964 #> 3  948     S1        A  0.2091514 #> 4  947     S1        A  0.7175182 #> 5  946     S1        A -0.1098521 #> 6  945     S1        A  1.8658180","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":null,"dir":"Reference","previous_headings":"","what":"Duration of Time Series \u2014 span","title":"Duration of Time Series \u2014 span","text":"Get duration.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Duration of Time Series \u2014 span","text":"","code":"span(x, ...)  # S4 method for TimeSeries span(x, calendar = NULL)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Duration of Time Series \u2014 span","text":"x TimeSeries object. ... Currently used. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Duration of Time Series \u2014 span","text":"numeric vector.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Duration of Time Series \u2014 span","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/span.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Duration of Time Series \u2014 span","text":"","code":"## Create time-series of 20 observations  ## Univariate ## Sampled every years starting from 1029 BCE (X <- series(rnorm(30), time = 1029:1000, calendar = BCE())) #> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.  ## Terminal and sampling times (returns rata die) start(X) #> [1] -376199 end(X) #> [1] -365607 time(X) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277 #> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990 #> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702 #> [28] -366337 -365972 -365607 span(X) #> [1] 10592  ## Multivariate ## Sampled every century starting from 1000 CE (Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE())) #> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.  ## Terminal and sampling times (returns Gregorian Common Era years) start(Y, calendar = CE()) #> [1] 1000 end(Y, calendar = CE()) #> [1] 1029 time(Y, calendar = CE()) #>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 span(Y, calendar = CE()) #> [1] 29.99726  ## Coerce to data frame df <- as.data.frame(Y, calendar = BP()) head(df) #>   time series variable      value #> 1  950     S1        A  0.2281259 #> 2  949     S1        A  1.6220241 #> 3  948     S1        A  1.0429135 #> 4  947     S1        A -0.9253764 #> 5  946     S1        A  0.2305605 #> 6  945     S1        A -1.5364367","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":null,"dir":"Reference","previous_headings":"","what":"Terminal Times of Time Series \u2014 start","title":"Terminal Times of Time Series \u2014 start","text":"Get times first last observations taken.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Terminal Times of Time Series \u2014 start","text":"","code":"# S4 method for TimeSeries start(x, calendar = NULL)  # S4 method for TimeSeries end(x, calendar = NULL)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Terminal Times of Time Series \u2014 start","text":"x TimeSeries object. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Terminal Times of Time Series \u2014 start","text":"numeric vector.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Terminal Times of Time Series \u2014 start","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/start.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Terminal Times of Time Series \u2014 start","text":"","code":"## Create time-series of 20 observations  ## Univariate ## Sampled every years starting from 1029 BCE (X <- series(rnorm(30), time = 1029:1000, calendar = BCE())) #> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.  ## Terminal and sampling times (returns rata die) start(X) #> [1] -376199 end(X) #> [1] -365607 time(X) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277 #> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990 #> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702 #> [28] -366337 -365972 -365607 span(X) #> [1] 10592  ## Multivariate ## Sampled every century starting from 1000 CE (Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE())) #> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.  ## Terminal and sampling times (returns Gregorian Common Era years) start(Y, calendar = CE()) #> [1] 1000 end(Y, calendar = CE()) #> [1] 1029 time(Y, calendar = CE()) #>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 span(Y, calendar = CE()) #> [1] 29.99726  ## Coerce to data frame df <- as.data.frame(Y, calendar = BP()) head(df) #>   time series variable       value #> 1  950     S1        A  0.97241451 #> 2  949     S1        A -0.70127022 #> 3  948     S1        A  0.06704523 #> 4  947     S1        A -0.67246065 #> 5  946     S1        A  0.90582968 #> 6  945     S1        A  1.30456740","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object \u2014 subset","title":"Extract or Replace Parts of an Object \u2014 subset","text":"Operators acting objects extract replace parts.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object \u2014 subset","text":"","code":"# S4 method for RataDie [(x, i)  # S4 method for TimeSeries [(x, i, j, k, drop = FALSE)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object \u2014 subset","text":"x object extract element(s) replace element(s). , j, k Indices specifying elements extract replace. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object \u2014 subset","text":"subsetted object.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object \u2014 subset","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":null,"dir":"Reference","previous_headings":"","what":"Sampling Times of Time Series \u2014 time","title":"Sampling Times of Time Series \u2014 time","text":"Get sampling times: time() creates vector times time series sampled. frequency() returns mean number samples per unit time.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sampling Times of Time Series \u2014 time","text":"","code":"# S4 method for TimeSeries time(x, calendar = NULL)  # S4 method for TimeSeries frequency(x)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sampling Times of Time Series \u2014 time","text":"x TimeSeries object. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sampling Times of Time Series \u2014 time","text":"numeric vector.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sampling Times of Time Series \u2014 time","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/time.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sampling Times of Time Series \u2014 time","text":"","code":"## Create time-series of 20 observations  ## Univariate ## Sampled every years starting from 1029 BCE (X <- series(rnorm(30), time = 1029:1000, calendar = BCE())) #> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.  ## Terminal and sampling times (returns rata die) start(X) #> [1] -376199 end(X) #> [1] -365607 time(X) #> Rata die: number of days since 01-01-01 (Gregorian). #>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277 #> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990 #> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702 #> [28] -366337 -365972 -365607 span(X) #> [1] 10592  ## Multivariate ## Sampled every century starting from 1000 CE (Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE())) #> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.  ## Terminal and sampling times (returns Gregorian Common Era years) start(Y, calendar = CE()) #> [1] 1000 end(Y, calendar = CE()) #> [1] 1029 time(Y, calendar = CE()) #>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 #> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 span(Y, calendar = CE()) #> [1] 29.99726  ## Coerce to data frame df <- as.data.frame(Y, calendar = BP()) head(df) #>   time series variable        value #> 1  950     S1        A -0.528586751 #> 2  949     S1        A -0.127997142 #> 3  948     S1        A -0.585114787 #> 4  947     S1        A  0.003627898 #> 5  946     S1        A -0.308915896 #> 6  945     S1        A -1.265319276","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":null,"dir":"Reference","previous_headings":"","what":"Time Series Windows \u2014 window","title":"Time Series Windows \u2014 window","text":"Extracts subset object x observed times start end (expressed rata die).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Time Series Windows \u2014 window","text":"","code":"# S4 method for TimeSeries window(x, start = NULL, end = NULL)","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Time Series Windows \u2014 window","text":"x TimeSeries object. start length-one numeric vector specifying start time period interest. end length-one numeric vector specifying end time period interest.","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Time Series Windows \u2014 window","text":"TimeSeries object.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Time Series Windows \u2014 window","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/window.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Time Series Windows \u2014 window","text":"","code":"## Create 3 time-series of 100 observations ## Sampled every years starting from 1000 CE (x <- series(matrix(rnorm(300), 100, 3), time = 1000:1099, calendar = CE())) #> 100 x 3 x 1 time series observed between 364878 and 401037 r.d.  ## Subset between 1025 and 1050 CE (y <- window(x, start = 374009, end = 383140)) #> 26 x 3 x 1 time series observed between 374009 and 383140 r.d.","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":null,"dir":"Reference","previous_headings":"","what":"Time Series Plotting Functions \u2014 year_axis","title":"Time Series Plotting Functions \u2014 year_axis","text":"Time Series Plotting Functions","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Time Series Plotting Functions \u2014 year_axis","text":"","code":"year_axis(   side,   at = NULL,   format = c(\"a\", \"ka\", \"Ma\", \"Ga\"),   labels = TRUE,   calendar = getOption(\"aion.last_calendar\"),   current_calendar = getOption(\"aion.last_calendar\"),   ... )","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Time Series Plotting Functions \u2014 year_axis","text":"side integer specifying side plot axis drawn . axis placed follows: 1=, 2=left, 3=4=right. numeric vector giving points tick-marks drawn. NULL, tickmark locations computed. format character string specifying prefix. one \"\", \"ka\", \"Ma\" \"Ga\". TRUE, good guess appropriate format made. labels logical scalar specifying whether annotations made tickmarks, vector character strings placed tickpoints. calendar TimeScale object specifying target calendar (see calendar()). current_calendar TimeScale object specifying calendar used last call plot(). ... parameters passed graphics::axis(). (e.g. graphical parameters).","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Time Series Plotting Functions \u2014 year_axis","text":"year_axis() called side-effects.","code":"","package":"aion"},{"path":[],"package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Time Series Plotting Functions \u2014 year_axis","text":"N. Frerebeau","code":"","package":"aion"},{"path":"https://packages.tesselle.org/aion/reference/year_axis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Time Series Plotting Functions \u2014 year_axis","text":"","code":"## Create a time-series of 300 observations ## Sampled every two years starting from 2000 BP X <- series(   object = rnorm(300),   time = seq(2000, by = -2, length.out = 300),   calendar = BP() )  ## Axis plot(X, axes = FALSE, calendar = BP()) # Remove axes year_axis(side = 1) # Same calendar as last plot year_axis(side = 3, calendar = CE()) # Specific calendar mtext(format(CE()), side = 3, line = 3)   ## Grid plot(X, panel.first = graphics::grid())","package":"aion"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Fabio Crameri's Color Schemes","text":"Crameri (2018) offers carefully chosen schemes, ready mapping data, colors : Perceptually uniform, Perceptually ordered, Color vision deficiency friendly, Readable black white print, Citable & reproducible. scales presented Crameri (2021) implemented , use base R graphics ggplot2.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"broc","dir":"Articles","previous_headings":"Diverging data","what":"broc","title":"Fabio Crameri's Color Schemes","text":"","code":"broc <- color(\"broc\") plot(broc(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"cork","dir":"Articles","previous_headings":"Diverging data","what":"cork","title":"Fabio Crameri's Color Schemes","text":"","code":"cork <- color(\"cork\") plot(cork(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"vik","dir":"Articles","previous_headings":"Diverging data","what":"vik","title":"Fabio Crameri's Color Schemes","text":"","code":"vik <- color(\"vik\") plot(vik(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"lisbon","dir":"Articles","previous_headings":"Diverging data","what":"lisbon","title":"Fabio Crameri's Color Schemes","text":"","code":"lisbon <- color(\"lisbon\") plot(lisbon(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"tofino","dir":"Articles","previous_headings":"Diverging data","what":"tofino","title":"Fabio Crameri's Color Schemes","text":"","code":"tofino <- color(\"tofino\") plot(tofino(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"berlin","dir":"Articles","previous_headings":"Diverging data","what":"berlin","title":"Fabio Crameri's Color Schemes","text":"","code":"berlin <- color(\"berlin\") plot(berlin(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"roma","dir":"Articles","previous_headings":"Diverging data","what":"roma","title":"Fabio Crameri's Color Schemes","text":"","code":"roma <- color(\"roma\") plot(roma(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"bam","dir":"Articles","previous_headings":"Diverging data","what":"bam","title":"Fabio Crameri's Color Schemes","text":"","code":"bam <- color(\"bam\") plot(bam(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"vanimo","dir":"Articles","previous_headings":"Diverging data","what":"vanimo","title":"Fabio Crameri's Color Schemes","text":"","code":"vanimo <- color(\"vanimo\") plot(vanimo(256))","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"batlow","dir":"Articles","previous_headings":"Sequential data","what":"batlow","title":"Fabio Crameri's Color Schemes","text":"","code":"batlow <- color(\"batlow\") plot(batlow(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"batloww","dir":"Articles","previous_headings":"Sequential data","what":"batlowW","title":"Fabio Crameri's Color Schemes","text":"","code":"batlowW <- color(\"batlowW\") plot(batlowW(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"batlowk","dir":"Articles","previous_headings":"Sequential data","what":"batlowK","title":"Fabio Crameri's Color Schemes","text":"","code":"batlowK <- color(\"batlowK\") plot(batlowK(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"devon","dir":"Articles","previous_headings":"Sequential data","what":"devon","title":"Fabio Crameri's Color Schemes","text":"","code":"devon <- color(\"devon\") plot(devon(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"lajolla","dir":"Articles","previous_headings":"Sequential data","what":"lajolla","title":"Fabio Crameri's Color Schemes","text":"","code":"lajolla <- color(\"lajolla\") plot(lajolla(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"bamako","dir":"Articles","previous_headings":"Sequential data","what":"bamako","title":"Fabio Crameri's Color Schemes","text":"","code":"bamako <- color(\"bamako\") plot(bamako(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"davos","dir":"Articles","previous_headings":"Sequential data","what":"davos","title":"Fabio Crameri's Color Schemes","text":"","code":"davos <- color(\"davos\") plot(davos(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"bilbao","dir":"Articles","previous_headings":"Sequential data","what":"bilbao","title":"Fabio Crameri's Color Schemes","text":"","code":"bilbao <- color(\"bilbao\") plot(bilbao(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"nuuk","dir":"Articles","previous_headings":"Sequential data","what":"nuuk","title":"Fabio Crameri's Color Schemes","text":"","code":"nuuk <- color(\"nuuk\") plot(nuuk(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"oslo","dir":"Articles","previous_headings":"Sequential data","what":"oslo","title":"Fabio Crameri's Color Schemes","text":"","code":"oslo <- color(\"oslo\") plot(oslo(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"grayc","dir":"Articles","previous_headings":"Sequential data","what":"grayC","title":"Fabio Crameri's Color Schemes","text":"","code":"grayC <- color(\"grayC\") plot(grayC(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"hawaii","dir":"Articles","previous_headings":"Sequential data","what":"hawaii","title":"Fabio Crameri's Color Schemes","text":"","code":"hawaii <- color(\"hawaii\") plot(hawaii(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"lapaz","dir":"Articles","previous_headings":"Sequential data","what":"lapaz","title":"Fabio Crameri's Color Schemes","text":"","code":"lapaz <- color(\"lapaz\") plot(lapaz(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"tokyo","dir":"Articles","previous_headings":"Sequential data","what":"tokyo","title":"Fabio Crameri's Color Schemes","text":"","code":"tokyo <- color(\"tokyo\") plot(tokyo(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"buda","dir":"Articles","previous_headings":"Sequential data","what":"buda","title":"Fabio Crameri's Color Schemes","text":"","code":"buda <- color(\"buda\") plot(buda(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"acton","dir":"Articles","previous_headings":"Sequential data","what":"acton","title":"Fabio Crameri's Color Schemes","text":"","code":"acton <- color(\"acton\") plot(acton(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"turku","dir":"Articles","previous_headings":"Sequential data","what":"turku","title":"Fabio Crameri's Color Schemes","text":"","code":"turku <- color(\"turku\") plot(turku(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"imola","dir":"Articles","previous_headings":"Sequential data","what":"imola","title":"Fabio Crameri's Color Schemes","text":"","code":"imola <- color(\"imola\") plot(imola(256))","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"oleron","dir":"Articles","previous_headings":"Multi-sequential data","what":"oleron","title":"Fabio Crameri's Color Schemes","text":"","code":"oleron <- color(\"oleron\") plot(oleron(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"bukavu","dir":"Articles","previous_headings":"Multi-sequential data","what":"bukavu","title":"Fabio Crameri's Color Schemes","text":"","code":"bukavu <- color(\"bukavu\") plot(bukavu(256))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/crameri.html","id":"fes","dir":"Articles","previous_headings":"Multi-sequential data","what":"fes","title":"Fabio Crameri's Color Schemes","text":"","code":"fes <- color(\"fes\") plot(fes(256))","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Paul Tol's Color Schemes","text":"Tol (2021) offers carefully chosen schemes, ready type data, colors : Distinct people, including color-blind readers, Distinct black white, Distinct screen paper, Matching well together. scales presented Paul Tol\u2019s technical note (issue 3.2, 2021-08-18) implemented , use base R graphics ggplot2.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"qualitative-data","dir":"Articles","previous_headings":"","what":"Qualitative data","title":"Paul Tol's Color Schemes","text":"According Paul Tol\u2019s technical note, bright, contrast, vibrant muted color schemes color-blind safe. light color scheme reasonably distinct normal color-blind vision intended fill labelled cells. pale dark schemes distinct either normal color-blind vision used text background highlight cell table. qualitative color schemes must used given (interpolation): colors picked maximum number supported values. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"bright","dir":"Articles","previous_headings":"Qualitative data","what":"bright","title":"Paul Tol's Color Schemes","text":"","code":"bright <- color(\"bright\") plot_scheme(bright(7), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"high-contrast","dir":"Articles","previous_headings":"Qualitative data","what":"high contrast","title":"Paul Tol's Color Schemes","text":"","code":"highcontrast <- color(\"high contrast\") plot_scheme(highcontrast(3), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"vibrant","dir":"Articles","previous_headings":"Qualitative data","what":"vibrant","title":"Paul Tol's Color Schemes","text":"","code":"vibrant <- color(\"vibrant\") plot_scheme(vibrant(7), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"muted","dir":"Articles","previous_headings":"Qualitative data","what":"muted","title":"Paul Tol's Color Schemes","text":"","code":"muted <- color(\"muted\") plot_scheme(muted(9), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"medium-contrast","dir":"Articles","previous_headings":"Qualitative data","what":"medium contrast","title":"Paul Tol's Color Schemes","text":"","code":"mediumcontrast <- color(\"medium contrast\") plot_scheme(mediumcontrast(6), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"pale-and-dark","dir":"Articles","previous_headings":"Qualitative data","what":"pale and dark","title":"Paul Tol's Color Schemes","text":"","code":"pale <- color(\"pale\") plot_scheme(pale(6), colours = TRUE, names = TRUE, size = 0.9) dark <- color(\"dark\") plot_scheme(dark(6), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"light","dir":"Articles","previous_headings":"Qualitative data","what":"light","title":"Paul Tol's Color Schemes","text":"","code":"light <- color(\"light\") plot_scheme(light(9), colours = TRUE, names = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"diverging-data","dir":"Articles","previous_headings":"","what":"Diverging data","title":"Paul Tol's Color Schemes","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"sunset","dir":"Articles","previous_headings":"Diverging data","what":"sunset","title":"Paul Tol's Color Schemes","text":"","code":"sunset <- color(\"sunset\") plot_scheme(sunset(9), colours = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"burd","dir":"Articles","previous_headings":"Diverging data","what":"BuRd","title":"Paul Tol's Color Schemes","text":"","code":"BuRd <- color(\"BuRd\") plot_scheme(BuRd(9), colours = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"prgn","dir":"Articles","previous_headings":"Diverging data","what":"PRGn","title":"Paul Tol's Color Schemes","text":"","code":"PRGn <- color(\"PRGn\") plot_scheme(PRGn(9), colours = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"sequential-data","dir":"Articles","previous_headings":"","what":"Sequential data","title":"Paul Tol's Color Schemes","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"ylorbr","dir":"Articles","previous_headings":"Sequential data","what":"YlOrBr","title":"Paul Tol's Color Schemes","text":"","code":"YlOrBr <- color(\"YlOrBr\") plot_scheme(YlOrBr(9), colours = TRUE, size = 0.9)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"iridescent","dir":"Articles","previous_headings":"Sequential data","what":"iridescent","title":"Paul Tol's Color Schemes","text":"","code":"iridescent <- color(\"iridescent\") plot_scheme(iridescent(23), colours = TRUE, size = 0.5)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"rainbow","dir":"Articles","previous_headings":"Sequential data","what":"rainbow","title":"Paul Tol's Color Schemes","text":"general rule, ordered data represented using rainbow scheme. three main arguments use (Tol 2021): spectral order visible light carries inherent magnitude message. bands almost constant hue sharp transitions , can perceived jumps data. Color-blind people difficulty distinguishing colors rainbow. use avoided, Paul Tol\u2019s technical note provides two color schemes reasonably clear color-blind vision. remain color-blind safe, two schemes must comply following conditions: discrete rainbow scheme must interpolated. smooth rainbow scheme used full range (Tol 2021 suggests starting purple).  using smooth rainbow scheme: Start -white instead purple lowest data value occurs often; End red instead brown highest data value occurs often.","code":"discrete_rainbow <- color(\"discrete rainbow\") plot_scheme(discrete_rainbow(14), colours = TRUE, size = 0.7) smooth_rainbow <- color(\"smooth rainbow\")  ## Start at purple instead of off-white plot(smooth_rainbow(256, range = c(0.25, 1))) ## End at red instead of brown plot(smooth_rainbow(256, range = c(0, 0.9)))","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"qualitative-color-schemes","dir":"Articles","previous_headings":"Diagnostic maps","what":"Qualitative color schemes","title":"Paul Tol's Color Schemes","text":"Diagnostic maps bright, vibrant, muted light (top bottom) qualitative color schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"diverging-schemes","dir":"Articles","previous_headings":"Diagnostic maps","what":"Diverging schemes","title":"Paul Tol's Color Schemes","text":"Diagnostic maps sunset, BuRd PRGn (top bottom) diverging color schemes. Diagnostic maps sunset, BuRd PRGn diverging color schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/articles/tol.html","id":"sequential-schemes","dir":"Articles","previous_headings":"Diagnostic maps","what":"Sequential schemes","title":"Paul Tol's Color Schemes","text":"Diagnostic maps YlOrBr, iridescent, discrete rainbow smooth rainbow (top bottom) sequential color schemes. Diagnostic maps YlOrBr, iridescent smooth rainbow sequential color schemes.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Colour Schemes for Scientific Data Visualization","text":"Color blindness affects large number individuals. communicating scientific results color palettes must therefore carefully chosen accessible readers. R package provides implementation Okabe Ito (2008), Tol (2021) Crameri (2018) color schemes. schemes ready type data (qualitative, diverging sequential), colors distinct people, including color-blind readers. package also provides tools simulate color-blindness test well colors palette identifiable. simulate color-blindness production-ready R figures may also interested colorblindr package. Tol (2021) Crameri (2018) offer carefully chosen schemes, ready type data, colors : Distinct people, including color-blind readers, Distinct black white, Distinct screen paper, Matching well together, Citable reproducible. See vignette(\"tol\") vignette(\"crameri\") complete overview. specific uses, several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) implemented, color schemes may color-blind safe. color schemes implemented use base R graphics ggplot2 ggraph.","code":"To cite khroma in publications use:    Frerebeau N (2023). _khroma: Colour Schemes for Scientific Data   Visualization_. Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.1472077 <https://doi.org/10.5281/zenodo.1472077>,   R package version 1.11.0, <https://packages.tesselle.org/khroma/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{khroma: Colour Schemes for Scientific Data Visualization}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.11.0},     doi = {10.5281/zenodo.1472077},     url = {https://packages.tesselle.org/khroma/},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Colour Schemes for Scientific Data Visualization","text":"can install released version khroma CRAN: development version GitHub :","code":"install.packages(\"khroma\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/khroma\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Colour Schemes for Scientific Data Visualization","text":"","code":"## Install extra packages (if needed) # install.packages(\"ggplot2\"))  ## Load packages library(khroma) ## Get a table of available palettes info() #>            palette        type max missing #> 1             broc   diverging 256    <NA> #> 2             cork   diverging 256    <NA> #> 3              vik   diverging 256    <NA> #> 4           lisbon   diverging 256    <NA> #> 5           tofino   diverging 256    <NA> #> 6           berlin   diverging 256    <NA> #> 7             roma   diverging 256    <NA> #> 8              bam   diverging 256    <NA> #> 9           vanimo   diverging 256    <NA> #> 10          oleron   diverging 256    <NA> #> 11          bukavu   diverging 256    <NA> #> 12             fes   diverging 256    <NA> #> 13           devon  sequential 256    <NA> #> 14         lajolla  sequential 256    <NA> #> 15          bamako  sequential 256    <NA> #> 16           davos  sequential 256    <NA> #> 17          bilbao  sequential 256    <NA> #> 18            nuuk  sequential 256    <NA> #> 19            oslo  sequential 256    <NA> #> 20           grayC  sequential 256    <NA> #> 21          hawaii  sequential 256    <NA> #> 22           lapaz  sequential 256    <NA> #> 23           tokyo  sequential 256    <NA> #> 24            buda  sequential 256    <NA> #> 25           acton  sequential 256    <NA> #> 26           turku  sequential 256    <NA> #> 27           imola  sequential 256    <NA> #> 28          batlow  sequential 256    <NA> #> 29         batlowW  sequential 256    <NA> #> 30         batlowK  sequential 256    <NA> #> 31           brocO  sequential 256    <NA> #> 32           corkO  sequential 256    <NA> #> 33            vikO  sequential 256    <NA> #> 34           romaO  sequential 256    <NA> #> 35            bamO  sequential 256    <NA> #> 36          bright qualitative   7    <NA> #> 37    highcontrast qualitative   3    <NA> #> 38         vibrant qualitative   7    <NA> #> 39           muted qualitative   9 #DDDDDD #> 40  mediumcontrast qualitative   6    <NA> #> 41            pale qualitative   6    <NA> #> 42            dark qualitative   6    <NA> #> 43           light qualitative   9    <NA> #> 44          sunset   diverging  11 #FFFFFF #> 45            BuRd   diverging   9 #FFEE99 #> 46            PRGn   diverging   9 #FFEE99 #> 47          YlOrBr  sequential   9 #888888 #> 48      iridescent  sequential  23 #999999 #> 49 discreterainbow  sequential  23 #777777 #> 50   smoothrainbow  sequential  34 #666666 #> 51        okabeito qualitative   8    <NA> #> 52   okabeitoblack qualitative   8    <NA> #> 53    stratigraphy qualitative 175    <NA> #> 54            soil qualitative  24    <NA> #> 55            land qualitative  14    <NA>","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"color-palettes-and-scales","dir":"","previous_headings":"Usage","what":"Color palettes and scales","title":"Colour Schemes for Scientific Data Visualization","text":"color() returns palette function called single integer argument returns vector colors.","code":"## Paul Tol's bright color scheme bright <- color(\"bright\") bright(7) #>      blue       red     green    yellow      cyan    purple      grey  #> \"#4477AA\" \"#EE6677\" \"#228833\" \"#CCBB44\" \"#66CCEE\" \"#AA3377\" \"#BBBBBB\"  #> attr(,\"missing\") #> [1] NA ## Show the color palette plot_scheme(bright(7), colours = TRUE) data(mpg, package = \"ggplot2\")  ## Use with graphics par(mar = c(5, 4, 1, 1) + 0.1) plot(   x = mpg$displ,   y = mpg$hwy,   pch = 16,   col = color(\"bright\")(7)[as.factor(mpg$class)],   xlab = \"displ\",   ylab = \"hwy\",   panel.first = grid() ) ## Use with ggplot2 ggplot2::ggplot(data = mpg) +   ggplot2::aes(x = displ, y = hwy, color = class) +   ggplot2::geom_point() +   ggplot2::theme_bw() +   scale_color_bright()","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"test-how-well-the-colors-are-identifiable","dir":"","previous_headings":"Usage > Diagnostic tools","what":"Test how well the colors are identifiable","title":"Colour Schemes for Scientific Data Visualization","text":"","code":"## Okabe & Ito's color scheme okabe <- color(\"okabe ito\")  set.seed(12345) plot_map(okabe(8)) ## BuRd sequential color scheme BuRd <- color(\"BuRd\")  plot_tiles(BuRd(128), n = 256)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"simulate-color-blindness","dir":"","previous_headings":"Usage > Diagnostic tools","what":"Simulate color-blindness","title":"Colour Schemes for Scientific Data Visualization","text":"","code":"plot_scheme_colorblind(okabe(8)) ## ggplot2 default color scheme ## (equally spaced hues around the color wheel) x <- scales::hue_pal()(8) plot_scheme_colorblind(x)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Colour Schemes for Scientific Data Visualization","text":"Please note khroma project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":[],"package":"khroma"},{"path":[],"package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":null,"dir":"Reference","previous_headings":"","what":"Color Palette \u2014 colour","title":"Color Palette \u2014 colour","text":"Provides qualitative, diverging sequential color schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color Palette \u2014 colour","text":"","code":"colour(palette, reverse = FALSE, names = TRUE, lang = \"en\", force = FALSE, ...)  color(palette, reverse = FALSE, names = TRUE, lang = \"en\", force = FALSE, ...)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color Palette \u2014 colour","text":"palette character string giving name palette used (see ). reverse logical scalar: resulting vector colors reversed? names logical scalar: names colors kept resulting vector? lang character string specifying language color names. must one \"en\" (English, default) \"fr\" (French). force logical scalar. TRUE, forces color scheme interpolated. used routinely qualitative color schemes, designed used remain color-blind safe. ... arguments passed colorRampPalette.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Color Palette \u2014 colour","text":"palette function following attributes, called single integer argument (number levels) returns (named) vector colors. palette character string giving name color scheme. type character string giving corresponding data type. One \"qualitative\", \"diverging\" \"sequential\". interpolate logical scalar: can color palette interpolated? missing character string giving hexadecimal representation color used NA values. max integer giving maximum number color values. relevant non-interpolated color schemes. color schemes can interpolated (diverging sequential data), color range can limited additional argument. range allows remove fraction color domain (interpolated; see examples).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"paul-tol-s-color-schemes","dir":"Reference","previous_headings":"","what":"Paul Tol's Color Schemes","title":"Color Palette \u2014 colour","text":"following palettes available. maximum number supported colors brackets, value relevant qualitative color schemes (divergent sequential schemes linearly interpolated). Qualitative data bright (7), high contrast (3), vibrant (7), muted (9), medium contrast (6), pale (6), dark (6), light (9). Diverging data sunset (11), BuRd (9), PRGn (9). Sequential data YlOrBr (9), iridescent (23), discrete rainbow (23), smooth rainbow (34).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Color Palette \u2014 colour","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"rainbow-color-scheme","dir":"Reference","previous_headings":"","what":"Rainbow Color Scheme","title":"Color Palette \u2014 colour","text":"general rule, ordered data represented using rainbow scheme. three main arguments use (Tol 2018): spectral order visible light carries inherent magnitude message. bands almost constant hue sharp transitions , can perceived jumps data. Color-blind people difficulty distinguishing colors rainbow. use avoided, Paul Tol's technical note provides two color schemes reasonably clear color-blind vision. remain color-blind safe, two schemes must comply following conditions: discreterainbow scheme must interpolated. smoothrainbow scheme used full range.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"okabe-and-ito-color-scheme","dir":"Reference","previous_headings":"","what":"Okabe and Ito Color Scheme","title":"Color Palette \u2014 colour","text":"following (qualitative) color scheme available: okabeito 8 colors. okabeito black 8 colors, black last.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"scientific-color-schemes","dir":"Reference","previous_headings":"","what":"Scientific Color Schemes","title":"Color Palette \u2014 colour","text":"following (qualitative) color schemes available: stratigraphy International Chronostratigraphic Chart (175 colors). land AVHRR Global Land Cover Classification (14 colors). soil FAO Reference Soil Groups (24 colors).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Color Palette \u2014 colour","text":"Jones, ., Montanarella, L. & Jones, R. (Ed.) (2005). Soil atlas Europe. Luxembourg: European Commission, Office Official Publications European Communities. 128 pp. ISBN: 92-894-8120-X. Okabe, M. & Ito, K. (2008). Color Universal Design (CUD): Make Figures Presentations Friendly Colorblind People. URL: https://jfly.uni-koeln.de/color/. Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf Commission Geological Map World","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Color Palette \u2014 colour","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/colour.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color Palette \u2014 colour","text":"","code":"## Okabe and Ito colour scheme colour(\"okabe ito\")(8) #>          black         orange       sky blue   bluish green         yellow  #>      \"#000000\"      \"#E69F00\"      \"#56B4E9\"      \"#009E73\"      \"#F0E442\"  #>           blue      vermilion reddish purple  #>      \"#0072B2\"      \"#D55E00\"      \"#CC79A7\"  #> attr(,\"missing\") #> [1] NA plot_scheme(colour(\"okabe ito\")(8))   ## Paul Tol's colour schemes ### Qualitative data plot_scheme(colour(\"bright\")(7))  plot_scheme(colour(\"high contrast\")(3))  plot_scheme(colour(\"vibrant\")(7))  plot_scheme(colour(\"muted\")(9))  plot_scheme(colour(\"medium contrast\")(6))  plot_scheme(colour(\"pale\")(6))  plot_scheme(colour(\"dark\")(6))  plot_scheme(colour(\"light\")(9))  ### Diverging data plot_scheme(colour(\"sunset\")(11))  plot_scheme(colour(\"BuRd\")(9))  plot_scheme(colour(\"PRGn\")(9))  ### Sequential data plot_scheme(colour(\"YlOrBr\")(9))  plot_scheme(colour(\"iridescent\")(23))  plot_scheme(colour(\"discrete rainbow\")(14))  plot_scheme(colour(\"discrete rainbow\")(23))  plot_scheme(colour(\"smooth rainbow\")(34))   ## Scientific colour schemes ### Geologic timescale plot_scheme(colour(\"stratigraphy\")(175))  ### AVHRR global land cover classification plot_scheme(colour(\"land\")(14))  ### FAO soil reference groups plot_scheme(colour(\"soil\")(24))   ## Adjust colour levels PRGn <- colour(\"PRGn\") plot_scheme(PRGn(9, range = c(0.5, 1)))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":null,"dir":"Reference","previous_headings":"","what":"Color Difference \u2014 compare","title":"Color Difference \u2014 compare","text":"Computes CIELAB distance metric.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color Difference \u2014 compare","text":"","code":"compare(x, metric = 2000, diag = FALSE, upper = FALSE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color Difference \u2014 compare","text":"x character vector colors. metric integer value giving year metric recommended CIE. must one \"1976\", \"1994\", \"2000\" (default; see spacesXYZ::DeltaE()). diag logical scalar: diagonal distance matrix printed? upper logical scalar: upper triangle distance matrix printed?","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Color Difference \u2014 compare","text":"distance matrix.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Color Difference \u2014 compare","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/compare.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color Difference \u2014 compare","text":"","code":"# Trichromat pal <- colour(\"bright\")  (deltaE <- compare(pal(5))) #>            blue      red    green   yellow #> red    42.96901                            #> green  45.17203 68.91205                   #> yellow 58.32662 49.74802 31.70794          #> cyan   27.21922 60.08809 45.20276 45.65689 summary(deltaE) #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>   27.22   43.52   45.43   47.50   56.18   68.91   # Deuteranopia deu <- convert(pal, mode = \"deuteranopia\") compare(deu(5)) #>            blue      red    green   yellow #> red    48.65131                            #> green  47.54984 18.09392                   #> yellow 62.21275 13.46961 26.56583          #> cyan   24.04176 43.39140 51.12708 54.18900  # Protanopia pro <- convert(pal, mode = \"protanopia\") compare(pro(5)) #>            blue      red    green   yellow #> red    33.88826                            #> green  48.47198 14.79836                   #> yellow 58.79437 24.03187 21.17733          #> cyan   24.67298 34.21991 49.39259 49.55829  # Tritanopia tri <- convert(pal, mode = \"tritanopia\") compare(tri(5)) #>             blue       red     green    yellow #> red    50.727249                               #> green   1.346637 49.444911                     #> yellow 43.626450 20.642711 42.597786           #> cyan   24.817186 56.361997 23.961375 38.777287  # Achromatopsia ach <- convert(pal, mode = \"achromatopsia\") compare(ach(5)) #>             blue       red     green    yellow #> red    15.133194                               #> green  38.752027 19.522094                     #> yellow 26.072281  9.633077 10.043244           #> cyan   17.098221 31.695821 56.285350 41.893242","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate Color-Blindness \u2014 convert","title":"Simulate Color-Blindness \u2014 convert","text":"Simulate Color-Blindness","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate Color-Blindness \u2014 convert","text":"","code":"convert(x, mode)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate Color-Blindness \u2014 convert","text":"x palette function called single integer argument (number levels) returns vector colors (see color()). mode character string giving colorblind vision used. must one \"deuteranopia\", \"protanopia\", \"tritanopia\" \"achromatopsia\". unambiguous substring can given.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate Color-Blindness \u2014 convert","text":"palette function returns vector anomalized colors. attributes initial palette function inherited, supplementary attribute \"mode\" giving corresponding color-blind vision.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulate Color-Blindness \u2014 convert","text":"Brettel, H., Vi\u00e9not, F. Mollon, J. D. (1997). Computerized Simulation Color Appearance Dichromats. Journal Optical Society America , 14(10), p. 2647-2655. doi:10.1364/JOSAA.14.002647 . Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf Vi\u00e9not, F., Brettel, H. Mollon, J. D. (1999). Digital Video Colourmaps Checking Legibility Displays Dichromats. Color Research & Application, 24(4), p. 243-52. doi:10.1002/(SICI)1520-6378(199908)24:4<243::AID-COL5>3.0.CO;2-3 .","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Simulate Color-Blindness \u2014 convert","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/convert.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate Color-Blindness \u2014 convert","text":"","code":"# Trichromat pal <- colour(\"bright\") plot_scheme(pal(7))   # Deuteranopia deu <- convert(pal, mode = \"deuteranopia\") plot_scheme(deu(7))   # Protanopia pro <- convert(pal, mode = \"protanopia\") plot_scheme(pro(7))   # Tritanopia tri <- convert(pal, mode = \"tritanopia\") plot_scheme(tri(7))   # Achromatopsia ach <- convert(pal, mode = \"achromatopsia\") plot_scheme(ach(7))   ## Plot simulated color blindness plot_scheme_colorblind(pal(7))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/info.html","id":null,"dir":"Reference","previous_headings":"","what":"Available Palettes \u2014 info","title":"Available Palettes \u2014 info","text":"Returns information available palettes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Available Palettes \u2014 info","text":"","code":"info()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Available Palettes \u2014 info","text":"data.frame following columns: palette Names palette. type Types palettes: sequential, diverging qualitative. max Maximum number colors contained palette. relevant qualitative palettes. missing hexadecimal color value mapping missing values.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/info.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Available Palettes \u2014 info","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/info.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Available Palettes \u2014 info","text":"","code":"## Get a table of available palettes info() #>            palette        type max missing #> 1             broc   diverging 256    <NA> #> 2             cork   diverging 256    <NA> #> 3              vik   diverging 256    <NA> #> 4           lisbon   diverging 256    <NA> #> 5           tofino   diverging 256    <NA> #> 6           berlin   diverging 256    <NA> #> 7             roma   diverging 256    <NA> #> 8              bam   diverging 256    <NA> #> 9           vanimo   diverging 256    <NA> #> 10          oleron   diverging 256    <NA> #> 11          bukavu   diverging 256    <NA> #> 12             fes   diverging 256    <NA> #> 13           devon  sequential 256    <NA> #> 14         lajolla  sequential 256    <NA> #> 15          bamako  sequential 256    <NA> #> 16           davos  sequential 256    <NA> #> 17          bilbao  sequential 256    <NA> #> 18            nuuk  sequential 256    <NA> #> 19            oslo  sequential 256    <NA> #> 20           grayC  sequential 256    <NA> #> 21          hawaii  sequential 256    <NA> #> 22           lapaz  sequential 256    <NA> #> 23           tokyo  sequential 256    <NA> #> 24            buda  sequential 256    <NA> #> 25           acton  sequential 256    <NA> #> 26           turku  sequential 256    <NA> #> 27           imola  sequential 256    <NA> #> 28          batlow  sequential 256    <NA> #> 29         batlowW  sequential 256    <NA> #> 30         batlowK  sequential 256    <NA> #> 31           brocO  sequential 256    <NA> #> 32           corkO  sequential 256    <NA> #> 33            vikO  sequential 256    <NA> #> 34           romaO  sequential 256    <NA> #> 35            bamO  sequential 256    <NA> #> 36          bright qualitative   7    <NA> #> 37    highcontrast qualitative   3    <NA> #> 38         vibrant qualitative   7    <NA> #> 39           muted qualitative   9 #DDDDDD #> 40  mediumcontrast qualitative   6    <NA> #> 41            pale qualitative   6    <NA> #> 42            dark qualitative   6    <NA> #> 43           light qualitative   9    <NA> #> 44          sunset   diverging  11 #FFFFFF #> 45            BuRd   diverging   9 #FFEE99 #> 46            PRGn   diverging   9 #FFEE99 #> 47          YlOrBr  sequential   9 #888888 #> 48      iridescent  sequential  23 #999999 #> 49 discreterainbow  sequential  23 #777777 #> 50   smoothrainbow  sequential  34 #666666 #> 51        okabeito qualitative   8    <NA> #> 52   okabeitoblack qualitative   8    <NA> #> 53    stratigraphy qualitative 175    <NA> #> 54            soil qualitative  24    <NA> #> 55            land qualitative  14    <NA>","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/khroma-package.html","id":null,"dir":"Reference","previous_headings":"","what":"khroma: Colour Schemes for Scientific Data Visualization \u2014 khroma-package","title":"khroma: Colour Schemes for Scientific Data Visualization \u2014 khroma-package","text":"Color schemes ready type data (qualitative, diverging sequential), colors distinct people, including color-blind readers. package provides implementation Paul Tol (2018) Fabio Crameri (2018) doi:10.5194/gmd-11-2541-2018  color schemes use 'graphics' 'ggplot2'. provides tools simulate color-blindness test well colors palette identifiable. Several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) also implemented.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/khroma-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"khroma: Colour Schemes for Scientific Data Visualization \u2014 khroma-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot.color_scheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Color Scheme \u2014 plot.color_scheme","title":"Plot Color Scheme \u2014 plot.color_scheme","text":"Quickly displays color scheme returned color().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot.color_scheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Color Scheme \u2014 plot.color_scheme","text":"","code":"# S3 method for color_scheme plot(x, ...)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot.color_scheme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Color Scheme \u2014 plot.color_scheme","text":"x character vector colors. ... Currently used.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot.color_scheme.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Color Scheme \u2014 plot.color_scheme","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot.color_scheme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Color Scheme \u2014 plot.color_scheme","text":"","code":"plot(colour(\"bright\")(7))  plot(colour(\"smooth rainbow\")(256))   ## Plot colour schemes plot_scheme(colour(\"bright\")(7))  plot_scheme(colour(\"sunset\")(11))  plot_scheme(colour(\"YlOrBr\")(9))  plot_scheme(colour(\"discrete rainbow\")(14))   ## Plot diagnostic maps plot_map(colour(\"bright\")(7))  plot_map(colour(\"sunset\")(11))  plot_map(colour(\"YlOrBr\")(9))  plot_map(colour(\"discrete rainbow\")(14))   ## Plot diagnostic images plot_tiles(colour(\"discrete rainbow\")(14), n = 256)  plot_tiles(colour(\"discrete rainbow\")(23), n = 256)  plot_tiles(colour(\"smooth rainbow\")(256), n = 256)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic Map \u2014 plot_map","title":"Diagnostic Map \u2014 plot_map","text":"Produces diagnostic map given color scheme.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic Map \u2014 plot_map","text":"","code":"plot_map(x)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic Map \u2014 plot_map","text":"x character vector colors.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_map.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diagnostic Map \u2014 plot_map","text":"N. Frerebeau, V. Arel-Bundock","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic Map \u2014 plot_map","text":"","code":"plot(colour(\"bright\")(7))  plot(colour(\"smooth rainbow\")(256))   ## Plot colour schemes plot_scheme(colour(\"bright\")(7))  plot_scheme(colour(\"sunset\")(11))  plot_scheme(colour(\"YlOrBr\")(9))  plot_scheme(colour(\"discrete rainbow\")(14))   ## Plot diagnostic maps plot_map(colour(\"bright\")(7))  plot_map(colour(\"sunset\")(11))  plot_map(colour(\"YlOrBr\")(9))  plot_map(colour(\"discrete rainbow\")(14))   ## Plot diagnostic images plot_tiles(colour(\"discrete rainbow\")(14), n = 256)  plot_tiles(colour(\"discrete rainbow\")(23), n = 256)  plot_tiles(colour(\"smooth rainbow\")(256), n = 256)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Color Scheme \u2014 plot_scheme","title":"Plot Color Scheme \u2014 plot_scheme","text":"Shows colors plot.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Color Scheme \u2014 plot_scheme","text":"","code":"plot_scheme(x, colours = FALSE, names = FALSE, size = 1)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Color Scheme \u2014 plot_scheme","text":"x character vector colors. colours logical scalar: hexadecimal representation colors displayed? names logical scalar: name colors displayed? size numeric value giving amount plotting text magnified relative default. Works cex parameter graphics::par().","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Color Scheme \u2014 plot_scheme","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Color Scheme \u2014 plot_scheme","text":"","code":"plot(colour(\"bright\")(7))  plot(colour(\"smooth rainbow\")(256))   ## Plot colour schemes plot_scheme(colour(\"bright\")(7))  plot_scheme(colour(\"sunset\")(11))  plot_scheme(colour(\"YlOrBr\")(9))  plot_scheme(colour(\"discrete rainbow\")(14))   ## Plot diagnostic maps plot_map(colour(\"bright\")(7))  plot_map(colour(\"sunset\")(11))  plot_map(colour(\"YlOrBr\")(9))  plot_map(colour(\"discrete rainbow\")(14))   ## Plot diagnostic images plot_tiles(colour(\"discrete rainbow\")(14), n = 256)  plot_tiles(colour(\"discrete rainbow\")(23), n = 256)  plot_tiles(colour(\"smooth rainbow\")(256), n = 256)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme_colourblind.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","title":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","text":"Shows colors plot different types simulated color blindness.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme_colourblind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","text":"","code":"plot_scheme_colourblind(x)  plot_scheme_colorblind(x)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme_colourblind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","text":"x character vector colors.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme_colourblind.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","text":"N. Frerebeau, V. Arel-Bundock","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_scheme_colourblind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Simulated Color Blindness \u2014 plot_scheme_colourblind","text":"","code":"# Trichromat pal <- colour(\"bright\") plot_scheme(pal(7))   # Deuteranopia deu <- convert(pal, mode = \"deuteranopia\") plot_scheme(deu(7))   # Protanopia pro <- convert(pal, mode = \"protanopia\") plot_scheme(pro(7))   # Tritanopia tri <- convert(pal, mode = \"tritanopia\") plot_scheme(tri(7))   # Achromatopsia ach <- convert(pal, mode = \"achromatopsia\") plot_scheme(ach(7))   ## Plot simulated color blindness plot_scheme_colorblind(pal(7))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_tiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic Map \u2014 plot_tiles","title":"Diagnostic Map \u2014 plot_tiles","text":"Produces diagnostic map given color scheme.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_tiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic Map \u2014 plot_tiles","text":"","code":"plot_tiles(x, n = 512)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_tiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic Map \u2014 plot_tiles","text":"x character vector colors. n integer specifying size grid (defaults \\(512\\)).","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_tiles.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diagnostic Map \u2014 plot_tiles","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/plot_tiles.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic Map \u2014 plot_tiles","text":"","code":"plot(colour(\"bright\")(7))  plot(colour(\"smooth rainbow\")(256))   ## Plot colour schemes plot_scheme(colour(\"bright\")(7))  plot_scheme(colour(\"sunset\")(11))  plot_scheme(colour(\"YlOrBr\")(9))  plot_scheme(colour(\"discrete rainbow\")(14))   ## Plot diagnostic maps plot_map(colour(\"bright\")(7))  plot_map(colour(\"sunset\")(11))  plot_map(colour(\"YlOrBr\")(9))  plot_map(colour(\"discrete rainbow\")(14))   ## Plot diagnostic images plot_tiles(colour(\"discrete rainbow\")(14), n = 256)  plot_tiles(colour(\"discrete rainbow\")(23), n = 256)  plot_tiles(colour(\"smooth rainbow\")(256), n = 256)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":null,"dir":"Reference","previous_headings":"","what":"Color Ramp \u2014 ramp","title":"Color Ramp \u2014 ramp","text":"Provides color scheme map numeric vector colors.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color Ramp \u2014 ramp","text":"","code":"ramp(palette, ...)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color Ramp \u2014 ramp","text":"palette character string giving name palette used (see color()). ... arguments passed color().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Color Ramp \u2014 ramp","text":"palette function argument vector values mapped colors. data midpoint can specified additional midpoint argument (see examples).","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Color Ramp \u2014 ramp","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/ramp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color Ramp \u2014 ramp","text":"","code":"## Visualize a simple DEM model ## Distribution of elevation values elevation <- hist(volcano)   ## Where are breaks? elevation$breaks #>  [1]  90 100 110 120 130 140 150 160 170 180 190 200  ## Build palette function ramp_BuRd <- ramp(palette = \"BuRd\")  (col <- ramp_BuRd(elevation$breaks)) #>  [1] \"#2166AC\" \"#3986BC\" \"#66A9CF\" \"#9CCAE1\" \"#CAE1EE\" \"#E8F0F4\" \"#F9ECE4\" #>  [8] \"#FCD5C0\" \"#F5AE8D\" \"#E37E64\" \"#CC4C43\" \"#B2182B\" #> attr(,\"breaks\") #>  [1]  90 100 110 120 130 140 150 160 170 180 190 200 image(volcano, col = col) legend(\"topright\", legend = attr(col, \"breaks\"), fill = col)   ## Rescale to midpoint (col <- ramp_BuRd(elevation$breaks, midpoint = 160)) #>  [1] \"#2166AC\" \"#347FB9\" \"#4D99C6\" \"#7AB6D5\" \"#A3CDE2\" \"#C7DFED\" \"#E0ECF2\" #>  [8] \"#F7F7F7\" \"#FAE6DA\" \"#FBD2BC\" \"#F6B495\" \"#EB9072\" #> attr(,\"breaks\") #>  [1]  90 100 110 120 130 140 150 160 170 180 190 200 image(volcano, col = col) legend(\"topright\", legend = attr(col, \"breaks\"), fill = col)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"Fabio Crameri's acton Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"","code":"scale_colour_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_acton(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"Fabio Crameri's bam Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"","code":"scale_colour_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_bam(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"Fabio Crameri's bamO Cyclic Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"","code":"scale_colour_bamO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_bamO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_bamO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"Fabio Crameri's bamako Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"","code":"scale_colour_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_bamako(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"Fabio Crameri's batlow Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"","code":"scale_colour_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_batlow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"Fabio Crameri's batlowK Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"","code":"scale_colour_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_batlowK(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"Fabio Crameri's batlowW Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"","code":"scale_colour_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_batlowW(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"Fabio Crameri's berlin Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"","code":"scale_colour_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_berlin(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"Fabio Crameri's bilbao Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"","code":"scale_colour_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_bilbao(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"Fabio Crameri's broc Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"","code":"scale_colour_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_broc(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"Fabio Crameri's brocO Cyclic Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"","code":"scale_colour_brocO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_brocO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_brocO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"Fabio Crameri's buda Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"","code":"scale_colour_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_buda(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"Fabio Crameri's bukavu Multi-Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"","code":"scale_colour_bukavu(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_color_bukavu(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_fill_bukavu(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"","code":"data(volcano)  volcan <- data.frame(   x = rep(1:ncol(volcano), each = nrow(volcano)),   y = rep(1:nrow(volcano), times = ncol(volcano)),   z = as.numeric(volcano) )  ggplot2::ggplot(volcan, ggplot2::aes(x, y, fill = z)) +   ggplot2::geom_raster() +   scale_fill_oleron(midpoint = 125)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"Fabio Crameri's cork Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"","code":"scale_colour_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_cork(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"Fabio Crameri's corkO Cyclic Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"","code":"scale_colour_corkO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_corkO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_corkO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"Provides cyclic color scales Fabio Crameri's Scientific colour.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works . discrete logical scalar: color scheme used discrete scale?","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"Available schemes: brocO corkO vikO romaO bamO","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"Fabio Crameri's davos Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"","code":"scale_colour_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_davos(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"Fabio Crameri's devon Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"","code":"scale_colour_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_devon(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"Provides diverging color scales Fabio Crameri's Scientific colour.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works . discrete logical scalar: color scheme used discrete scale?","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"Available schemes: broc cork vik lisbon tofino berlin roma bam vanimo","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"Fabio Crameri's fes Multi-Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"","code":"scale_colour_fes(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_color_fes(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_fill_fes(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"","code":"data(volcano)  volcan <- data.frame(   x = rep(1:ncol(volcano), each = nrow(volcano)),   y = rep(1:nrow(volcano), times = ncol(volcano)),   z = as.numeric(volcano) )  ggplot2::ggplot(volcan, ggplot2::aes(x, y, fill = z)) +   ggplot2::geom_raster() +   scale_fill_oleron(midpoint = 125)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"Fabio Crameri's grayC Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"","code":"scale_colour_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_grayC(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"Fabio Crameri's hawaii Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"","code":"scale_colour_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_hawaii(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"Fabio Crameri's imola Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"","code":"scale_colour_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_imola(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"Fabio Crameri's lajolla Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"","code":"scale_colour_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_lajolla(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"Fabio Crameri's lapaz Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"","code":"scale_colour_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_lapaz(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"Fabio Crameri's lisbon Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"","code":"scale_colour_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_lisbon(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"Provides multi-sequential color scales Fabio Crameri's Scientific colour.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"Available schemes: oleron bukavu fes","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"","code":"data(volcano)  volcan <- data.frame(   x = rep(1:ncol(volcano), each = nrow(volcano)),   y = rep(1:nrow(volcano), times = ncol(volcano)),   z = as.numeric(volcano) )  ggplot2::ggplot(volcan, ggplot2::aes(x, y, fill = z)) +   ggplot2::geom_raster() +   scale_fill_oleron(midpoint = 125)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"Fabio Crameri's nuuk Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"","code":"scale_colour_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_nuuk(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"Fabio Crameri's oleron Multi-Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"","code":"scale_colour_oleron(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_color_oleron(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"colour\" )  scale_fill_oleron(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"","code":"data(volcano)  volcan <- data.frame(   x = rep(1:ncol(volcano), each = nrow(volcano)),   y = rep(1:nrow(volcano), times = ncol(volcano)),   z = as.numeric(volcano) )  ggplot2::ggplot(volcan, ggplot2::aes(x, y, fill = z)) +   ggplot2::geom_raster() +   scale_fill_oleron(midpoint = 125)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"Fabio Crameri's oslo Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"","code":"scale_colour_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_oslo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"Fabio Crameri's roma Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"","code":"scale_colour_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_roma(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"Fabio Crameri's romaO Cyclic Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"","code":"scale_colour_romaO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_romaO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_romaO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"Provides sequential color scales Fabio Crameri's Scientific colour.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works . discrete logical scalar: color scheme used discrete scale?","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"Available schemes: batlow batlowW batlowK devon lajolla bamako davos bilbao nuuk oslo grayC hawaii lapaz tokyo buda acton turku imola","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"Fabio Crameri's tofino Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"","code":"scale_colour_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_tofino(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"Fabio Crameri's tokyo Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"","code":"scale_colour_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_tokyo(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"Fabio Crameri's turku Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"","code":"scale_colour_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_turku(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_batlow()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_bamako()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_hawaii(reverse = TRUE)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"Fabio Crameri's vanimo Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"","code":"scale_colour_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_vanimo(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vanimo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's vanimo Diverging Color Scheme \u2014 scale_crameri_vanimo","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"Fabio Crameri's vik Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"","code":"scale_colour_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_vik(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vik.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's vik Diverging Color Scheme \u2014 scale_crameri_vik","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":null,"dir":"Reference","previous_headings":"","what":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"Fabio Crameri's vikO Cyclic Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"","code":"scale_colour_vikO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_vikO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_vikO(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"Crameri, F. (2021). Scientific colour maps. Zenodo, v7.0. doi:10.5281/zenodo.4491293","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_broc(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_colour_berlin(midpoint = 9000)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":null,"dir":"Reference","previous_headings":"","what":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"Provides AVHRR Global Land Cover classification modified Paul Tol (colorblind safe).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"","code":"scale_colour_land(..., lang = \"en\", aesthetics = \"colour\")  scale_color_land(..., lang = \"en\", aesthetics = \"colour\")  scale_fill_land(..., lang = \"en\", aesthetics = \"fill\")  scale_edge_colour_land(..., lang = \"en\")  scale_edge_color_land(..., lang = \"en\")  scale_edge_fill_land(..., lang = \"en\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"... Arguments passed ggplot2::discrete_scale(). lang character string specifying language color names (see details). must one \"en\" (english, default), \"fr\" (french) NULL. NULL, values matched based color names. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"Values matched based land classification names.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_land.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AVHRR Global Land Cover Classification Color Scheme for ggplot2\nand ggraph \u2014 scale_colour_land","text":"","code":"library(ggplot2)  land <- data.frame(   name = c(     \"water\", \"evergreen needleleaf forest\", \"deciduous needleleaf forest\",     \"mixed forest\", \"evergreen broadleaf forest\", \"deciduous broadleaf forest\",     \"woodland\", \"wooded grassland\", \"grassland\", \"cropland\", \"closed shrubland\",     \"open shrubland\", \"bare ground\", \"urban and built\"   ) )  ggplot2::ggplot(land) +   ggplot2::geom_rect(ggplot2::aes(xmin = rep(0, 14), xmax = rep(1, 14),                                   ymin = 1:14, ymax = 1:14+1, fill = name)) +   ggplot2::scale_y_reverse() +   scale_fill_land(name = \"land\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"Provides color scale logical values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"","code":"scale_colour_logical(..., aesthetics = \"colour\")  scale_color_logical(..., aesthetics = \"colour\")  scale_fill_logical(..., aesthetics = \"fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"... Arguments passed ggplot2::discrete_scale(). aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"discrete scale.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_logical_discrete.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Binary Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_logical_discrete","text":"","code":"library(ggplot2)  logic <- data.frame(   x = rep(1:5, times = 5),   y = rep(1:5, each = 5),   values = sample(c(TRUE, FALSE), 25, TRUE) )  ggplot2::ggplot(logic, ggplot2::aes(x, y, fill = values)) +   ggplot2::geom_raster() +   scale_fill_logical()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":null,"dir":"Reference","previous_headings":"","what":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"Provides qualitative color scale Okabe Ito 2008.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"","code":"scale_colour_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"colour\" )  scale_color_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"colour\" )  scale_fill_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"fill\" )  scale_edge_colour_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"edge_colour\" )  scale_edge_color_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"edge_colour\" )  scale_edge_fill_okabeito(   ...,   reverse = FALSE,   black_position = c(\"first\", \"last\"),   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? black_position character string giving position black color. must one \"first\" \"last\". unambiguous substring can given. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"qualitative color scheme used given (interpolation): colors picked maximum number supported values (8).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"Okabe, M. & Ito, K. (2008). Color Universal Design (CUD): Make Figures Presentations Friendly Colorblind People. URL: https://jfly.uni-koeln.de/color/.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_okabeito_discrete.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Okabe and Ito's Discrete Color Scheme for ggplot2 and ggraph \u2014 scale_okabeito_discrete","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_okabeito()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_okabeito(black_position = \"last\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":null,"dir":"Reference","previous_headings":"","what":"Color Scale Builder \u2014 scale_picker","title":"Color Scale Builder \u2014 scale_picker","text":"Builds color scale ggplot2 ggraph.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color Scale Builder \u2014 scale_picker","text":"","code":"scale_colour_picker(..., palette = \"YlOrBr\")  scale_color_picker(..., palette = \"YlOrBr\")  scale_fill_picker(..., palette = \"YlOrBr\")  scale_edge_colour_picker(..., palette = \"YlOrBr\")  scale_edge_color_picker(..., palette = \"YlOrBr\")  scale_edge_fill_picker(..., palette = \"YlOrBr\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color Scale Builder \u2014 scale_picker","text":"... Extra parameters passed color scale function. palette character string giving name color scheme used (see info()).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Color Scale Builder \u2014 scale_picker","text":"discrete continuous scale.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Color Scale Builder \u2014 scale_picker","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_picker.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color Scale Builder \u2014 scale_picker","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_picker(palette = \"okabeito\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":null,"dir":"Reference","previous_headings":"","what":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"Provides FAO Soil Reference Groups color scheme.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"","code":"scale_colour_soil(..., lang = \"en\", aesthetics = \"colour\")  scale_color_soil(..., lang = \"en\", aesthetics = \"colour\")  scale_fill_soil(..., lang = \"en\", aesthetics = \"fill\")  scale_edge_colour_soil(..., lang = \"en\")  scale_edge_color_soil(..., lang = \"en\")  scale_edge_fill_soil(..., lang = \"en\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"... Arguments passed ggplot2::discrete_scale(). lang character string specifying language color names (see details). must one \"en\" (english, default), \"fr\" (french) NULL. NULL, values matched based color names. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"Values matched based soil names.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"Jones, ., Montanarella, L. & Jones, R. (Ed.) (2005). Soil atlas Europe. Luxembourg: European Commission, Office Official Publications European Communities. 128 pp. ISBN: 92-894-8120-X.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_soil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FAO Soil Reference Groups Color Scheme for ggplot2 and ggraph \u2014 scale_colour_soil","text":"","code":"library(ggplot2)  soil <- data.frame(   name = c(     \"Acrisol\", \"Albeluvisol\", \"Andosol\", \"Anthrosol\", \"Arenosol\", \"Calcisol\",     \"Cambisol\", \"Chernozem\", \"Cryosol\", \"Fluvisol\", \"Kastanozem\", \"Gleysol\",     \"Gypsisol\", \"Histosol\", \"Leptosol\", \"Luvisol\", \"Phaeozem\", \"Planosol\",     \"Podzol\", \"Regosol\", \"Solonchak\", \"Solonetz\", \"Umbrisol\", \"Vertisol\"   ) )  ggplot2::ggplot(soil) +   ggplot2::geom_rect(ggplot2::aes(xmin = rep(0, 24), xmax = rep(1, 24),                                   ymin = 1:24, ymax = 1:24+1, fill = name)) +   ggplot2::scale_y_reverse() +   scale_fill_soil(name = \"Soil\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":null,"dir":"Reference","previous_headings":"","what":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"Provides geologic timescale color scheme.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"","code":"scale_colour_stratigraphy(..., lang = \"en\", aesthetics = \"colour\")  scale_color_stratigraphy(..., lang = \"en\", aesthetics = \"colour\")  scale_fill_stratigraphy(..., lang = \"en\", aesthetics = \"fill\")  scale_edge_colour_stratigraphy(..., lang = \"en\")  scale_edge_color_stratigraphy(..., lang = \"en\")  scale_edge_fill_stratigraphy(..., lang = \"en\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"... Arguments passed ggplot2::discrete_scale(). lang character string specifying language color names (see details). must one \"en\" (english, default), \"fr\" (french) NULL. NULL, values matched based color names. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"Values matched based geological unit names.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"Commission Geological Map World.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_stratigraphy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Geologic Timescale Color Scheme for ggplot2 and ggraph \u2014 scale_colour_stratigraphy","text":"","code":"library(ggplot2)  strati <- data.frame(   name = c(\"Phanerozoic\", \"Paleozoic\", \"Cambrian\", \"Ordovician\", \"Silurian\",            \"Devonian\", \"Carboniferous\", \"Mesozoic\", \"Triassic\", \"Cretaceous\",            \"Jurassic\", \"Cenozoic\", \"Paleogene\", \"Neogene\", \"Quaternary\"),   type = c(\"Eon\", \"Era\", \"Period\", \"Period\", \"Period\", \"Period\", \"Period\",            \"Era\", \"Period\", \"Period\", \"Period\", \"Era\", \"Period\", \"Period\",            \"Period\"),   start = c(541, 541, 541, 485, 444, 419, 359,             252, 252, 201, 145, 66, 66, 23, 2.6),   end = c(0, 252, 485, 444, 419, 359, 252,           66, 201, 145, 66, 2.6, 23, 2.6, 0) )  ## Keep chronological order in the legend strati$name <- factor(strati$name, levels = rev(unique(strati$name)),                       ordered = TRUE)  ## Workaround: use `limits = force` to remove unused values ggplot2::ggplot(strati) +   ggplot2::geom_rect(ggplot2::aes(xmin = rep(0, 15), xmax = rep(1, 15),                                   ymin = start, ymax = end, fill = name)) +   ggplot2::scale_y_reverse() +   ggplot2::facet_grid(. ~ type) +   scale_fill_stratigraphy(name = \"Stratigraphy\", limits = force)","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"Paul Tol's BuRd Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"","code":"scale_colour_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_BuRd(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_BuRd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's BuRd Diverging Color Scheme \u2014 scale_tol_BuRd","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_sunset(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_BuRd(midpoint = 9000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_PRGn(midpoint = 9000, range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"Paul Tol's PRGn Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"","code":"scale_colour_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_PRGn(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_PRGn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's PRGn Diverging Color Scheme \u2014 scale_tol_PRGn","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_sunset(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_BuRd(midpoint = 9000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_PRGn(midpoint = 9000, range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"Paul Tol's YlOrBr Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"","code":"scale_colour_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_YlOrBr(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ). Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_YlOrBr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's YlOrBr Sequential Color Scheme \u2014 scale_tol_YlOrBr","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_YlOrBr()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_iridescent(reverse = TRUE)   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_smoothrainbow(range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"Paul Tol's bright Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"","code":"scale_colour_bright(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_bright(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_bright(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_bright(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_bright(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_bright(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_bright.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's bright Discrete Color Scheme \u2014 scale_tol_bright","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"Paul Tol's dark Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"","code":"scale_colour_dark(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_dark(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_dark(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_dark(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_dark(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_dark(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_dark.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's dark Discrete Color Scheme \u2014 scale_tol_dark","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"Provides qualitative color scales Paul Tol's Colour Schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"","code":"scale_fill_muted(..., reverse = FALSE, aesthetics = \"fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"discrete scale.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discrete.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's Discrete Color Schemes for ggplot2 and ggraph \u2014 scale_tol_discrete","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"Paul Tol's discrete rainbow Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"","code":"scale_colour_discreterainbow(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_discreterainbow(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_discreterainbow(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_discreterainbow(   ...,   reverse = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_discreterainbow(   ...,   reverse = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_discreterainbow(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ). Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"rainbow-color-scheme","dir":"Reference","previous_headings":"","what":"Rainbow Color Scheme","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"general rule, ordered data represented using rainbow scheme. three main arguments use (Tol 2018): spectral order visible light carries inherent magnitude message. bands almost constant hue sharp transitions , can perceived jumps data. Color-blind people difficulty distinguishing colors rainbow. use avoided, Paul Tol's technical note provides two color schemes reasonably clear color-blind vision. remain color-blind safe, two schemes must comply following conditions: discreterainbow scheme must interpolated. smoothrainbow scheme used full range.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_discreterainbow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's discrete rainbow Sequential Color Scheme \u2014 scale_tol_discreterainbow","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_YlOrBr()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_iridescent(reverse = TRUE)   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_smoothrainbow(range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"Provides diverging color scales Paul Tol's Colour Schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"continuous scale.","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_diverging.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_tol_diverging","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_sunset(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_BuRd(midpoint = 9000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_PRGn(midpoint = 9000, range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"Paul Tol's high contrast Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"","code":"scale_colour_highcontrast(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_highcontrast(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_highcontrast(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_highcontrast(   ...,   reverse = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_highcontrast(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_highcontrast(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_highcontrast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's high contrast Discrete Color Scheme \u2014 scale_tol_highcontrast","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"Paul Tol's iridescent Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"","code":"scale_colour_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_iridescent(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ). Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_iridescent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's iridescent Sequential Color Scheme \u2014 scale_tol_iridescent","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_YlOrBr()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_iridescent(reverse = TRUE)   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_smoothrainbow(range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"Paul Tol's light Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"","code":"scale_colour_light(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_light(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_light(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_light(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_light(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_light(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_light.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's light Discrete Color Scheme \u2014 scale_tol_light","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"Paul Tol's medium contrast Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"","code":"scale_colour_mediumcontrast(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_mediumcontrast(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_mediumcontrast(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_mediumcontrast(   ...,   reverse = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_mediumcontrast(   ...,   reverse = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_mediumcontrast(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_mediumcontrast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's medium contrast Discrete Color Scheme \u2014 scale_tol_mediumcontrast","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"Paul Tol's muted Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"","code":"scale_colour_muted(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_muted(..., reverse = FALSE, aesthetics = \"colour\")  scale_edge_colour_muted(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_muted(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_muted(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_muted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's muted Discrete Color Scheme \u2014 scale_tol_muted","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"Paul Tol's pale Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"","code":"scale_colour_pale(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_pale(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_pale(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_pale(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_pale(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_pale(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_pale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's pale Discrete Color Scheme \u2014 scale_tol_pale","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"Provides sequential color scales Paul Tol's Colour Schemes.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works . discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"#'","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ). Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"rainbow-color-scheme","dir":"Reference","previous_headings":"","what":"Rainbow Color Scheme","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"general rule, ordered data represented using rainbow scheme. three main arguments use (Tol 2018): spectral order visible light carries inherent magnitude message. bands almost constant hue sharp transitions , can perceived jumps data. Color-blind people difficulty distinguishing colors rainbow. use avoided, Paul Tol's technical note provides two color schemes reasonably clear color-blind vision. remain color-blind safe, two schemes must comply following conditions: discreterainbow scheme must interpolated. smoothrainbow scheme used full range.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sequential.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_tol_sequential","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_YlOrBr()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_iridescent(reverse = TRUE)   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_smoothrainbow(range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"Paul Tol's smooth rainbow Sequential Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"","code":"scale_colour_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_smoothrainbow(   ...,   reverse = FALSE,   range = c(0, 1),   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme, exception discrete rainbow scheme (see ). Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"rainbow-color-scheme","dir":"Reference","previous_headings":"","what":"Rainbow Color Scheme","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"general rule, ordered data represented using rainbow scheme. three main arguments use (Tol 2018): spectral order visible light carries inherent magnitude message. bands almost constant hue sharp transitions , can perceived jumps data. Color-blind people difficulty distinguishing colors rainbow. use avoided, Paul Tol's technical note provides two color schemes reasonably clear color-blind vision. remain color-blind safe, two schemes must comply following conditions: discreterainbow scheme must interpolated. smoothrainbow scheme used full range.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_smoothrainbow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's smooth rainbow Sequential Color Scheme \u2014 scale_tol_smoothrainbow","text":"","code":"data(faithfuld, package = \"ggplot2\")  ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_YlOrBr()   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_iridescent(reverse = TRUE)   ggplot2::ggplot(faithfuld, ggplot2::aes(waiting, eruptions, fill = density)) +   ggplot2::geom_raster() +   scale_fill_smoothrainbow(range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"Paul Tol's sunset Diverging Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"","code":"scale_colour_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_color_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"colour\" )  scale_fill_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"fill\" )  scale_edge_colour_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_color_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_colour\" )  scale_edge_fill_sunset(   ...,   reverse = FALSE,   range = c(0, 1),   midpoint = 0,   discrete = FALSE,   aesthetics = \"edge_fill\" )","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"... Arguments passed ggplot2::continuous_scale(). reverse logical scalar. resulting vector colors reversed? range length-two numeric vector specifying fraction scheme's color domain keep. midpoint length-one numeric vector giving midpoint (data value) diverging scale. Defaults 0. discrete logical scalar: color scheme used discrete scale? TRUE, departure Paul Tol's recommendations likely poor use color. aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"continuous scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"colors defined needed given scheme, color coordinates linearly interpolated provide continuous version scheme. Note default color NA can overridden passing value ggplot2::continuous_scale().","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"Tol, P. (2018). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.1. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_sunset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's sunset Diverging Color Scheme \u2014 scale_tol_sunset","text":"","code":"data(economics, package = \"ggplot2\")  ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_sunset(reverse = TRUE, midpoint = 12000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_BuRd(midpoint = 9000)   ggplot2::ggplot(economics, ggplot2::aes(psavert, pce, colour = unemploy)) +   ggplot2::geom_point() +   scale_color_PRGn(midpoint = 9000, range = c(0.25, 1))","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":null,"dir":"Reference","previous_headings":"","what":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"Paul Tol's vibrant Discrete Color Scheme","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"","code":"scale_colour_vibrant(..., reverse = FALSE, aesthetics = \"colour\")  scale_color_vibrant(..., reverse = FALSE, aesthetics = \"colour\")  scale_fill_vibrant(..., reverse = FALSE, aesthetics = \"fill\")  scale_edge_colour_vibrant(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_color_vibrant(..., reverse = FALSE, aesthetics = \"edge_colour\")  scale_edge_fill_vibrant(..., reverse = FALSE, aesthetics = \"edge_fill\")","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"... Arguments passed ggplot2::discrete_scale(). reverse logical scalar. resulting vector colors reversed? aesthetics character string vector character strings listing name(s) aesthetic(s) scale works .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"discrete scale.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"interpolation","dir":"Reference","previous_headings":"","what":"Interpolation","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"qualitative color schemes used given (interpolation): colors picked maximum number supported values.","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"qualitative-color-schemes","dir":"Reference","previous_headings":"","what":"Qualitative Color Schemes","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"According Paul Tol's technical note, bright, highcontrast, vibrant muted color schemes color-blind safe. mediumcontrast color scheme designed situations needing color pairs. light color scheme reasonably distinct normal colorblind vision intended fill labeled cells. pale dark schemes distinct either normal colorblind vision used text background highlight cell table. Refer original document details recommended uses (see references).","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"Tol, P. (2021). Colour Schemes. SRON. Technical Note . SRON/EPS/TN/09-002, issue 3.2. URL: https://personal.sron.nl/~pault/data/colourschemes.pdf","code":"","package":"khroma"},{"path":[],"package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"N. Frerebeau","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_tol_vibrant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Paul Tol's vibrant Discrete Color Scheme \u2014 scale_tol_vibrant","text":"","code":"library(ggplot2)  ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_bright()   ggplot2::ggplot(mpg, ggplot2::aes(displ, hwy, colour = class)) +   ggplot2::geom_point() +   scale_colour_vibrant()   ggplot2::ggplot(diamonds, ggplot2::aes(clarity, fill = cut)) +   ggplot2::geom_bar() +   scale_fill_muted()","package":"khroma"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Ternary Plots","text":"Ternary plots made simple. isopleuros allows create ternary plot using graphics. provides functions display data ternary space, add tune graphical elements display statistical summaries. also includes common ternary diagrams useful archaeologist (e.g.\u00a0soil texture charts, ceramic phase diagram). isopleuros dependency-free package1 designed simple possible. need finer tuning advanced features, consider Ternary ggtern package.","code":"To cite isopleuros in publications use:    Frerebeau N (2023). _isopleuros: Ternary Plots_. Universit\u00e9 Bordeaux   Montaigne, Pessac, France. R package version 1.0.0,   <https://packages.tesselle.org/isopleuros/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{isopleuros: Ternary Plots}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.0.0},     url = {https://packages.tesselle.org/isopleuros/},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Ternary Plots","text":"can install released version isopleuros CRAN : development version GitHub :","code":"install.packages(\"isopleuros\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/isopleuros\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Ternary Plots","text":"","code":"## Load package library(isopleuros) ## Graphical parameters par(mfrow = c(1, 2), mar = c(0, 0, 0, 0) + 0.1)  ## Set colors col <- c(\"blue\", \"red\")  ## Ternary plot ## (data from Aitchison 1986) ternary_plot(   x = lava,    panel.first = ternary_grid(),   col = col[as.factor(lava$A > 30)], )  ## Split data groups <- split(lava, f = lava$A > 30)  ## Add tolerance ellipses for (i in seq_along(groups)) {   ternary_tolerance(groups[[i]], level = 0.975, lty = 2, border = col[[i]]) }  ## Density contours ternary_plot(lava, panel.first = ternary_grid()) ternary_density(lava, n = 500, nlevels = 10) ## Install extra package (if needed) # install.packages(\"folio\")  ## Data from Barrera and Velde 1989 data(\"verre\", package = \"folio\")  ## Select data coda <- verre[, c(\"Na2O\", \"CaO\", \"K2O\", \"MgO\", \"P2O5\", \"Al2O3\")]  ## Ternary plots with marginal compositions ternary_pairs(coda, col = as.factor(coda$Na2O > 5)) ## Graphical parameters par(mfrow = c(2, 2), mar = c(0, 0, 0, 0) + 0.1)  ## Ceramic phase diagram ternary_plot(NULL, axes = FALSE, ann = FALSE, frame.plot = TRUE) triangle_phase_cas(symbol = TRUE, pch = 16)  ternary_plot(NULL, xlab = \"CaO\", ylab = \"Al2O3\", zlab = \"SiO2\") triangle_phase_ceramic(symbol = TRUE, pch = 16)  ## HYPRES soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_hypres()  ## USDA (1951) soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_usda(symbol = TRUE)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Ternary Plots","text":"Please note isopleuros project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/arctic.html","id":null,"dir":"Reference","previous_headings":"","what":"Arctic Lake Sediments Compositions \u2014 arctic","title":"Arctic Lake Sediments Compositions \u2014 arctic","text":"Sand, silt, clay compositions 39 sediment samples different water depths Arctic lake.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/arctic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arctic Lake Sediments Compositions \u2014 arctic","text":"","code":"arctic","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/arctic.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Arctic Lake Sediments Compositions \u2014 arctic","text":"data.frame 4 variables: sand  silt  clay  depth Water depth (m).","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/arctic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Arctic Lake Sediments Compositions \u2014 arctic","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/boxite.html","id":null,"dir":"Reference","previous_headings":"","what":"Boxite Compositions \u2014 boxite","title":"Boxite Compositions \u2014 boxite","text":"Compositions 25 specimens boxite.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/boxite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boxite Compositions \u2014 boxite","text":"","code":"boxite","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/boxite.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Boxite Compositions \u2014 boxite","text":"data.frame 5 variables: albite. B blandite. C cornite. D daubite. E endite.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/boxite.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Boxite Compositions \u2014 boxite","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":null,"dir":"Reference","previous_headings":"","what":"Cartesian Coordinates \u2014 coordinates_cartesian","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"Computes cartesian coordinates.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"","code":"coordinates_cartesian(x, y, z, ...)  # S4 method for numeric,numeric coordinates_cartesian(x, y)  # S4 method for ANY,missing coordinates_cartesian(x)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"x, y numeric vector giving x y ternary coordinates set points. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"list.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_cartesian.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cartesian Coordinates \u2014 coordinates_cartesian","text":"","code":"## Compositional data coda <- data.frame(   x = c(1, 0, 0, 1/3),   y = c(0, 1, 0, 1/3),   z = c(0, 0, 1, 1/3) )  ## Ternary coordinates (tern <- coordinates_ternary(coda)) #> $x #> [1] 0.0 1.0 0.5 0.5 #>  #> $y #> [1] 0.0000000 0.0000000 0.8660254 0.2886751 #>   ## Cartesian coordinates (cart <- coordinates_cartesian(tern)) #> $x #> [1] 1.0000000 0.0000000 0.0000000 0.3333333 #>  #> $y #> [1] 0.0000000 1.0000000 0.0000000 0.3333333 #>  #> $z #> [1] 0.0000000 0.0000000 1.0000000 0.3333333 #>","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":null,"dir":"Reference","previous_headings":"","what":"Ternary Coordinates \u2014 coordinates_ternary","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"Computes ternary coordinates.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"","code":"coordinates_ternary(x, y, z, ...)  # S4 method for numeric,numeric,numeric coordinates_ternary(x, y, z, missing = getOption(\"isopleuros.missing\"))  # S4 method for ANY,missing,missing coordinates_ternary(x, missing = getOption(\"isopleuros.missing\"))","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"x, y, z numeric vector giving x, y z cartesian coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... Currently used. missing logical scalar: missing values replaced zeros computation proceeds? FALSE (default), incomplete cases removed.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"list.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/coordinates_ternary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ternary Coordinates \u2014 coordinates_ternary","text":"","code":"## Compositional data coda <- data.frame(   x = c(1, 0, 0, 1/3),   y = c(0, 1, 0, 1/3),   z = c(0, 0, 1, 1/3) )  ## Ternary coordinates (tern <- coordinates_ternary(coda)) #> $x #> [1] 0.0 1.0 0.5 0.5 #>  #> $y #> [1] 0.0000000 0.0000000 0.8660254 0.2886751 #>   ## Cartesian coordinates (cart <- coordinates_cartesian(tern)) #> $x #> [1] 1.0000000 0.0000000 0.0000000 0.3333333 #>  #> $y #> [1] 0.0000000 1.0000000 0.0000000 0.3333333 #>  #> $z #> [1] 0.0000000 0.0000000 1.0000000 0.3333333 #>","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/isopleuros-package.html","id":null,"dir":"Reference","previous_headings":"","what":"isopleuros: Ternary Plots \u2014 isopleuros-package","title":"isopleuros: Ternary Plots \u2014 isopleuros-package","text":"Ternary plots made simple. package allows create ternary plots using 'graphics'. provides functions display data ternary space, add tune graphical elements display statistical summaries. also includes common ternary diagrams useful archaeologist (e.g. soil texture charts, ceramic phase diagram).","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/isopleuros-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"isopleuros: Ternary Plots \u2014 isopleuros-package","text":"isopleuros uses following options() configure behavior: isopleuros.missing: logical scalar. missing values replaced zeros ternary coordinates computation proceeds? FALSE (default), incomplete cases removed.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/isopleuros-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"isopleuros: Ternary Plots \u2014 isopleuros-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne 33607 Pessac cedex France","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/lava.html","id":null,"dir":"Reference","previous_headings":"","what":"Skye Lavas Compositions \u2014 lava","title":"Skye Lavas Compositions \u2014 lava","text":"AFM compositions 23 aphyric Skye lavas.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/lava.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skye Lavas Compositions \u2014 lava","text":"","code":"lava","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/lava.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Skye Lavas Compositions \u2014 lava","text":"data.frame 3 variables: Na2O + K2O (percent). F Fe2O3 (percent). M MgO (percent).","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/lava.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Skye Lavas Compositions \u2014 lava","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"Draw arrows pairs points.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"","code":"ternary_arrows(x0, y0, z0, ...)  # S4 method for numeric,numeric,numeric ternary_arrows(x0, y0, z0, x1 = x0, y1 = y0, z1 = z0, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"x0, y0, z0 numeric vector giving x, y z ternary coordinates points draw. ... arguments passed graphics::arrows(). x1, y1, z1 numeric vector giving x, y z ternary coordinates points draw.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"ternary_arrows() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_arrows.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Arrows to a Ternary Plot \u2014 ternary_arrows","text":"","code":"## Add arrows ternary_plot(NULL, panel.first = ternary_grid()) ternary_arrows(x0 = 40, y0 = 20, z0 = 40,                x1 = 20, y1 = 40, z1 = 40)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an Axis to a Ternary Plot \u2014 ternary_axis","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"Adds axis current plot.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"","code":"ternary_axis(   side,   at = NULL,   labels = TRUE,   tick = TRUE,   font = NA,   lty = \"solid\",   lwd = 1,   lwd.ticks = lwd,   col = NULL,   col.ticks = NULL,   ... )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"side integer specifying side plot axis drawn . axis placed follows: 1=, 2=right 3=left. numeric vector giving points tick-marks drawn. labels logical scalar specifying whether (numerical) annotations made tickmarks, character vector labels placed tickpoints. logical, also supplied length. tick logical scalar: tickmarks axis line drawn? font font text. Defaults par(\"font.axis\"). lty character string numeric value specifying line type axis line tick marks. lwd, lwd.ticks non-negative numeric value specifying line widths axis line tick marks. col, col.ticks Colors axis line tick marks respectively. Defaults par(\"col.axis\"). ... graphical parameters may also passed arguments function, particularly, cex.axis, col.axis font.axis axis annotation.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"ternary_axis() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_axis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an Axis to a Ternary Plot \u2014 ternary_axis","text":"","code":"## Add axis ternary_plot(NULL, axes = FALSE) ternary_axis(side = 1, col = \"red\") ternary_axis(side = 2, col = \"blue\") ternary_axis(side = 3, col = \"green\")   ## Add box and grid ternary_plot(NULL, axes = FALSE) ternary_box(lty = \"dashed\", col = \"red\") ternary_grid(lty.primary = \"dotted\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw a Box around a Ternary Plot \u2014 ternary_box","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"Draw Box around Ternary Plot","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"","code":"ternary_box(lty = \"solid\", ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"lty character string numeric value specifying line type box. ... graphical parameters may also passed arguments function, particularly, col lwd.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"ternary_box() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_box.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw a Box around a Ternary Plot \u2014 ternary_box","text":"","code":"## Add axis ternary_plot(NULL, axes = FALSE) ternary_axis(side = 1, col = \"red\") ternary_axis(side = 2, col = \"blue\") ternary_axis(side = 3, col = \"green\")   ## Add box and grid ternary_plot(NULL, axes = FALSE) ternary_box(lty = \"dashed\", col = \"red\") ternary_grid(lty.primary = \"dotted\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":null,"dir":"Reference","previous_headings":"","what":"Contour Lines \u2014 ternary_contour","title":"Contour Lines \u2014 ternary_contour","text":"Computes draws contour lines.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contour Lines \u2014 ternary_contour","text":"","code":"ternary_contour(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_contour(   x,   y,   z,   value,   n = 50,   nlevels = 10,   levels = pretty(range(value, na.rm = TRUE), nlevels),   ilr = TRUE,   linear = TRUE,   extrapolate = FALSE,   palette = function(i) grDevices::hcl.colors(i, \"YlOrRd\", rev = TRUE),   ... )  # S4 method for ANY,missing,missing ternary_contour(   x,   value,   n = 50,   nlevels = 10,   levels = pretty(range(value, na.rm = TRUE), nlevels),   ilr = TRUE,   linear = TRUE,   extrapolate = FALSE,   palette = function(i) grDevices::hcl.colors(i, \"YlOrRd\", rev = TRUE),   ... )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Contour Lines \u2014 ternary_contour","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed ternary_lines(). value numeric vector giving values plotted. n length-one numeric specifying number grid points. nlevels length-one numeric vector specifying number contour levels desired. used levels NULL. levels numeric vector levels draw contour lines. ilr logical scalar: interpolation computed ILR space? FALSE, interpolation computed Cartesian space. linear logical scalar: linear interpolation used? FALSE, spline interpolation used (see akima::interp()). extrapolate logical scalar: extrapolation used outside convex hull determined data points (see akima::interp())? palette color palette function takes single integer argument (number levels) returns vector colors.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Contour Lines \u2014 ternary_contour","text":"ternary_contour() called side-effects. Invisibly returns list elements levels (contour levels) colors (contour colors) can used legend.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Contour Lines \u2014 ternary_contour","text":"Contour computed bivariate interpolation onto grid, isometric log ratio transformation original data.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Contour Lines \u2014 ternary_contour","text":"akima package needs installed machine.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Contour Lines \u2014 ternary_contour","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_contour.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Contour Lines \u2014 ternary_contour","text":"","code":"## Add density ## Data from Aitchison 1986 ternary_plot(arctic, panel.first = ternary_grid()) levels <- ternary_contour(arctic, value = arctic$depth, n = 100, nlevels = 10)  ## Add a legend legend_image <- grDevices::as.raster(rev(levels$colors)) graphics::rasterImage(legend_image, 0.85, 0.75, 0.9, 1) graphics::text(x = 0.9, y = c(0.75, 1), labels = range(levels$levels), pos = 4)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"Draw lines intersect point.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"","code":"ternary_crosshairs(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_crosshairs(x, y, z, x_mark = TRUE, y_mark = TRUE, z_mark = TRUE, ...)  # S4 method for ANY,missing,missing ternary_crosshairs(x, x_mark = TRUE, y_mark = TRUE, z_mark = TRUE, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... graphical parameters (see graphics::par()) may also supplied arguments, particularly, line type, lty, line width, lwd color, col. Also line characteristics lend, ljoin lmitre. x_mark, y_mark, z_mark logical scalar: x, y z axis component drawn?","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"ternary_crosshairs() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_crosshairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Cross-Hairs to a Ternary Plot \u2014 ternary_crosshairs","text":"","code":"## Add cross-hairs ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid()) ternary_crosshairs(lava)   ternary_plot(lava, panel.first = ternary_grid()) ternary_crosshairs(lava, y_mark = FALSE, z_mark = FALSE, col = \"red\")   ternary_plot(lava, panel.first = ternary_grid()) ternary_crosshairs(lava, x_mark = FALSE, z_mark = FALSE, col = \"green\")   ternary_plot(lava, panel.first = ternary_grid()) ternary_crosshairs(lava, x_mark = FALSE, y_mark = FALSE, col = \"blue\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Density Contour Lines \u2014 ternary_density","title":"Density Contour Lines \u2014 ternary_density","text":"Computes draws density contour lines.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Density Contour Lines \u2014 ternary_density","text":"","code":"ternary_density(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_density(   x,   y,   z,   h = NULL,   n = 25,   nlevels = 10,   levels = NULL,   palette = function(i) grDevices::hcl.colors(i, \"YlOrRd\", rev = TRUE),   ... )  # S4 method for ANY,missing,missing ternary_density(   x,   h = NULL,   n = 25,   nlevels = 10,   levels = NULL,   palette = function(i) grDevices::hcl.colors(i, \"YlOrRd\", rev = TRUE),   ... )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Density Contour Lines \u2014 ternary_density","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed ternary_lines(). h length-one numeric vector giving bandwidth. n length-one numeric specifying number grid points. nlevels length-one numeric vector specifying number contour levels desired. used levels NULL. levels numeric vector levels draw contour lines. palette color palette function takes single integer argument (number levels) returns vector colors.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Density Contour Lines \u2014 ternary_density","text":"ternary_density() called side-effects. Invisibly returns list elements levels (contour levels) colors (contour colors) can used legend.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Density Contour Lines \u2014 ternary_density","text":"Two-dimensional kernel density estimation axis-aligned bivariate normal kernel. Normal kernel evaluated square grid, isometric log ratio transformation original data.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Density Contour Lines \u2014 ternary_density","text":"Two-dimensional kernel density estimation adapted MASS::kde2d(). must considered experimental subject major changes future release.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Density Contour Lines \u2014 ternary_density","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Density Contour Lines \u2014 ternary_density","text":"","code":"## Add density ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid()) levels <- ternary_density(lava, n = 500, nlevels = 10)  ## Add a legend legend_image <- grDevices::as.raster(rev(levels$colors)) graphics::rasterImage(legend_image, 0.85, 0.75, 0.9, 1) graphics::text(x = 0.9, y = c(0.75, 1), labels = range(levels$levels), pos = 4)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"Computes draws confidence/tolerance ellipse.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"","code":"ternary_ellipse(x, y, z, ...)  ternary_confidence(x, y, z, ...)  ternary_tolerance(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_ellipse(x, y, z, radius = 1, ...)  # S4 method for ANY,missing,missing ternary_ellipse(x, radius = 1, ...)  # S4 method for numeric,numeric,numeric ternary_confidence(x, y, z, level = 0.95, ...)  # S4 method for ANY,missing,missing ternary_confidence(x, level = 0.95, ...)  # S4 method for numeric,numeric,numeric ternary_tolerance(x, y, z, level = 0.95, ...)  # S4 method for ANY,missing,missing ternary_tolerance(x, level = 0.95, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::polygon(). radius numeric vector specifying scaling half-diameters. level numeric vector specifying confidence/tolerance level.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"ternary_ellipse() called side-effects.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"Ellipse coordinates computed isometric log ratio transformation original data.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_ellipse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an Ellipse to a Ternary Plot \u2014 ternary_ellipse","text":"","code":"## Ellipses ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid(5, 10)) ternary_tolerance(lava, level = 0.95, border = \"blue\", lty = 2) ternary_confidence(lava, level = 0.95, border = \"red\", lty = 3)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Grid to a Ternary Plot \u2014 ternary_grid","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"Adds triangular grid existing plot.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"","code":"ternary_grid(   primary = NULL,   secondary = NULL,   col.primary = \"darkgray\",   col.secondary = \"lightgray\",   lty.primary = \"dashed\",   lty.secondary = \"dotted\",   lwd.primary = 1,   lwd.secondary = lwd.primary )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"primary integer specifying number cells primary grid x, y z direction. secondary integer specifying number cells secondary grid x, y z direction. col.primary, col.secondary character string specifying color grid lines. lty.primary, lty.secondary character string numeric value specifying line type grid lines. lwd.primary, lwd.secondary non-negative numeric value specifying line width grid lines.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"ternary_grid() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Grid to a Ternary Plot \u2014 ternary_grid","text":"","code":"## Blank plot ternary_plot(NULL)   ## Compositional data coda <- data.frame(   X = c(20, 60, 20, 1/3),   Y = c(20, 20, 60, 1/3),   Z = c(60, 20, 20, 1/3) )  ## Ternary plot ternary_plot(coda, pch = 16, col = \"red\")   ## Add a grid ternary_plot(coda, panel.first = ternary_grid(5, 10))   ## Zoom ternary_plot(coda, xlim = c(0.5, 1), panel.first = ternary_grid())  ternary_plot(coda, ylim = c(0.5, 1), panel.first = ternary_grid())  ternary_plot(coda, zlim = c(0.5, 1), panel.first = ternary_grid())   ## Color according to a supplementary variable ## Data from Aitchison 1986 col <- grDevices::colorRampPalette(c(\"red\", \"blue\"))(nrow(arctic)) ternary_plot(arctic, panel.first = ternary_grid(), pch = 16, col = col)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":null,"dir":"Reference","previous_headings":"","what":"Convex Hull of a Set of Points \u2014 ternary_hull","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"Computes draws convex hull set points specified.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"","code":"ternary_hull(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_hull(x, y, z, ...)  # S4 method for ANY,missing,missing ternary_hull(x, y, z, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::polygon().","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"ternary_hull() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_hull.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convex Hull of a Set of Points \u2014 ternary_hull","text":"","code":"## Convex hull ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid(5, 10)) ternary_hull(lava, border = \"red\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"Add Connected Line Segments Ternary Plot","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"","code":"ternary_lines(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_lines(x, y, z, type = \"l\", ...)  # S4 method for ANY,missing,missing ternary_lines(x, type = \"l\", ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... graphical parameters (see graphics::par()) may also supplied arguments, particularly, line type, lty, line width, lwd, color, col type = \"b\", pch. Also line characteristics lend, ljoin lmitre. type character string indicating type plotting; actually types graphics::plot.default().","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"ternary_lines() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_lines.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Connected Line Segments to a Ternary Plot \u2014 ternary_lines","text":"","code":"## Compositional data coda <- data.frame(   X = c(20, 60, 20, 20),   Y = c(20, 20, 60, 40),   Z = c(60, 20, 20, 40) )  ## Add lines ternary_plot(NULL, panel.first = ternary_grid()) ternary_lines(coda, col = \"red\", lwd = 2)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Compositional Mean \u2014 ternary_mean","title":"Compositional Mean \u2014 ternary_mean","text":"Computes draws closed geometric mean set points specified.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compositional Mean \u2014 ternary_mean","text":"","code":"ternary_mean(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_mean(x, y, z, ...)  # S4 method for ANY,missing,missing ternary_mean(x, y, z, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compositional Mean \u2014 ternary_mean","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::points().","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compositional Mean \u2014 ternary_mean","text":"ternary_mean() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compositional Mean \u2014 ternary_mean","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compositional Mean \u2014 ternary_mean","text":"","code":"## Mean ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid()) ternary_mean(lava, pch = 16, col = \"red\") ternary_confidence(lava, level = 0.95, border = \"red\", lty = 1)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":null,"dir":"Reference","previous_headings":"","what":"Ternary Plot Matrices \u2014 ternary_pairs","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"Produces matrix ternary plots.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"","code":"ternary_pairs(x, ...)  # S4 method for matrix ternary_pairs(x, margin = NULL, ...)  # S4 method for data.frame ternary_pairs(x, margin = NULL, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"x matrix data.frame. Columns converted numeric way data.matrix() . ... arguments passed graphics::arrows(). margin character string integer giving index column used third part ternary plots. NULL (default), marginal compositions used (.e. geometric mean non-selected parts).","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"ternary_pairs() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ternary Plot Matrices \u2014 ternary_pairs","text":"","code":"## Data from Aitchison 1986 ## Ternary plots with marginal compositions ternary_pairs(boxite)   ## Ternary plots with endite ternary_pairs(boxite, margin = \"E\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis \u2014 ternary_pca","title":"Principal Component Analysis \u2014 ternary_pca","text":"Computes draws principal component.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis \u2014 ternary_pca","text":"","code":"ternary_pca(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_pca(x, y, z, axis = 1, ...)  # S4 method for ANY,missing,missing ternary_pca(x, axis = 1, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis \u2014 ternary_pca","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::lines(). axis integer specifying dimension plotted.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis \u2014 ternary_pca","text":"ternary_pca() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Principal Component Analysis \u2014 ternary_pca","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis \u2014 ternary_pca","text":"","code":"## PCA ## Data from Aitchison 1986 ternary_plot(lava, panel.first = ternary_grid()) ternary_pca(lava, axis = 1, col = \"red\", lty = 2)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Ternary Plot \u2014 ternary_plot","title":"Ternary Plot \u2014 ternary_plot","text":"Produces ternary plot.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ternary Plot \u2014 ternary_plot","text":"","code":"ternary_plot(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_plot(   x,   y,   z,   xlim = NULL,   ylim = NULL,   zlim = NULL,   xlab = NULL,   ylab = NULL,   zlab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for ANY,missing,missing ternary_plot(   x,   xlim = NULL,   ylim = NULL,   zlim = NULL,   xlab = NULL,   ylab = NULL,   zlab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ternary Plot \u2014 ternary_plot","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... graphical parameters may also passed arguments function. xlim length-two numeric vector giving x limits range \\([0,1]\\). ylim length-two numeric vector giving y limits range \\([0,1]\\). zlim length-two numeric vector giving z limits range \\([0,1]\\). xlab, ylab, zlab character string giving label x, y z axes. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ternary Plot \u2014 ternary_plot","text":"ternary_plot() called side-effects: results graphic displayed.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ternary Plot \u2014 ternary_plot","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ternary Plot \u2014 ternary_plot","text":"","code":"## Blank plot ternary_plot(NULL)   ## Compositional data coda <- data.frame(   X = c(20, 60, 20, 1/3),   Y = c(20, 20, 60, 1/3),   Z = c(60, 20, 20, 1/3) )  ## Ternary plot ternary_plot(coda, pch = 16, col = \"red\")   ## Add a grid ternary_plot(coda, panel.first = ternary_grid(5, 10))   ## Zoom ternary_plot(coda, xlim = c(0.5, 1), panel.first = ternary_grid())  ternary_plot(coda, ylim = c(0.5, 1), panel.first = ternary_grid())  ternary_plot(coda, zlim = c(0.5, 1), panel.first = ternary_grid())   ## Color according to a supplementary variable ## Data from Aitchison 1986 col <- grDevices::colorRampPalette(c(\"red\", \"blue\"))(nrow(arctic)) ternary_plot(arctic, panel.first = ternary_grid(), pch = 16, col = col)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Points to a Ternary Plot \u2014 ternary_points","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"Add Points Ternary Plot","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"","code":"ternary_points(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_points(x, y, z, type = \"p\", ...)  # S4 method for ANY,missing,missing ternary_points(x, type = \"p\", ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... graphical parameters (see graphics::par()) may also supplied arguments, particularly, plotting character, pch, character expansion, cex color, col. type character string indicating type plotting; actually types graphics::plot.default().","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"ternary_points() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_points.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Points to a Ternary Plot \u2014 ternary_points","text":"","code":"## Add points ## Data from Aitchison 1986 ternary_plot(NULL, panel.first = ternary_grid()) ternary_points(lava, col = \"red\", pch = 16)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":null,"dir":"Reference","previous_headings":"","what":"Polygon Drawing \u2014 ternary_polygon","title":"Polygon Drawing \u2014 ternary_polygon","text":"Draws polygons whose vertices given x, y z.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polygon Drawing \u2014 ternary_polygon","text":"","code":"ternary_polygon(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_polygon(x, y, z, ...)  # S4 method for ANY,missing,missing ternary_polygon(x, y, z, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polygon Drawing \u2014 ternary_polygon","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::polygon().","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Polygon Drawing \u2014 ternary_polygon","text":"ternary_polygon() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Polygon Drawing \u2014 ternary_polygon","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_polygon.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polygon Drawing \u2014 ternary_polygon","text":"","code":"## Compositional data coda <- data.frame(   X = c(20, 60, 20),   Y = c(20, 20, 60),   Z = c(60, 20, 20) )  ## Add a polygon ternary_plot(NULL, panel.first = ternary_grid()) ternary_polygon(coda, density = 5, border = \"red\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"Draw line segments pairs points.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"","code":"ternary_segments(x0, y0, z0, ...)  # S4 method for numeric,numeric,numeric ternary_segments(x0, y0, z0, x1 = x0, y1 = y0, z1 = z0, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"x0, y0, z0 numeric vector giving x, y z ternary coordinates points draw. ... graphical parameters (see graphics::par()) may also supplied arguments, particularly, line type, lty, line width, lwd color, col. Also line characteristics lend, ljoin lmitre. x1, y1, z1 numeric vector giving x, y z ternary coordinates points draw.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"ternary_segments() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_segments.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Line Segments to a Ternary Plot \u2014 ternary_segments","text":"","code":"## Add segments ternary_plot(NULL, panel.first = ternary_grid()) ternary_segments(x0 = 40, y0 = 20, z0 = 40,                  x1 = 20, y1 = 40, z1 = 40)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Text to a Ternary Plot \u2014 ternary_text","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"Draws strings given vector labels coordinates given x, y z.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"","code":"ternary_text(x, y, z, ...)  # S4 method for numeric,numeric,numeric ternary_text(x, y, z, labels = seq_along(x), ...)  # S4 method for ANY,missing,missing ternary_text(x, labels = seq_along(x$x), ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"x, y, z numeric vector giving x, y z ternary coordinates set points. y z missing, attempt made interpret x suitable way (see grDevices::xyz.coords()). ... arguments passed graphics::text(). labels character vector expression specifying text written.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"ternary_text() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Text to a Ternary Plot \u2014 ternary_text","text":"","code":"## Compositional data coda <- data.frame(   X = c(20, 60, 20),   Y = c(20, 20, 60),   Z = c(60, 20, 20) )  ## Add text ternary_plot(NULL, panel.first = ternary_grid()) ternary_text(coda, labels = c(\"A\", \"B\", \"C\"), col = \"red\", cex = 2)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":null,"dir":"Reference","previous_headings":"","what":"Ternary Plot Annotation \u2014 ternary_title","title":"Ternary Plot Annotation \u2014 ternary_title","text":"Ternary Plot Annotation","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ternary Plot Annotation \u2014 ternary_title","text":"","code":"ternary_title(   main = NULL,   sub = NULL,   xlab = NULL,   ylab = NULL,   zlab = NULL,   line = NA,   outer = FALSE,   ... )","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ternary Plot Annotation \u2014 ternary_title","text":"main character string specifying main title (top). sub character string specifying sub-title (bottom). xlab, ylab, zlab character string giving label x, y z axes. line Specifying value line overrides default placement labels, places many lines outwards plot edge. outer logical scalar: titles placed outer margins plot? ... graphical parameters may also passed arguments function, particularly, font.main, cex.main, col.main font.sub, cex.sub, col.sub title annotation; font.lab, cex.lab col.lab axis label.","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ternary Plot Annotation \u2014 ternary_title","text":"ternary_title() called side-effects.","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ternary Plot Annotation \u2014 ternary_title","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/ternary_title.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ternary Plot Annotation \u2014 ternary_title","text":"","code":"## Add title ternary_plot(NULL, main = \"Main title\", sub = \"Subtitle\",              xlab = \"A\", ylab = \"B\", zlab = \"C\")   ternary_plot(NULL, ann = FALSE) ternary_title(main = \"Main title\", sub = \"Subtitle\",               xlab = \"A\", ylab = \"B\", zlab = \"C\")","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_phase_cas.html","id":null,"dir":"Reference","previous_headings":"","what":"Ceramic Phase Diagram \u2014 triangle_phase_cas","title":"Ceramic Phase Diagram \u2014 triangle_phase_cas","text":"Ceramic Phase Diagram","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_phase_cas.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ceramic Phase Diagram \u2014 triangle_phase_cas","text":"","code":"triangle_phase_cas(labels = TRUE, symbol = FALSE, mol = FALSE, ...)  triangle_phase_ceramic(labels = TRUE, symbol = FALSE, mol = FALSE, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_phase_cas.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ceramic Phase Diagram \u2014 triangle_phase_cas","text":"labels logical scalar: labels displayed? symbol logical scalar: symbol used instead full labels? used labels TRUE. mol logical scalar: molarity used instead molar mass? ... arguments passed graphics::polygon().","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_phase_cas.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ceramic Phase Diagram \u2014 triangle_phase_cas","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_phase_cas.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ceramic Phase Diagram \u2014 triangle_phase_cas","text":"","code":"## Ceramic phase diagram ternary_plot(NULL, xlab = \"CaO\", ylab = \"Al2O3\", zlab = \"SiO2\") triangle_phase_ceramic(symbol = TRUE, mol = TRUE, pch = 16)   ternary_plot(NULL, xlab = \"CaO\", ylab = \"Al2O3\", zlab = \"SiO2\") triangle_phase_ceramic(symbol = TRUE, mol = FALSE, pch = 16)   ## CAS diagram ternary_plot(NULL, axes = FALSE, ann = FALSE, frame.plot = TRUE) triangle_phase_cas(mol = FALSE, pch = 16)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_soil.html","id":null,"dir":"Reference","previous_headings":"","what":"Soil Texture Triangle \u2014 triangle_soil","title":"Soil Texture Triangle \u2014 triangle_soil","text":"Soil Texture Triangle","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_soil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Soil Texture Triangle \u2014 triangle_soil","text":"","code":"triangle_soil_hypres(labels = TRUE, symbol = FALSE, ...)  triangle_soil_folk(labels = TRUE, symbol = FALSE, ...)  triangle_soil_shepard(labels = TRUE, symbol = FALSE, ...)  triangle_soil_usda(labels = TRUE, symbol = FALSE, ...)","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_soil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Soil Texture Triangle \u2014 triangle_soil","text":"labels logical scalar: labels displayed? symbol logical scalar: symbol used instead full labels? used labels TRUE. ... arguments passed graphics::polygon().","code":"","package":"isopleuros"},{"path":[],"package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_soil.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Soil Texture Triangle \u2014 triangle_soil","text":"N. Frerebeau","code":"","package":"isopleuros"},{"path":"https://packages.tesselle.org/isopleuros/reference/triangle_soil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Soil Texture Triangle \u2014 triangle_soil","text":"","code":"## HYPRES soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_hypres()   ## USDA (1951) soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_usda(symbol = TRUE)   ## Folk (1954) soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_folk(symbol = TRUE)   ## Shepard (1954) soil texture ternary_plot(NULL, xlab = \"sand\", ylab = \"silt\", zlab = \"clay\") triangle_soil_shepard()","package":"isopleuros"},{"path":[],"package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Easily Install and Load tesselle Packages","text":"tesselle suite collection packages research teaching archaeology. packages focus quantitative analysis methods developed archaeology. tesselle packages designed work seamlessly together complement general-purpose specialized statistical packages. packages can used explore analyze common data types archaeology: count data, compositional data chronological data. tesselle package designed make easy install load key packages tesselle suite single step.","code":"To cite tesselle in publications use:    Frerebeau N (2023). _tesselle: Easily Install and Load 'tesselle'   Packages_. Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.6500491 <https://doi.org/10.5281/zenodo.6500491>,   R package version 1.3.0, <https://packages.tesselle.org/tesselle/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{tesselle: Easily Install and Load 'tesselle' Packages}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.3.0},     url = {https://packages.tesselle.org/tesselle/},     doi = {10.5281/zenodo.6500491},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Easily Install and Load tesselle Packages","text":"can install released version tesselle CRAN : development version GitHub :","code":"install.packages(\"tesselle\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/tesselle\")","package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Easily Install and Load tesselle Packages","text":"library(tesselle) load core packages: tabula: analysis visualization archaeological count data; kairos: analysis chronological patterns archaeological count data; khroma: colour schemes scientific data visualization.","code":"library(tesselle) #> --- Attaching packages -------------------------------------------- tesselle --- #> * tabula    3.0.0 #> * kairos    2.0.0 #> * khroma    1.11.0","package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Easily Install and Load tesselle Packages","text":"Please note tesselle project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"tesselle"},{"path":[],"package":"tesselle"},{"path":[],"package":"tesselle"},{"path":[],"package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/reference/tesselle-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tesselle: Easily Install and Load 'tesselle' Packages \u2014 tesselle-package","title":"tesselle: Easily Install and Load 'tesselle' Packages \u2014 tesselle-package","text":"Easy install load key packages 'tesselle' suite single step. 'tesselle' suite collection packages research teaching archaeology. packages focus quantitative analysis methods developed archaeology. 'tesselle' packages designed work seamlessly together complement general-purpose specialized statistical packages. packages can used explore analyze common data types archaeology: count data, compositional data chronological data. Learn 'tesselle' https://www.tesselle.org.","code":"","package":"tesselle"},{"path":[],"package":"tesselle"},{"path":[],"package":"tesselle"},{"path":"https://packages.tesselle.org/tesselle/reference/tesselle-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tesselle: Easily Install and Load 'tesselle' Packages \u2014 tesselle-package","text":"Full list authors contributors (alphabetic order): Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne 33607 Pessac cedex France","code":"","package":"tesselle"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Datasets for Teaching Archaeology and Paleontology","text":"Datasets teaching quantitative approaches modeling archaeology paleontology. package provides several types data related broad topics (cultural evolution, radiocarbon dating, paleoenvironments, etc.), can used illustrate statistical methods classroom (multivariate data analysis, compositional data analysis, diversity measurement, etc.). package teaching purposes : datasets may outdated. using folio, must cite relevant publications dataset (see documentation).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Datasets for Teaching Archaeology and Paleontology","text":"can install released version folio CRAN : development version GitHub :","code":"install.packages(\"folio\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/folio\")","package":"folio"},{"path":"https://packages.tesselle.org/folio/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Datasets for Teaching Archaeology and Paleontology","text":"","code":"## Arnold and Libby's Curve of Knowns ## Replicate fig. 1 from Arnold and Libby (1949) data(\"arnold1949\")  fit <- lm(activity_found ~ age_expected, data = arnold1949) summary(fit) #>  #> Call: #> lm(formula = activity_found ~ age_expected, data = arnold1949) #>  #> Residuals: #>        1        2        3        4        5        6  #>  0.31341 -0.27618 -0.04573 -0.19344  0.09780  0.10414  #>  #> Coefficients: #>                Estimate Std. Error t value Pr(>|t|)     #> (Intercept)  12.2665215  0.2903726   42.24 1.88e-06 *** #> age_expected -0.0011588  0.0000938  -12.35 0.000247 *** #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Residual standard error: 0.2421 on 4 degrees of freedom #> Multiple R-squared:  0.9745, Adjusted R-squared:  0.9681  #> F-statistic: 152.6 on 1 and 4 DF,  p-value: 0.0002467  plot(   x = arnold1949$age_expected,   y = arnold1949$activity_found,   type = \"p\",   pch = 16,   xlab = \"Historical age (years)\",   ylab = \"Specific activity (C/m/g)\",   main = \"Samples of known ages\" ) segments(   x0 = arnold1949$age_expected,   y0 = arnold1949$activity_found - arnold1949$activity_found_error,   x1 = arnold1949$age_expected,   y1 = arnold1949$activity_found + arnold1949$activity_found_error ) segments(   x0 = arnold1949$age_expected - arnold1949$age_expected_error,   y0 = arnold1949$activity_found,   x1 = arnold1949$age_expected + arnold1949$age_expected_error,   y1 = arnold1949$activity_found ) abline(fit, col = \"red\")","package":"folio"},{"path":"https://packages.tesselle.org/folio/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Datasets for Teaching Archaeology and Paleontology","text":"Please note folio project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":[],"package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/arnold1949.html","id":null,"dir":"Reference","previous_headings":"","what":"Arnold and Libby's Curve of Knowns \u2014 arnold1949","title":"Arnold and Libby's Curve of Knowns \u2014 arnold1949","text":"\"agreement prediction observation seen satisfactory.\"","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/arnold1949.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arnold and Libby's Curve of Knowns \u2014 arnold1949","text":"","code":"arnold1949","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/arnold1949.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Arnold and Libby's Curve of Knowns \u2014 arnold1949","text":"data.frame 6 observations 8 variables: sample Sample name. age_expected Expected age (year BP). age_expected_error Error age_expected (year BP). age_found Measured age (year BP). age_found_error Error age_found (year BP). activity_expected Expected specific activity (cpm/g carbon). activity_found Measured specific activity (cpm/g carbon). activity_found_error Error activity_found (cpm/g carbon).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/arnold1949.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Arnold and Libby's Curve of Knowns \u2014 arnold1949","text":"Arnold, J. R. Libby, W. F. (1949). Age Determinations Radiocarbon Content: Checks Samples Known Age. Science, 110(2869), 678-80. doi:10.1126/science.110.2869.678","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/birds.html","id":null,"dir":"Reference","previous_headings":"","what":"European Birds \u2014 birds","title":"European Birds \u2014 birds","text":"dataset birds species abundance remote European woodlands.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/birds.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"European Birds \u2014 birds","text":"","code":"birds","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/birds.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"European Birds \u2014 birds","text":"data.frame 35 rows (species) 3 variables (woodlands).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/birds.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"European Birds \u2014 birds","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/boves.html","id":null,"dir":"Reference","previous_headings":"","what":"Boves Ceramics \u2014 boves","title":"Boves Ceramics \u2014 boves","text":"dataset containing ceramic counts castle site Boves (Somme, France). data grouped eight periods ranging 10th 18th century thirteen ceramic types.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/boves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boves Ceramics \u2014 boves","text":"","code":"boves","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/boves.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Boves Ceramics \u2014 boves","text":"data.frame 8 rows 13 variables (ceramic types):  IIa  IIb  IIc  IIIa  IIIb  IIIc  IVa  IVb  Va  Vb  VI  VII","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/boves.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Boves Ceramics \u2014 boves","text":"Racinet P. (2002). Le site castral et prioral de Boves du Xe au XVIIe si\u00e8cle. Bilan des recherches 1996-2000. Revue arch\u00e9ologique de Picardie. Num\u00e9ro sp\u00e9cial 20, 123 p.","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/chevelon.html","id":null,"dir":"Reference","previous_headings":"","what":"Chevelon Ground Stone \u2014 chevelon","title":"Chevelon Ground Stone \u2014 chevelon","text":"dataset ground stone artifact counts Cholla project (USA).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/chevelon.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chevelon Ground Stone \u2014 chevelon","text":"","code":"chevelon","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/chevelon.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Chevelon Ground Stone \u2014 chevelon","text":"data.frame 12 rows 10 variables (ground stone types): BMe Basin metate. SMe Slab metate. TMe Trough metate. IMe Indeterminate metate. UMa Unifacial mano. BMa Bifacial mano. MUHa Modified unifacial handstone. MBHa Modified bifacial handstone. UUHa Unmodified unifacial handstone. UBHa Unmodified bifacial handstone.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/chevelon.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Chevelon Ground Stone \u2014 chevelon","text":"Reid, J. J. (ed.) (1982). Cholla Project Archaeology. Vol. 2. Archaeological Series 161. Tucson: University Arizona. doi:10.6067/XCV8435710","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/compiegne.html","id":null,"dir":"Reference","previous_headings":"","what":"Compi\u00e8gne Ceramics \u2014 compiegne","title":"Compi\u00e8gne Ceramics \u2014 compiegne","text":"dataset containing ceramic counts Place des Hallettes Compi\u00e8gne (Oise, France). data grouped five periods century, ranging 9th 14th century, sixteen ceramic types.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/compiegne.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compi\u00e8gne Ceramics \u2014 compiegne","text":"","code":"compiegne","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/compiegne.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Compi\u00e8gne Ceramics \u2014 compiegne","text":"data.frame 5 rows (chronological periods, numbered oldest recent 1 5) 16 variables (ceramic types): Red white ceramics fine sized inclusions. B Red white ceramics medium sized inclusions. C Dark ceramics fine sized inclusions. D Dark ceramics medium sized inclusions. E Ceramics close groups B D, similarities group F. F Black, red beige ceramics coarse inclusions. G Red polished ceramics fine medium sized inclusions. H Black polished ceramics fine sized inclusions. Black polished ceramics medium sized inclusions. J Polished painted ceramics fine medium sized inclusions. K Painted ceramics, similar group . L Painted ceramics, similar group B. M Painted ceramics coarse inclusions. N Glazed ceramics. O Stamped ceramics. P Coated ceramics.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/compiegne.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Compi\u00e8gne Ceramics \u2014 compiegne","text":"Lacroix, M. C. (1997). La c\u00e9ramique m\u00e9di\u00e9vale du site des Hallettes \u00e0 Compi\u00e8gne (Oise). Revue arch\u00e9ologique de Picardie. Num\u00e9ro sp\u00e9cial, 13(1), 135-168. doi:10.3406/pica.1997.1945","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":null,"dir":"Reference","previous_headings":"","what":"EPICA Dome C \u2014 epica2008","title":"EPICA Dome C \u2014 epica2008","text":"EPICA Dome C 800-ka composite \\(CO_2\\) data.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"EPICA Dome C \u2014 epica2008","text":"","code":"epica2008","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"EPICA Dome C \u2014 epica2008","text":"data.frame 2 variables: age Year BP. CO2 \\(CO_2\\) (ppmv).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"EPICA Dome C \u2014 epica2008","text":"https://www.ncei.noaa.gov/access/paleo-search/study/6091","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"EPICA Dome C \u2014 epica2008","text":"L\u00fcthi, D., Le Floch, M., Bereiter, B., Blunier, T., Barnola, J.-M., Siegenthaler, U., Raynaud, D., Jouzel, J., Fischer, H., Kawamura, K. Stocker, T. F. (2008). High-resolution carbon dioxide concentration record 650,000-800,000 years present. Nature, 453, 379-382. doi:10.1038/nature06949 Monnin, E., Indermuhle, ., Dallenbach, ., Fluckiger, J., Stauffer, B., Stocker, T. F., Raynaud, D. Barnola, J.-M. (2001). Atmospheric CO2 concentrations last glacial termination. Science, 291, 112-114. doi:10.1126/science.291.5501.112 Petit, J. R., Jouzel, J., Raynaud, D., Barkov, N. ., Barnola, J.-M., Basile, ., Benders, M., Chappellaz, J., Davis, M., Delayque, G., Delmotte, M., Kotlyakov, V. M., Legrand, M., Lipenkov, V. Y., Lorius, C., Pepin, L., Ritz, C., Saltzman, E. Stievenard, M. (1999). Climate atmospheric history past 420,000 years Vostok ice core, Antarctica. Nature, 399, 429-436. doi:10.1038/20859 Siegenthaler, U., Stocker, T. F., Monnin, E., Luthi, D., Schwander, J., Stauffer, B., Raynaud, D., Barnola, J.-M., Fischer, H., Masson-Delmotte, V., Jouzel, J. (2005). Stable Carbon Cycle-Climate Relationship Late Pleistocene. Science, 310, 1313-1317. doi:10.1126/science.1120130","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/epica2008.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"EPICA Dome C \u2014 epica2008","text":"","code":"plot(   x = epica2008$age / 1000,   y = epica2008$CO2,   type = \"l\",   xlim = c(800, 0),   xlab = \"kilo year BP\",   ylab = expression(\"CO\"[2]~\"(ppmv)\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/folio-package.html","id":null,"dir":"Reference","previous_headings":"","what":"folio: Datasets for Teaching Archaeology and Paleontology \u2014 folio-package","title":"folio: Datasets for Teaching Archaeology and Paleontology \u2014 folio-package","text":"Datasets teaching quantitative approaches modeling archaeology paleontology. package provides several types data related broad topics (cultural evolution, radiocarbon dating, paleoenvironments, etc.), can used illustrate statistical methods classroom (multivariate data analysis, compositional data analysis, diversity measurement, etc.).","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/folio-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"folio: Datasets for Teaching Archaeology and Paleontology \u2014 folio-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne F-33607 Pessac cedex France","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal09.html","id":null,"dir":"Reference","previous_headings":"","what":"IntCal09 \u2014 intcal09","title":"IntCal09 \u2014 intcal09","text":"IntCal series radiocarbon calibration curves.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal09.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IntCal09 \u2014 intcal09","text":"","code":"intcal09","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal09.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"IntCal09 \u2014 intcal09","text":"data.frame 5 variables: calBP Calendar (calibrated) age (year BP). age Radiocarbon age (year BP). error Radiocarbon error (year BP). delta Isotopic ratio \\(\\Delta^{14}C\\) (per mil). sigma Error delta (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal09.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"IntCal09 \u2014 intcal09","text":"Reimer, P. J., Baillie, M. G. L., Bard, E., Bayliss, ., Beck, J. W., Blackwell, P. G., Bronk Ramsey, C. et al. (2009). IntCal09 Marine09 Radiocarbon age Calibration Curves, 0-50,000 Years Cal BP. Radiocarbon, 51(4): 1111-50. doi:10.1017/S0033822200034202 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal13.html","id":null,"dir":"Reference","previous_headings":"","what":"IntCal13 \u2014 intcal13","title":"IntCal13 \u2014 intcal13","text":"IntCal series radiocarbon calibration curves.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal13.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IntCal13 \u2014 intcal13","text":"","code":"intcal13","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal13.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"IntCal13 \u2014 intcal13","text":"data.frame 5 variables: calBP Calendar (calibrated) age (year BP). age Radiocarbon age (year BP). error Radiocarbon error (year BP). delta Isotopic ratio \\(\\Delta^{14}C\\) (per mil). sigma Error delta (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal13.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"IntCal13 \u2014 intcal13","text":"Reimer, P. J., Bard, E. Bayliss, ., Beck, J. W., Blackwell, P. G., Bronk Ramsey, C., Buck, C. E. et al. (2013). IntCal13 Marine13 Radiocarbon age Calibration Curves 0-50,000 Years cal BP. Radiocarbon, 55(4): 1869-87. doi:10.2458/azu_js_rc.55.16947 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal20.html","id":null,"dir":"Reference","previous_headings":"","what":"IntCal20 \u2014 intcal20","title":"IntCal20 \u2014 intcal20","text":"IntCal series radiocarbon calibration curves.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal20.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IntCal20 \u2014 intcal20","text":"","code":"intcal20","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal20.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"IntCal20 \u2014 intcal20","text":"data.frame 5 variables: calBP Calendar (calibrated) age (year BP). age Radiocarbon age (year BP). error Radiocarbon error (year BP). delta Isotopic ratio \\(\\Delta^{14}C\\) (per mil). sigma Error delta (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/intcal20.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"IntCal20 \u2014 intcal20","text":"Reimer, P. J., Austin, W. E. N., Bard, E., Bayliss, ., Blackwell, P. G., Bronk Ramsey, C., Butzin, M. et al. (2020). IntCal20 Northern Hemisphere Radiocarbon \u00e2ge Calibration Curve (0-55 Cal KBP). Radiocarbon, 62(4), 725-757. doi:10.1017/RDC.2020.41 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/kommos.html","id":null,"dir":"Reference","previous_headings":"","what":"Transport Jars from Kommos (Crete). \u2014 kommos","title":"Transport Jars from Kommos (Crete). \u2014 kommos","text":"Chemical analysis (neutron activation analysis) 88 Late Bronze Age transport jars found excavations Kommos, Crete.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/kommos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transport Jars from Kommos (Crete). \u2014 kommos","text":"","code":"kommos","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/kommos.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transport Jars from Kommos (Crete). \u2014 kommos","text":"data.frame 22 variables (chemical elements): type CJ: Canaanite jar; EJ: Egyptian jar; TSJ: transport stirrup jar; SNA: short-necked amphora. date Chronology (period). Sm Sm content (ppm). Lu Lu content (ppm). U U content (ppm). Yb Yb content (ppm). content (ppm). Sb Sb content (ppm). Ca Ca content (ppm). Na Na content (ppm). La La content (ppm). Ce Ce content (ppm). Th Th content (ppm). Cr Cr content (ppm). Hf Hf content (ppm). Cs Cs content (ppm). Sc Sc content (ppm). Rb Rb content (ppm). Fe Fe content (ppm). Ta Ta content (ppm). Co Co content (ppm). Eu Eu content (ppm).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/kommos.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Transport Jars from Kommos (Crete). \u2014 kommos","text":"Day, P. M., Quinn, P. S., Rutter, J. B. & Kilikoglou, V. (2011). World Goods: Transport Jars Commodity Exchange Late Bronze Age Harbor Kommos, Crete. Hesperia, 80, 511-558. doi:10.2972/hesperia.80.4.0511","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":null,"dir":"Reference","previous_headings":"","what":"Law Dome Ice Core \u2014 law2006","title":"Law Dome Ice Core \u2014 law2006","text":"Law Dome Ice Core 2000-year \\(CH_4\\), \\(CO_2\\) \\(N_{2}O\\) data.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Law Dome Ice Core \u2014 law2006","text":"","code":"law2006","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Law Dome Ice Core \u2014 law2006","text":"data.frame 2004 observations 8 variables: year Year AD. NOAA04 NOAA04 \\(CH_4\\) scale. CH4_spl \\(CH_4\\) spline (ppb). CH4_grw \\(CH_4\\) growth Rate (ppb/yr). CO2_spl \\(CO_2\\) spline (ppb). CO2_grw \\(CO_2\\) growth Rate (ppb/yr). N2O_spl \\(N_{2}O\\) spline (ppb). N2O_grw \\(N_{2}O\\) growth Rate (ppb/yr).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Law Dome Ice Core \u2014 law2006","text":"https://www.ncei.noaa.gov/access/paleo-search/study/9959","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Law Dome Ice Core \u2014 law2006","text":"MacFarling Meure, C., Etheridge, D., Trudinger, C., Steele, P., Langenfelds, R., van Ommen, T., Smith, . Elkins, J. (2006). Law Dome CO2, CH4 N2O Ice Core Records Extended 2000 years BP. Geophysical Research Letters, 33(14), L14810. doi:10.1029/2006GL026152 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Law Dome Ice Core \u2014 law2006","text":"","code":"plot(   x = law2006$year,   y = law2006$CO2_spl,   type = \"l\",   xlab = \"Year AD\",   ylab = expression(\"CO\"[2]~\"(ppm)\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"global Pliocene-Pleistocene benthic \\(\\delta^{18}O\\) stack.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"","code":"lisiecki2005","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"data.frame 3 variables: age Calendar age (kilo year cal BP). delta Benthic \\(\\delta^{18}O\\) (per mil). error Standard error (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"https://www.ncei.noaa.gov/access/paleo-search/study/5847","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"LR04 stack spans 5.3 Myr average 57 globally distributed benthic \\(\\delta^{18}O\\) records (measure global ice volume deep ocean temperature) collected scientific literature.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"Lisiecki, L. E. Raymo, M. E. (2005). Pliocene-Pleistocene stack 57 globally distributed benthic d18O records. Paleoceanography, 20, PA1003. doi:10.1029/2004PA001071","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"","code":"plot(   x = lisiecki2005$age,   y = lisiecki2005$delta,   type = \"l\",   xlim = c(500, 0),   xlab = \"kilo year BP\",   ylab = expression(delta^{18}*\"O\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":null,"dir":"Reference","previous_headings":"","what":"Medieval Ceramics from the Loire Basin \u2014 loire","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"dataset containing ceramic counts Loire Basin (France).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"","code":"loire","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"data.frame 332 rows (archaeological assemblages) 331 variables. first five columns provide background information, next columns give MNI ceramic types: site Name archaeological site. city City. area Geographical area. lower Lower bound temporal range. upper Upper bound temporal range.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"https://ceramedvaldeloire.huma-num.fr/editions/suppl79racf2022/accueil","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"Husi, P. (dir.). (2022). La c\u00e9ramique m\u00e9di\u00e9vale et moderne du bassin de la Loire moyenne, chrono-typologie et transformation des aires culturelles dans la longue dur\u00e9e (6e-19e s.). Suppl\u00e9ments \u00e0 la revue Arch\u00e9ologique du Centre de la France, 79.","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/merzbach.html","id":null,"dir":"Reference","previous_headings":"","what":"Merzbach Ceramics \u2014 merzbach","title":"Merzbach Ceramics \u2014 merzbach","text":"dataset containing ceramic counts Merzbach assemblage (Germany). data grouped eight phases.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/merzbach.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merzbach Ceramics \u2014 merzbach","text":"","code":"merzbach","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/merzbach.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Merzbach Ceramics \u2014 merzbach","text":"data.frame 8 rows (phases, numbered VII XIV) 36 variables (pottery motifs).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/merzbach.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Merzbach Ceramics \u2014 merzbach","text":"Crema, E. R. (2016). Sample codes data \"Revealing patterns cultural transmission frequency data: equilibrium non-equilibrium assumptions\". Zenodo, v1.0. doi:10.5281/zenodo.187558 .","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/merzbach.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Merzbach Ceramics \u2014 merzbach","text":"Crema, E. R., Kandler, . & Shennan, S. (2016). Revealing Patterns Cultural Transmission Frequency Data: Equilibrium Non-Equilibrium Assumptions. Scientific Reports, 6(1). doi:10.1038/srep39122 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/mississippi.html","id":null,"dir":"Reference","previous_headings":"","what":"Mississippi Ceramics \u2014 mississippi","title":"Mississippi Ceramics \u2014 mississippi","text":"dataset containing ceramic counts Mississippi region.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/mississippi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mississippi Ceramics \u2014 mississippi","text":"","code":"mississippi","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/mississippi.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mississippi Ceramics \u2014 mississippi","text":"data.frame 20 rows 10 variables (ceramic types): ParkinPunctate  BartonKentMPI  Painted  FortuneNoded  RanchIncised  WallsEngraved  WallaceIncised  RhodesIncised  VernonPaulApplique  HullEngraved","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/mississippi.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mississippi Ceramics \u2014 mississippi","text":"Lipo, C. P., Madsen, M. E. & Dunnell, R. C. (2015). Theoretically-Sufficient Computationally-Practical Technique Deterministic Frequency Seriation. PLOS ONE, 10(4), e0124942. doi:10.1371/journal.pone.0124942 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/munsingen.html","id":null,"dir":"Reference","previous_headings":"","what":"M\u00fcnsingen Cemetery \u2014 munsingen","title":"M\u00fcnsingen Cemetery \u2014 munsingen","text":"dataset data set artifact presence/absence Celtic M\u00fcnsingen-Rain cemetery (Switzerland).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/munsingen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"M\u00fcnsingen Cemetery \u2014 munsingen","text":"","code":"munsingen","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/munsingen.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"M\u00fcnsingen Cemetery \u2014 munsingen","text":"data.frame 59 rows (graves) 70 variables (artifacts).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/munsingen.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"M\u00fcnsingen Cemetery \u2014 munsingen","text":"Hodson, F. R. (1968). La Tene Cemetery M\u00fcnsingen-Rain. St\u00e4mpfli, Bern. Kendall, D. G. (1971). Seriation abundance matrices. Hodson, F. R., Kendall, D. G. Tautu, P. (eds), Mathematics Archaeological Historical Sciences. Edinburgh University Press, Edinburgh, 215-232.","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/munsingen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"M\u00fcnsingen Cemetery \u2014 munsingen","text":"","code":"heatmap(   x = as.matrix(munsingen),   Rowv = NA,   Colv = NA,   scale = \"none\",   col = c(\"white\", \"black\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":null,"dir":"Reference","previous_headings":"","what":"NGRIP 50-year Average \u2014 ngrip2004","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"50-year averaged oxygen isotope data North Greenland Ice Core Project (ss09sea time scale).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"","code":"ngrip2004","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"data.frame 2 variables: age Calendar age (years 2000 AD), ss09sea time scale. delta \\(\\delta^{18}O\\) (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"https://www.ncei.noaa.gov/access/paleo-search/study/2481","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"North Greenland Ice Core Project members (2004). High-resolution record Northern Hemisphere climate extending last interglacial period. Nature, 431(7005), 147-151. doi:10.1038/nature02805","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2004.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NGRIP 50-year Average \u2014 ngrip2004","text":"","code":"plot(   x = ngrip2004$age / 1000,   y = ngrip2004$delta,   type = \"l\",   xlim = c(120, 0),   xlab = \"ss09sea (ka b2k)\",   ylab = expression(delta^{18}*\"O\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":null,"dir":"Reference","previous_headings":"","what":"NGRIP 20-year Average \u2014 ngrip2010","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"20-year averaged oxygen isotope data North Greenland Ice Core Project (GICC05 time scale).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"","code":"ngrip2010","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"data.frame 4 variables: age Calendar age (years 2000 AD), GICC05 time scale (GICC05modelext going beyond 60 ka b2k). depth (meters). delta \\(\\delta^{18}O\\) (per mil). MCE Maximum counting error (years).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"https://www.iceandclimate.nbi.ku.dk/data/","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"Use labels GICC05 (GICC05modelext going beyond 60 ka b2k) graphs.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"Vinther, B. M., Clausen, H. B., Johnsen, S. J., Rasmussen, S. O., Andersen, K. K., Buchardt, S. L., Dahl-Jensen, D., Seierstad, . K., Siggaard-Andersen, M.-L., Steffensen, J. P., Svensson, . M., Olsen, J. & Heinemeier, J. (2006). synchronized dating three Greenland ice cores throughout Holocene. Journal Geophysical Research, 111, D13102. doi:10.1029/2005JD006921 . Rasmussen, S. O., Andersen, K. K., Svensson, . M., Steffensen, J. P., Vinther, B. M., Clausen, H. B., Siggaard-Andersen, M.-L., Johnsen, S. J., Larsen, L. B., Dahl-Jensen, D., Bigler, M., R\u00f6thlisberger, R., Fischer, H., Goto-Azuma, K., Hansson, M. E. & Ruth, U. (2006). new Greenland ice core chronology last glacial termination. Journal Geophysical Research, 111, D06102. doi:10.1029/2005JD006079 . Andersen, K. K., Svensson, ., Johnsen, S. J., Rasmussen, S. O., Bigler, M., R\u00f6thlisberger, R., Ruth, U., Siggaard-Andersen, M.-L., Steffensen, J. P., Dahl-Jensen, D., Vinther, B. M. & Clausen, H.B. (2005). Greenland Ice Core Chronology 2005, 15-42 ka. Part 1: Constructing time scale. Quaternary Science Reviews, 25(23-24):3246-3257. doi:10.1016/j.quascirev.2006.08.002 . Svensson, ., Andersen, K. K., Bigler, M., Clausen, H. B., Dahl-Jensen, D., Davies, S. M., Johnsen, S. J., Muscheler, R., Rasmussen, S. O., R\u00f6thlisberger, R., Seierstad, ., Steffensen, J. P. & Vinther, B. M. (2008). 60,000 year Greenland stratigraphic ice core chronology. Climate Past, 4:47\u201357. doi:10.5194/cp-4-47-2008 . Wolff, E. W., Chappellaz, J., Blunier, T., Rasmussen, S. O. & Svensson, . (2010). Millennial-scale variability last glacial: ice core record. Quaternary Science Reviews, 29:2828-2838. doi:10.1016/j.quascirev.2009.10.013 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/ngrip2010.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NGRIP 20-year Average \u2014 ngrip2010","text":"","code":"plot(   x = ngrip2010$age / 1000,   y = ngrip2010$delta,   type = \"l\",   xlim = c(120, 0),   xlab = \"GICC05\",   ylab = expression(delta^{18}*\"O\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/nydal1996.html","id":null,"dir":"Reference","previous_headings":"","what":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","title":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","text":"Corrected \\({}^{14}C\\) measurements air samples collected five Norwegian sites 1962-1993.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/nydal1996.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","text":"","code":"nydal1996","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/nydal1996.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","text":"data.frame 5 variables: site Sampling station. start Beginning date sampling period. end Ending date sampling period. delta Isotopic ratio \\(\\Delta^{14}C\\) (per mil). sigma Error delta (per mil).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/nydal1996.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","text":"Nydal, R. L\u00f6vseth, K. (1996). Carbon-14 Measurements Atmospheric CO2 Northern Southern Hemisphere Sites, 1962-1993. ORNL/CDIAC-93; NDP-057. Washington, DC: USDOE Office Energy Research. doi:10.2172/461185","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/nydal1996.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"\\({}^{14}C\\) Measurements in Atmospheric \\(CO_2\\) \u2014 nydal1996","text":"","code":"plot(   x = nydal1996$start,   y = nydal1996$delta,   type = \"p\",   xlab = \"Date\",   ylab = expression(Delta^{14}*\"C\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":null,"dir":"Reference","previous_headings":"","what":"Mediterranean Shipwrecks \u2014 shipwrecks","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"dataset mediterranean shipwrecks.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"","code":"shipwrecks","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"data.frame 1784 rows 13 variables: name Wreck name. sea Region sea wreck discovered. country Country wreck discovered. region Region wreck discovered. depth_min Minimum depth wreck (m). depth_max Maximum depth wreck (m). depth Depth wreck (m). period Period. dating Dating. date_early Earliest date. date_late Latest date. origin Place origin. destination Place destination.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"Strauss, J. (2013). Shipwrecks Database. Version 1.0. Accessed 2022-08-13. URL: http://oxrep.classics.ox.ac.uk/databases/shipwrecks_database/","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"dataset contains typos needs normalized.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"Parker, . J. (1992). Ancient Shipwrecks Mediterranean Roman Provinces. British Archaeological Reports International Series 580. Oxford. Strauss, E. J. (2007). Roman Cargoes: Underwater Evidence Eastern Mediterranean. Doctoral thesis, University College London. URL: https://discovery.ucl.ac.uk/id/eprint/1349806.","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":null,"dir":"Reference","previous_headings":"","what":"Late Pleistocene Sea Level Stack \u2014 spratt2016","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"Late Pleistocene sea level stack based marine sediment core data (foraminiferal carbonate \\(\\delta^{18}O\\)).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"","code":"spratt2016","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"data.frame 9 variables: age_calkaBP Age (calendar kilo year BP). SeaLev_shortPC1 Sea Level (meters present day), climate reconstructions (scaled first principal component seven sea level reconstructions (0-430 ka)). SeaLev_shortPC1_err_sig Sea Level standard deviation bootstrap (meters), climate reconstructions (scaled first principal component seven sea level reconstructions (0-430 ka)). SeaLev_shortPC1_err_lo Sea Level 95% confidence interval lower bound (meters), climate reconstructions (scaled first principal component seven sea level reconstructions (0-430 ka)). SeaLev_shortPC1_err_up Sea Level 95% confidence interval upper bound (meters), climate reconstructions (scaled first principal component seven sea level reconstructions (0-430 ka)). SeaLev_longPC1 Sea Level (meters present day), climate reconstructions (scaled first principal component five sea level reconstructions (0-798 ka)). SeaLev_longPC1_err_sig Sea Level standard deviation bootstrap (meters), climate reconstructions (scaled first principal component five sea level reconstructions (0-798 ka)). SeaLev_longPC1_err_lo Sea Level 95% confidence interval lower bound (meters), climate reconstructions (scaled first principal component five sea level reconstructions (0-798 ka)). SeaLev_longPC1_err_up Sea Level 95% confidence interval upper bound (meters), climate reconstructions (scaled first principal component five sea level reconstructions (0-798 ka)).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"https://www.ncei.noaa.gov/access/paleo-search/study/19982","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"Spratt, R. M. Lisiecki, L. E. (2016). Late Pleistocene sea level stack. Climate Past, 12, 1079-1092. doi:10.5194/cp-12-1079-2016","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"","code":"plot(   x = spratt2016$age_calkaBP,   y = spratt2016$SeaLev_longPC1,   type = \"l\",   xlim = c(500, 0),   xlab = \"kilo year BP\",   ylab = \"Sea level (meters above present)\" )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":null,"dir":"Reference","previous_headings":"","what":"Chronostratigraphic Chart \u2014 stratigraphy","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"ICS international chronostratigraphic chart (v2022/2).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"","code":"stratigraphy","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"data.frame 5 variables: type Unit type (\"eon\", \"era\", \"period\", \"series\" \"stage\"). Precambrian Hadean informal units. name Unit name. age Numerical age (Ma). error Error numerical age (Ma). parent Parent unit.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"https://stratigraphy.org/ICSchart/ChronostratChart2022-02.pdf","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"Cohen, K. M., Finney, S. C., Gibbard, P. L. Fan, J.-X. (2013). ICS International Chronostratigraphic Chart. Episodes, 36(3): 199-204. doi:10.18814/epiiugs/2013/v36i3/002","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":null,"dir":"Reference","previous_headings":"","what":"French Medieval Glass Composition \u2014 verre","title":"French Medieval Glass Composition \u2014 verre","text":"Chemical analysis (electron probe X-ray micro analysis) 398 medieval glass vessels found France.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"French Medieval Glass Composition \u2014 verre","text":"","code":"verre","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"French Medieval Glass Composition \u2014 verre","text":"data.frame 17 variables: Site CNL: Cour Napol\u00e9on, Louvre; ORL: Orl\u00e9ans; POI: Poitiers; ANG: Angers; OMO: Omonville, Seine Maritime; ROU: Rouen; MEA: Meaux; CHL: Ch\u00e2lons-sur-Marne; PAI: Pairu (Argonne, Ardennes); BER: Bercettes (Argonne, Ardennes); BIN: Binois (Argonne, Ardennes); CHE: Chevrie (Argonne, Ardennes); MIT: Mitte (Argonne, Ardennes); MET: Metz; CHM: Chambaran. Sample Sample code. Type Typology. Age Century. Periode : 9th-12th century; II: 13th-first half 15th century; III: end 15th end 16th century; IV: end 16th end 17th century. Tint B: blue; CL: colourless; CLg colourless (greyish tint); PB: pale blue; PGE: pale greenish; PGE-B: pale green-blue blue-green; PGY-B: pale grey-blue; R: opaque red; W: opaque white; *av: added aventurine spots; *bl: added thread blue blue spots; *r: added thread opaque red opaque red spots; *w: added thread opaque white. Na2O Na2O content (percent). CaO CaO content (percent). K2O K2O content (percent). MgO MgO content (percent). P2O5 P2O5 content (percent). SiO2 SiO2 content (percent). Al2O3 Al2O3 content (percent). FeO FeO content (percent). MnO MnO content (percent). Cl Cl content (percent). Reference Site reference.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"French Medieval Glass Composition \u2014 verre","text":"Barrera J., Velde B. (1989). study french medieval glass composition. Arch\u00e9ologie m\u00e9di\u00e9vale, 19, 81-130. doi:10.3406/arcme.1989.953 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"French Medieval Glass Composition \u2014 verre","text":"","code":"plot(   x = verre$Na2O,   y = verre$CaO / (verre$CaO + verre$K2O),   type = \"p\",   xlab = expression(\"Na\"[2]*\"O (%)\"),   ylab = expression(\"CaO\"/\"(CaO\"+\"K\"[2]*\"O)\") )","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/zuni.html","id":null,"dir":"Reference","previous_headings":"","what":"Zuni Ceramics \u2014 zuni","title":"Zuni Ceramics \u2014 zuni","text":"dataset containing ceramic counts Zuni region American Southwest.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/zuni.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Zuni Ceramics \u2014 zuni","text":"","code":"zuni","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/zuni.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Zuni Ceramics \u2014 zuni","text":"data.frame 420 rows (assemblages) 18 variables (ceramic types). numbers brackets correspond date range type (AD years): LINO Lino Gray (575-875). KIAT Kiatuthlanna Black--white (850-910). RED Red Mesa Black--white (900-1030). GALL Gallup Black--white (1025-1150). ESC Escavada Black--white (1050-1150). PUBW Puerco Black--white (1050-1200). RES Reserve Black--white (1071-1115). TULA Tularosa Black--white (1175-1300). PINE Pinedale Black--white (1275-1325). PUBR Puerco Black--red (1050-1200). WING Wingate Black--red (1070-1200). WIPO Wingate Polychrome (1150-1250). SJ St. Johns Black--red/Polychrome (1200-1300). LSJ St. Johns glaze, Techado Polychrome (1275-1300). SPR Springerville Polychrome (1250-1300). PINER Pinedale Black--red/Polychrome (1275-1325). HESH Heshotauthla Polychrome (1285-1400). KWAK Kwakina Polychrome (1285-1400).","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/zuni.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Zuni Ceramics \u2014 zuni","text":"Peeples, M. ., & Schachner, G. (2012). Refining correspondence analysis-based ceramic seriation regional data sets. Journal Archaeological Science, 39(8), 2818-2827. doi:10.1016/j.jas.2012.04.040 .","code":"","package":"folio"},{"path":[],"package":"folio"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Pre-Processing XY Data from Experimental Methods","text":"alkahest lightweight, dependency-free toolbox pre-processing XY data experimental methods (.e.\u00a0signal can measured along continuous variable). provides methods baseline estimation correction, smoothing, normalization, integration peaks detection.","code":"To cite alkahest in publications use:    Frerebeau N (2023). _alkahest: Pre-Processing XY Data from   Experimental Methods_. Universit\u00e9 Bordeaux Montaigne, Pessac, France.   doi:10.5281/zenodo.7081524 <https://doi.org/10.5281/zenodo.7081524>,   R package version 1.1.1, <https://packages.tesselle.org/alkahest/>.  Une entr\u00e9e BibTeX pour les utilisateurs LaTeX est    @Manual{,     author = {Nicolas Frerebeau},     title = {{alkahest: Pre-Processing XY Data from Experimental Methods}},     year = {2023},     organization = {Universit\u00e9 Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 1.1.1},     url = {https://packages.tesselle.org/alkahest/},     doi = {10.5281/zenodo.7081524},   }  This package is a part of the tesselle project <https://www.tesselle.org>.","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Pre-Processing XY Data from Experimental Methods","text":"can install released version alkahest CRAN : development version GitHub :","code":"install.packages(\"alkahest\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/alkahest\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Pre-Processing XY Data from Experimental Methods","text":"alkahest expects input data simplest form (two-column matrix data frame, two-element list two numeric vectors).","code":"## Load the package library(alkahest) ## X-ray diffraction data(\"XRD\")  ## 4S Peak Filling baseline baseline <- baseline_peakfilling(XRD, n = 10, m = 5, by = 10, sparse = TRUE)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\") ## Correct baseline XRD <- signal_drift(XRD, lag = baseline, subtract = TRUE)  ## Find peaks peaks <- peaks_find(XRD, SNR = 3, m = 11)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(peaks, type = \"p\", pch = 16, col = \"red\") ## Simulate data set.seed(12345) x <- seq(-4, 4, length = 100) y <- dnorm(x) z <- y + rnorm(100, mean = 0, sd = 0.01) # Add some noise  ## Plot raw data plot(x, z, type = \"l\", xlab = \"\", ylab = \"\", main = \"Raw data\") lines(x, y, type = \"l\", lty = 2, col = \"red\")  ## Savitzky\u2013Golay filter smooth <- smooth_savitzky(x, z, m = 21, p = 2) plot(smooth, type = \"l\", xlab = \"\", ylab = \"\", main = \"Savitzky\u2013Golay filter\") lines(x, y, type = \"l\", lty = 2, col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Pre-Processing XY Data from Experimental Methods","text":"Please note alkahest project released Contributor Code Conduct. contributing project, agree abide terms.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":[],"package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/BEGe.html","id":null,"dir":"Reference","previous_headings":"","what":"Gamma-Ray Spectrometry \u2014 BEGe","title":"Gamma-Ray Spectrometry \u2014 BEGe","text":"Gamma-Ray Spectrometry","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/BEGe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gamma-Ray Spectrometry \u2014 BEGe","text":"","code":"BEGe","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/BEGe.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Gamma-Ray Spectrometry \u2014 BEGe","text":"data.frame 8192 rows (channels) 2 variables. energy (keV) count","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/BEGe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gamma-Ray Spectrometry \u2014 BEGe","text":"","code":"data(\"BEGe\") plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/LaBr.html","id":null,"dir":"Reference","previous_headings":"","what":"Gamma-Ray Spectrometry \u2014 LaBr","title":"Gamma-Ray Spectrometry \u2014 LaBr","text":"Gamma-Ray Spectrometry","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/LaBr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gamma-Ray Spectrometry \u2014 LaBr","text":"","code":"LaBr","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/LaBr.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Gamma-Ray Spectrometry \u2014 LaBr","text":"data.frame 1024 rows (channels) 2 variables. energy (keV) count","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/LaBr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gamma-Ray Spectrometry \u2014 LaBr","text":"","code":"data(\"LaBr\") plot(LaBr, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/XRD.html","id":null,"dir":"Reference","previous_headings":"","what":"Powder X-ray Diffraction \u2014 XRD","title":"Powder X-ray Diffraction \u2014 XRD","text":"Powder X-ray Diffraction","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/XRD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Powder X-ray Diffraction \u2014 XRD","text":"","code":"XRD","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/XRD.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Powder X-ray Diffraction \u2014 XRD","text":"data.frame 2989 rows 2 variables. theta  count","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/XRD.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Powder X-ray Diffraction \u2014 XRD","text":"","code":"data(\"XRD\") plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/alkahest-package.html","id":null,"dir":"Reference","previous_headings":"","what":"alkahest: Pre-Processing XY Data from Experimental Methods \u2014 alkahest-package","title":"alkahest: Pre-Processing XY Data from Experimental Methods \u2014 alkahest-package","text":"lightweight, dependency-free toolbox pre-processing XY data experimental methods (.e. signal can measured along continuous variable). package provides methods baseline estimation correction, smoothing, normalization, integration peaks detection. Baseline correction methods includes polynomial fitting described Lieber Mahadevan-Jansen (2003) doi:10.1366/000370203322554518 , Rolling Ball algorithm Kneen Annegarn (1996) doi:10.1016/0168-583X(95)00908-6 , SNIP algorithm Ryan et al. (1988) doi:10.1016/0168-583X(88)90063-8 , 4S Peak Filling Liland (2015) doi:10.1016/j.mex.2015.02.009  .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/alkahest-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"alkahest: Pre-Processing XY Data from Experimental Methods \u2014 alkahest-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Arch\u00e9osciences Bordeaux (UMR 6034) Maison de l'Arch\u00e9ologie Universit\u00e9 Bordeaux Montaigne 33607 Pessac cedex France","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear Baseline Estimation \u2014 baseline_linear","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"Linear Baseline Estimation","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"","code":"baseline_linear(x, y, ...)  # S4 method for numeric,numeric baseline_linear(x, y, points = range(x))  # S4 method for ANY,missing baseline_linear(x, points = range(x))","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. points numeric vector specifying data points used fitting process (x unit).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_linear.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linear Baseline Estimation \u2014 baseline_linear","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Linear baseline baseline <- baseline_linear(XRD, points = c(25, 34))  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")   ## Correct baseline XRD$count <- XRD$count - baseline$y  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":null,"dir":"Reference","previous_headings":"","what":"4S Peak Filling \u2014 baseline_peakfilling","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"Baseline estimation iterative mean suppression.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"","code":"baseline_peakfilling(x, y, ...)  # S4 method for numeric,numeric baseline_peakfilling(x, y, n, m, by = 10, lambda = 1600, d = 2, sparse = FALSE)  # S4 method for ANY,missing baseline_peakfilling(x, n, m, by = 10, lambda = 1600, d = 2, sparse = FALSE)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. n integer value giving number iterations. m odd integer giving half window size. length-one numeric vector givging umber buckets divide x . lambda integer giving smoothing parameter. larger lambda , smoother curve (see smooth_whittaker()). d integer specifying order penalty (see smooth_whittaker()). sparse logical scalar: sparse matrices used computation (see smooth_whittaker())? TRUE, Matrix required.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"Liland, K. H. (2015). 4S Peak Filling - baseline estimation iterative mean suppression. MethodsX, 2, 135-140. doi:10.1016/j.mex.2015.02.009 .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"","code":"## X-ray diffraction data(\"XRD\")  ## 4S Peak Filling baseline baseline <- baseline_peakfilling(XRD, n = 10, m = 5, by = 10, sparse = TRUE)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Polynomial Baseline Estimation \u2014 baseline_polynomial","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"Polynomial Baseline Estimation","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"","code":"baseline_polynomial(x, y, ...)  # S4 method for numeric,numeric baseline_polynomial(x, y, d = 3, tolerance = 0.001, stop = 100)  # S4 method for ANY,missing baseline_polynomial(x, d = 3, tolerance = 0.001, stop = 100)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. d integer giving degree polynomial. Must less number unique points. tolerance numeric scalar giving tolerance difference iterations. stop integer giving stopping rule (.e. maximum number iterations).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"Lieber, C. . Mahadevan-Jansen, . (2003). Automated Method Subtraction Fluorescence Biological Raman Spectra. Applied Spectroscopy, 57(11): 1363-67. doi:10.1366/000370203322554518 .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Subset from 20 to 70 degrees XRD <- signal_select(XRD, from = 20, to = 70)  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Polynomial baseline baseline <- baseline_polynomial(XRD, d = 4, tolerance = 0.02, stop = 1000)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":null,"dir":"Reference","previous_headings":"","what":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"Rolling Ball Baseline Estimation","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"","code":"baseline_rollingball(x, y, ...)  # S4 method for numeric,numeric baseline_rollingball(x, y, m, s)  # S4 method for ANY,missing baseline_rollingball(x, m, s)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. m odd integer giving window size (.e. number adjacent points used; see window_sliding()) minimization/maximization. s odd integer giving window size (.e. number adjacent points used; see window_sliding()) smoothing.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, progressively wider/narrower windows used ends data series.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"Kneen, M. . Annegarn, H. J. (1996). Algorithm Fitting XRF, SEM PIXE X-Ray Spectra Backgrounds. Nuclear Instruments Methods Physics Research Section B: Beam Interactions Materials Atoms, 109/110: 209-213. doi:10.1016/0168-583X(95)00908-6 .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Subset from 20 to 70 degrees XRD <- signal_select(XRD, from = 20, to = 70)  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Rolling Ball baseline baseline <- baseline_rollingball(XRD, m = 201, s = 151)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":null,"dir":"Reference","previous_headings":"","what":"Rubberband Baseline Estimation \u2014 baseline_rubberband","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"Rubberband Baseline Estimation","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"","code":"baseline_rubberband(x, y, ...)  # S4 method for numeric,numeric baseline_rubberband(x, y, noise = 0, spline = TRUE, ...)  # S4 method for ANY,missing baseline_rubberband(x, noise = 0, spline = TRUE, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra arguments passed stats::smooth.spline(). noise length-one numeric vector giving noise level. used method \"rubberband\". spline logical scalar: spline interpolation support points used instead linear interpolation? used method \"rubberband\".","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"convex envelope spectrum determined baseline estimated part convex envelope lying spectrum. Note rubber band enter concave regions () spectrum.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"baseline_rubberband() slightly modified C. Beleites' hyperSpec::spc.rubberband().","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rubberband.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rubberband Baseline Estimation \u2014 baseline_rubberband","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")  ## Rubberband baseline baseline <- baseline_rubberband(BEGe)  plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":null,"dir":"Reference","previous_headings":"","what":"SNIP Baseline Estimation \u2014 baseline_snip","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"Sensitive Nonlinear Iterative Peak clipping algorithm.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"","code":"baseline_snip(x, y, ...)  # S4 method for numeric,numeric baseline_snip(x, y, LLS = FALSE, decreasing = FALSE, n = 100)  # S4 method for ANY,missing baseline_snip(x, LLS = FALSE, decreasing = FALSE, n = 100)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. LLS logical scalar: LLS operator applied x employing SNIP algorithm? used method \"SNIP\". decreasing logical scalar: decreasing clipping window used? n integer value giving number iterations.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"Morh\u00e1\u010d, M., Kliman, J., Matou\u0161ek, V., Veselsk\u00fd, M. & Turzo, . (1997). Background elimination methods multidimensional gamma-ray spectra. Nuclear Instruments Methods Physics Research Section : Accelerators, Spectrometers, Detectors Associated Equipment, 401(1), p. 113-132. doi:10.1016/S0168-9002(97)01023-1 Morh\u00e1\u010d, M. & Matou\u0161ek, V. (2008). Peak Clipping Algorithms Background Estimation Spectroscopic Data. Applied Spectroscopy, 62(1), p. 91-106. doi:10.1366/000370208783412762 Ryan, C. G., Clayton, E., Griffin, W. L., Sie, S. H. & Cousens, D. R. (1988). SNIP, statistics-sensitive background treatment quantitative analysis PIXE spectra geoscience applications. Nuclear Instruments Methods Physics Research Section B: Beam Interactions Materials Atoms, 34(3), p. 396-402. doi:10.1016/0168-583X(88)90063-8","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")  ## SNIP baseline baseline <- baseline_snip(BEGe, LLS = FALSE, decreasing = FALSE, n = 100)  plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectangle Rule \u2014 integrate_rectangle","title":"Rectangle Rule \u2014 integrate_rectangle","text":"Approximates definite integral using rectangle rule.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectangle Rule \u2014 integrate_rectangle","text":"","code":"integrate_rectangle(x, y, ...)  # S4 method for numeric,numeric integrate_rectangle(x, y, right = FALSE)  # S4 method for ANY,missing integrate_rectangle(x, right = FALSE)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectangle Rule \u2014 integrate_rectangle","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. right logical scalar: right rule used instead left rule?","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectangle Rule \u2014 integrate_rectangle","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rectangle Rule \u2014 integrate_rectangle","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_rectangle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectangle Rule \u2014 integrate_rectangle","text":"","code":"## Calculate the area under the sine curve from 0 to pi # integrate(f = function(x) x^3, lower = 0, upper = 2) x <- seq(0, 2, len = 101) y <- x^3  plot(x, y, type = \"l\")   integrate_rectangle(x, y, right = FALSE) # 3.9204 #> [1] 3.9204 integrate_rectangle(x, y, right = TRUE) # 4.0804 #> [1] 4.0804 integrate_trapezoid(x, y) # 4.0004 #> [1] 4.0004","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":null,"dir":"Reference","previous_headings":"","what":"Trapezoidal Rule \u2014 integrate_trapezoid","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"Approximates definite integral using trapezoidal rule.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"","code":"integrate_trapezoid(x, y, ...)  # S4 method for numeric,numeric integrate_trapezoid(x, y)  # S4 method for ANY,missing integrate_trapezoid(x)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/integrate_trapezoid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trapezoidal Rule \u2014 integrate_trapezoid","text":"","code":"## Calculate the area under the sine curve from 0 to pi # integrate(f = function(x) x^3, lower = 0, upper = 2) x <- seq(0, 2, len = 101) y <- x^3  plot(x, y, type = \"l\")   integrate_rectangle(x, y, right = FALSE) # 3.9204 #> [1] 3.9204 integrate_rectangle(x, y, right = TRUE) # 4.0804 #> [1] 4.0804 integrate_trapezoid(x, y) # 4.0004 #> [1] 4.0004","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":null,"dir":"Reference","previous_headings":"","what":"Strip XRD ka2 \u2014 ka2_strip_penalized","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"Strip XRD ka2","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"","code":"ka2_strip_penalized(x, y, ...)  # S4 method for numeric,numeric ka2_strip_penalized(   x,   y,   lambda,   wave = c(1.5406, 1.54443),   tau = 0.5,   nseg = 1,   progress = interactive() )  # S4 method for ANY,missing ka2_strip_penalized(   x,   lambda,   wave = c(1.5406, 1.54443),   tau = 0.5,   nseg = 1,   progress = interactive() )","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. lambda integer giving smoothing parameter. larger lambda , smoother curve. wave length-two numeric vector giving characteristic wavelengths anode material (defaults copper). tau length-one numeric vector giving ratio \\(\\alpha\\)1 \\(\\alpha\\)2 line intensities (defaults 1/2). nseg length-one numeric vector specifying number equally sized segments B-spline basis matrix computation. progress logical scalar: progress bar displayed?","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"Matrix required.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"de Rooi, J. J., van der Pers, N. M., Hendrikx, R. W. ., Delhez, R., B\u00f6ttger . J. Eilers, P. H. C. (2014). Smoothing X-ray diffraction data Ka2 elimination using penalized likelihood composite link model. Journal Applied Crystallography, 47: 852-860. doi:10.1107/S1600576714005809","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"J. J. de Rooi et al. (original R code).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"","code":"if (FALSE) { ## X-ray diffraction data(\"XRD\")  ## Subset from 20 to 40 degrees XRD <- signal_select(XRD, from = 20, to = 40)  ## Plot diffractogram plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Penalized likelihood smoothing lambda <- seq(from = 1, to = 8, length.out = 40) lambda <- 10^lambda  likelihood <- smooth_likelihood(XRD, lambda = lambda, d = 3) lines(likelihood, col = \"red\")  ## Strip ka2 ka2 <- ka2_strip_penalized(XRD, lambda = lambda, tau = 0.5, nseg = 1) lines(ka2, col = \"blue\") }","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":null,"dir":"Reference","previous_headings":"","what":"Find Peaks \u2014 peaks_find","title":"Find Peaks \u2014 peaks_find","text":"Finds local maxima sequential data.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find Peaks \u2014 peaks_find","text":"","code":"peaks_find(x, y, ...)  # S4 method for numeric,numeric peaks_find(x, y, method = \"MAD\", SNR = 2, m = NULL, ...)  # S4 method for ANY,missing peaks_find(x, method = \"MAD\", SNR = 2, m = NULL, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find Peaks \u2014 peaks_find","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra parameters passed internal methods. method character string specifying method used background noise estimation (see ). SNR integer giving signal--noise-ratio peak detection (see ). m odd integer giving window size (.e. number adjacent points used). NULL, 5% data points used half window size.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find Peaks \u2014 peaks_find","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Find Peaks \u2014 peaks_find","text":"local maximum highest one given window higher \\(SNR \\times noise\\) recognized peak. following methods available noise estimation: MAD Median Absolute Deviation. Note improve peak detection, may helpful smooth data remove baseline beforehand.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Find Peaks \u2014 peaks_find","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, progressively wider/narrower windows used ends data series. Adapted Stasia Grinberg's findPeaks function.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Find Peaks \u2014 peaks_find","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_find.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find Peaks \u2014 peaks_find","text":"","code":"## X-ray diffraction data(\"XRD\")  ## 4S Peak Filling baseline baseline <- baseline_peakfilling(XRD, n = 10, m = 5, by = 10, sparse = TRUE)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")   ## Correct baseline XRD <- signal_drift(XRD, lag = baseline, subtract = TRUE)  ## Find peaks peaks <- peaks_find(XRD, SNR = 3, m = 11)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(peaks, type = \"p\", pch = 16, col = \"red\") abline(h = attr(peaks, \"noise\"), lty = 2) # noise threshold   ## Half-Width at Half-Maximum x <- seq(-4, 4, length = 1000) y <- dnorm(x)  peaks_fwhm(x, y, center = 0) # Expected: 2 * sqrt(2 * log(2)) #> [1] 2.354354","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":null,"dir":"Reference","previous_headings":"","what":"Half-Width at Half-Maximum \u2014 peaks_fwhm","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"Estimates Half-Width Half-Maximum (FWHM) given peak.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"","code":"peaks_fwhm(x, y, ...)  # S4 method for numeric,numeric peaks_fwhm(x, y, center)  # S4 method for ANY,missing peaks_fwhm(x, center)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. center numeric value giving peak position x units.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"numeric value.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"tries get smallest possible estimate.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/peaks_fwhm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Half-Width at Half-Maximum \u2014 peaks_fwhm","text":"","code":"## X-ray diffraction data(\"XRD\")  ## 4S Peak Filling baseline baseline <- baseline_peakfilling(XRD, n = 10, m = 5, by = 10, sparse = TRUE)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(baseline, type = \"l\", col = \"red\")   ## Correct baseline XRD <- signal_drift(XRD, lag = baseline, subtract = TRUE)  ## Find peaks peaks <- peaks_find(XRD, SNR = 3, m = 11)  plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\") lines(peaks, type = \"p\", pch = 16, col = \"red\") abline(h = attr(peaks, \"noise\"), lty = 2) # noise threshold   ## Half-Width at Half-Maximum x <- seq(-4, 4, length = 1000) y <- dnorm(x)  peaks_fwhm(x, y, center = 0) # Expected: 2 * sqrt(2 * log(2)) #> [1] 2.354354","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_negative.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace Negative Values \u2014 replace_negative","title":"Replace Negative Values \u2014 replace_negative","text":"Replace Negative Values","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_negative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace Negative Values \u2014 replace_negative","text":"","code":"replace_negative(x, y, ...)  # S4 method for numeric,numeric replace_negative(x, y, value = 0)  # S4 method for ANY,missing replace_negative(x, value = 0)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_negative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace Negative Values \u2014 replace_negative","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra parameters passed threshold. value numeric value replace negative values.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_negative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace Negative Values \u2014 replace_negative","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_negative.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Replace Negative Values \u2014 replace_negative","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace Values Below a Given Threshold \u2014 replace_threshold","title":"Replace Values Below a Given Threshold \u2014 replace_threshold","text":"Replace Values Given Threshold","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace Values Below a Given Threshold \u2014 replace_threshold","text":"","code":"replace_threshold(x, y, threshold, ...)  # S4 method for numeric,numeric,`function` replace_threshold(x, y, threshold, value = 0, ...)  # S4 method for ANY,missing,`function` replace_threshold(x, threshold, value = 0, ...)  # S4 method for numeric,numeric,numeric replace_threshold(x, y, threshold, value = 0, ...)  # S4 method for ANY,missing,numeric replace_threshold(x, threshold, value = 0, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace Values Below a Given Threshold \u2014 replace_threshold","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). threshold numeric value function takes numeric vector argument returns single numeric value. ... Extra parameters passed threshold. value numeric value replace values threshold.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace Values Below a Given Threshold \u2014 replace_threshold","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/replace_threshold.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Replace Values Below a Given Threshold \u2014 replace_threshold","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":null,"dir":"Reference","previous_headings":"","what":"Bin \u2014 resample_bin","title":"Bin \u2014 resample_bin","text":"Averages x values applies function corresponding y values.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bin \u2014 resample_bin","text":"","code":"resample_bin(x, y, ...)  # S4 method for numeric,numeric resample_bin(x, y, by, f = mean, ...)  # S4 method for ANY,missing resample_bin(x, y, by, f = sum)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bin \u2014 resample_bin","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra parameters passed f. integer specifying binning ratio (.e. number points grouped together; see window_tumbling()). f function takes numeric vector intensities argument returns single numeric vector. Used estimate local representative value bin (defaults sum(); see examples).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bin \u2014 resample_bin","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bin \u2014 resample_bin","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_bin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bin \u2014 resample_bin","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")   ## Bin by 3 XRD_bin_mean <- resample_bin(XRD, by = 3, f = mean) XRD_bin_min <- resample_bin(XRD, by = 3, f = min)  plot(XRD, type = \"l\", xlim = c(25, 35),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_bin_mean, type = \"l\", col = \"red\") lines(XRD_bin_min, type = \"l\", col = \"green\")   ## Downsample by 10 XRD_down <- resample_down(XRD, by = 10)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_down, type = \"l\", col = \"red\")   ## Linearly interpolate XRD_approx <- resample_interpolate(XRD, from = 20, to = 40, by = 0.02)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_approx, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":null,"dir":"Reference","previous_headings":"","what":"Downsample \u2014 resample_down","title":"Downsample \u2014 resample_down","text":"Downsample","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downsample \u2014 resample_down","text":"","code":"resample_down(x, y, ...)  # S4 method for numeric,numeric resample_down(x, y, by)  # S4 method for ANY,missing resample_down(x, y, by)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downsample \u2014 resample_down","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. integer specifying downsampling factor.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downsample \u2014 resample_down","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Downsample \u2014 resample_down","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_down.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downsample \u2014 resample_down","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")   ## Bin by 3 XRD_bin_mean <- resample_bin(XRD, by = 3, f = mean) XRD_bin_min <- resample_bin(XRD, by = 3, f = min)  plot(XRD, type = \"l\", xlim = c(25, 35),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_bin_mean, type = \"l\", col = \"red\") lines(XRD_bin_min, type = \"l\", col = \"green\")   ## Downsample by 10 XRD_down <- resample_down(XRD, by = 10)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_down, type = \"l\", col = \"red\")   ## Linearly interpolate XRD_approx <- resample_interpolate(XRD, from = 20, to = 40, by = 0.02)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_approx, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Linearly Interpolate \u2014 resample_interpolate","title":"Linearly Interpolate \u2014 resample_interpolate","text":"Linearly Interpolate","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linearly Interpolate \u2014 resample_interpolate","text":"","code":"resample_interpolate(x, y, ...)  # S4 method for numeric,numeric resample_interpolate(x, y, from, to, by, ...)  # S4 method for ANY,missing resample_interpolate(x, y, from, to, by, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linearly Interpolate \u2014 resample_interpolate","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra arguments passed stats::approx(). length-one numeric vector giving starting value sequence interpolation take place. length-one numeric vector giving end value sequence interpolation take place. length-one numeric vector specifying increment sequence.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linearly Interpolate \u2014 resample_interpolate","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Linearly Interpolate \u2014 resample_interpolate","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/resample_interpolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linearly Interpolate \u2014 resample_interpolate","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")   ## Bin by 3 XRD_bin_mean <- resample_bin(XRD, by = 3, f = mean) XRD_bin_min <- resample_bin(XRD, by = 3, f = min)  plot(XRD, type = \"l\", xlim = c(25, 35),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_bin_mean, type = \"l\", col = \"red\") lines(XRD_bin_min, type = \"l\", col = \"green\")   ## Downsample by 10 XRD_down <- resample_down(XRD, by = 10)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_down, type = \"l\", col = \"red\")   ## Linearly interpolate XRD_approx <- resample_interpolate(XRD, from = 20, to = 40, by = 0.02)  plot(XRD, type = \"l\", xlim = c(20, 40),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD_approx, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalize intensities by AUC \u2014 rescale_area","title":"Normalize intensities by AUC \u2014 rescale_area","text":"Rescales intensities area curve (AUC) equal 1.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalize intensities by AUC \u2014 rescale_area","text":"","code":"rescale_area(x, y, ...)  # S4 method for numeric,numeric rescale_area(x, y, method = c(\"rectangle\", \"trapezoid\"), ...)  # S4 method for ANY,missing rescale_area(x, method = c(\"rectangle\", \"trapezoid\"), ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalize intensities by AUC \u2014 rescale_area","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. method character string specifying method integration. must one \"rectangle\" \"trapezoid\". unambiguous substring can given.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalize intensities by AUC \u2014 rescale_area","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Normalize intensities by AUC \u2014 rescale_area","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_area.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalize intensities by AUC \u2014 rescale_area","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Normalize by area under the curve BEGe_area <- rescale_area(BEGe) plot(BEGe_area, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")  integrate_rectangle(BEGe) #> [1] 147922.1 integrate_rectangle(BEGe_area) #> [1] 1  ## Rescale so that intensities sum to 1 BEGe_total <- rescale_total(BEGe, total = 1) plot(BEGe_total, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Rescale intensities to 0-1 BEGe_range <- rescale_range(BEGe, min = 0, max = 1) plot(BEGe_range, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"Rescales intensities specified minimum maximum.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"","code":"rescale_range(x, y, ...)  rescale_min(x, y, ...)  rescale_max(x, y, ...)  # S4 method for numeric,numeric rescale_range(x, y, min = 0, max = 1)  # S4 method for ANY,missing rescale_range(x, min = 0, max = 1)  # S4 method for numeric,numeric rescale_min(x, y, min = 0)  # S4 method for ANY,missing rescale_min(x, min = 0)  # S4 method for numeric,numeric rescale_max(x, y, max = 1)  # S4 method for ANY,missing rescale_max(x, max = 1)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. min legnth-one numeric vector specifying output minimum. max legnth-one numeric vector specifying output maximum.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rescales intensities to have specified minimum and maximum \u2014 rescale_range","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Normalize by area under the curve BEGe_area <- rescale_area(BEGe) plot(BEGe_area, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")  integrate_rectangle(BEGe) #> [1] 147922.1 integrate_rectangle(BEGe_area) #> [1] 1  ## Rescale so that intensities sum to 1 BEGe_total <- rescale_total(BEGe, total = 1) plot(BEGe_total, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Rescale intensities to 0-1 BEGe_range <- rescale_range(BEGe, min = 0, max = 1) plot(BEGe_range, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":null,"dir":"Reference","previous_headings":"","what":"Rescale intensities to sum to a specified value \u2014 rescale_total","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"Rescales intensities sum specified value.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"","code":"rescale_total(x, y, ...)  # S4 method for numeric,numeric rescale_total(x, y, total = 1)  # S4 method for ANY,missing rescale_total(x, total = 1)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. total legnth-one numeric vector specifying output total. Defaults 1, .e. normalizes total intensity.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_total.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rescale intensities to sum to a specified value \u2014 rescale_total","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Normalize by area under the curve BEGe_area <- rescale_area(BEGe) plot(BEGe_area, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")  integrate_rectangle(BEGe) #> [1] 147922.1 integrate_rectangle(BEGe_area) #> [1] 1  ## Rescale so that intensities sum to 1 BEGe_total <- rescale_total(BEGe, total = 1) plot(BEGe_total, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Rescale intensities to 0-1 BEGe_range <- rescale_range(BEGe, min = 0, max = 1) plot(BEGe_range, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Intensities \u2014 rescale_transform","title":"Transform Intensities \u2014 rescale_transform","text":"Transform Intensities","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Intensities \u2014 rescale_transform","text":"","code":"rescale_transform(x, y, ...)  # S4 method for numeric,numeric rescale_transform(x, y, f, ...)  # S4 method for ANY,missing rescale_transform(x, f, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Intensities \u2014 rescale_transform","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra arguments passed f. f function takes numeric vector intensities argument returns numeric vector.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Intensities \u2014 rescale_transform","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Intensities \u2014 rescale_transform","text":"Transformation intensities can used improve identification peaks low signal--noise ratio.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Transform Intensities \u2014 rescale_transform","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/rescale_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Intensities \u2014 rescale_transform","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Transform intensities BEGe_trans <- rescale_transform(BEGe, f = sqrt) plot(BEGe_trans, type = \"l\", xlab = \"Energy (keV)\", ylab = \"sqrt(Count)\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":null,"dir":"Reference","previous_headings":"","what":"Bind \u2014 signal_bind","title":"Bind \u2014 signal_bind","text":"Combines XY objects.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bind \u2014 signal_bind","text":"","code":"signal_bind(...)  # S4 method for ANY signal_bind(...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bind \u2014 signal_bind","text":"... object can interpreted suitable way (see grDevices::xy.coords()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bind \u2014 signal_bind","text":"Returns matrix intensities.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bind \u2014 signal_bind","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_bind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bind \u2014 signal_bind","text":"","code":"## X-ray diffraction data(\"XRD\")  XRD1 <- signal_drift(XRD, lag = 1500)  ## Bind XRD_bind <- signal_bind(XRD, XRD1) XRD_bind[, 1:10] #>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> XRD  6780 6786 6773 6961 6958 6796 6920 6910 6785  6879 #> XRD1 8280 8286 8273 8461 8458 8296 8420 8410 8285  8379  ## Mean XRD_mean <- signal_mean(XRD, XRD1)  plot(NULL, type = \"l\", xlim = c(10, 70) , ylim = c(3000, 36000),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD, type = \"l\") lines(XRD1, type = \"l\") lines(XRD_mean, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":null,"dir":"Reference","previous_headings":"","what":"Baseline Correction \u2014 signal_correct","title":"Baseline Correction \u2014 signal_correct","text":"Baseline Correction","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Baseline Correction \u2014 signal_correct","text":"","code":"signal_correct(x, y, ...)  # S4 method for numeric,numeric signal_correct(x, y, method = c(\"linear\", \"rubberband\", \"SNIP\", \"4S\"), ...)  # S4 method for ANY,missing signal_correct(x, method = c(\"linear\", \"rubberband\", \"SNIP\", \"4S\"), ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Baseline Correction \u2014 signal_correct","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra arguments passed baseline_*() (see details). method character string specifying method baseline estimation. must one \"linear\", \"rubberband\", \"SNIP\" \"4S\" (see details). unambiguous substring can given.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Baseline Correction \u2014 signal_correct","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Baseline Correction \u2014 signal_correct","text":"Available methods baseline estimation: linear Linear baseline estimation (see baseline_linear()). rubberband Rubberband baseline estimation (see baseline_rubberband()). SNIP Sensitive Nonlinear Iterative Peak clipping algorithm (see baseline_snip()). 4S 4S Peak Filling (see baseline_peakfilling()).","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Baseline Correction \u2014 signal_correct","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_correct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Baseline Correction \u2014 signal_correct","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Drift baseline <- baseline_snip(BEGe) BEGe_drif <- signal_drift(BEGe, lag = baseline, subtract = TRUE)  plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\") lines(BEGe_drif, type = \"l\", col = \"red\")   ## Correct BEGe_corr <- signal_correct(BEGe, method = \"SNIP\")  plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\") lines(BEGe_corr, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":null,"dir":"Reference","previous_headings":"","what":"Drift Intensities \u2014 signal_drift","title":"Drift Intensities \u2014 signal_drift","text":"Drift Intensities","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drift Intensities \u2014 signal_drift","text":"","code":"signal_drift(x, y, lag, ...)  # S4 method for numeric,numeric,numeric signal_drift(x, y, lag)  # S4 method for ANY,missing,ANY signal_drift(x, lag, subtract = FALSE)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drift Intensities \u2014 signal_drift","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). lag numeric vector specifying offset object can interpreted suitable way (see grDevices::xy.coords()) ... Currently used. subtract logical scalar: lag subtracted y?","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drift Intensities \u2014 signal_drift","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Drift Intensities \u2014 signal_drift","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_drift.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Drift Intensities \u2014 signal_drift","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Subset from 2.75 to 200 keV BEGe <- signal_select(BEGe, from = 3, to = 200)  ## Drift BEGe_plus <- signal_drift(BEGe, lag = 250) BEGe_minus <- signal_drift(BEGe, lag = 250, subtract = TRUE)  plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\") lines(BEGe_plus, type = \"l\", col = \"red\") lines(BEGe_minus, type = \"l\", col = \"green\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Intensities \u2014 signal_mean","title":"Mean Intensities \u2014 signal_mean","text":"Mean Intensities","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean Intensities \u2014 signal_mean","text":"","code":"signal_mean(...)  # S4 method for ANY signal_mean(...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean Intensities \u2014 signal_mean","text":"... object can interpreted suitable way (see grDevices::xy.coords()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean Intensities \u2014 signal_mean","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean Intensities \u2014 signal_mean","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean Intensities \u2014 signal_mean","text":"","code":"## X-ray diffraction data(\"XRD\")  XRD1 <- signal_drift(XRD, lag = 1500)  ## Bind XRD_bind <- signal_bind(XRD, XRD1) XRD_bind[, 1:10] #>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> XRD  6780 6786 6773 6961 6958 6796 6920 6910 6785  6879 #> XRD1 8280 8286 8273 8461 8458 8296 8420 8410 8285  8379  ## Mean XRD_mean <- signal_mean(XRD, XRD1)  plot(NULL, type = \"l\", xlim = c(10, 70) , ylim = c(3000, 36000),      xlab = expression(2*theta), ylab = \"Count\") lines(XRD, type = \"l\") lines(XRD1, type = \"l\") lines(XRD_mean, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":null,"dir":"Reference","previous_headings":"","what":"Shift the X Scale \u2014 signal_shift","title":"Shift the X Scale \u2014 signal_shift","text":"Shifts x scale given value.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Shift the X Scale \u2014 signal_shift","text":"","code":"signal_shift(x, y, lag, ...)  # S4 method for numeric,numeric signal_shift(x, y, lag)  # S4 method for ANY,missing signal_shift(x, lag)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shift the X Scale \u2014 signal_shift","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). lag numeric vector specifying offset. ... Currently used.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shift the X Scale \u2014 signal_shift","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Shift the X Scale \u2014 signal_shift","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/signal_shift.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Shift the X Scale \u2014 signal_shift","text":"","code":"## X-ray diffraction data(\"XRD\")  ## Plot spectrum plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Shift by one degree XRD_offset <- signal_shift(XRD, lag = 1) lines(XRD_offset, type = \"l\", col = \"red\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":null,"dir":"Reference","previous_headings":"","what":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"Penalized Likelihood Smoothing","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"","code":"smooth_likelihood(x, y, ...)  # S4 method for numeric,numeric smooth_likelihood(x, y, lambda, d = 2, SE = FALSE, progress = interactive())  # S4 method for ANY,missing smooth_likelihood(x, lambda, d = 2, SE = FALSE, progress = interactive())","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. lambda integer giving smoothing parameter. larger lambda , smoother curve. d integer specifying order penalty. SE logical scalar: standard errors returned? progress logical scalar: progress bar displayed?","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"Matrix required.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"de Rooi, J. J., van der Pers, N. M., Hendrikx, R. W. ., Delhez, R., B\u00f6ttger . J. Eilers, P. H. C. (2014). Smoothing X-ray diffraction data Ka2 elimination using penalized likelihood composite link model. Journal Applied Crystallography, 47: 852-860. doi:10.1107/S1600576714005809","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"J. J. de Rooi et al. (original R code).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"","code":"if (FALSE) { ## X-ray diffraction data(\"XRD\")  ## Subset from 20 to 40 degrees XRD <- signal_select(XRD, from = 20, to = 40)  ## Plot diffractogram plot(XRD, type = \"l\", xlab = expression(2*theta), ylab = \"Count\")  ## Penalized likelihood smoothing lambda <- seq(from = 1, to = 8, length.out = 40) lambda <- 10^lambda  likelihood <- smooth_likelihood(XRD, lambda = lambda, d = 3) lines(likelihood, col = \"red\")  ## Strip ka2 ka2 <- ka2_strip_penalized(XRD, lambda = lambda, tau = 0.5, nseg = 1) lines(ka2, col = \"blue\") }","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":null,"dir":"Reference","previous_headings":"","what":"Loess Smoothing \u2014 smooth_loess","title":"Loess Smoothing \u2014 smooth_loess","text":"Smoothes intensities loess fitting.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Loess Smoothing \u2014 smooth_loess","text":"","code":"smooth_loess(x, y, ...)  # S4 method for numeric,numeric smooth_loess(x, y, span = 0.75, ...)  # S4 method for ANY,missing smooth_loess(x, span = 0.75, ...)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Loess Smoothing \u2014 smooth_loess","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Extra arguments passed stats::loess(). span integer specifying degree smoothing (see stats::loess()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Loess Smoothing \u2014 smooth_loess","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Loess Smoothing \u2014 smooth_loess","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_loess.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Loess Smoothing \u2014 smooth_loess","text":"","code":"## Simulate data with some noise x <- seq(-4, 4, length = 100) y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)  ## Plot spectrum plot(x, y, type = \"l\", xlab = \"\", ylab = \"\")   ## Rectangular smoothing unweighted <- smooth_rectangular(x, y, m = 3) plot(unweighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Triangular smoothing weighted <- smooth_triangular(x, y, m = 5) plot(weighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Loess smoothing loess <- smooth_loess(x, y, span = 0.75) plot(loess, type = \"l\", xlab = \"\", ylab = \"\")   ## Savitzky\u2013Golay filter savitzky <- smooth_savitzky(x, y, m = 21, p = 2) plot(savitzky, type = \"l\", xlab = \"\", ylab = \"\")   ## Whittaker smoothing whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2) plot(whittaker, type = \"l\", xlab = \"\", ylab = \"\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Rectangular Smoothing \u2014 smooth_rectangular","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"Unweighted sliding-average rectangular Smoothing.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"","code":"smooth_rectangular(x, y, ...)  # S4 method for numeric,numeric smooth_rectangular(x, y, m = 3)  # S4 method for ANY,missing smooth_rectangular(x, m)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. m odd integer giving window size (.e. number adjacent points used; see window_sliding()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"replaces point signal average \\(m\\) adjacent points.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, progressively wider/narrower windows used ends data series.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_rectangular.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rectangular Smoothing \u2014 smooth_rectangular","text":"","code":"## Simulate data with some noise x <- seq(-4, 4, length = 100) y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)  ## Plot spectrum plot(x, y, type = \"l\", xlab = \"\", ylab = \"\")   ## Rectangular smoothing unweighted <- smooth_rectangular(x, y, m = 3) plot(unweighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Triangular smoothing weighted <- smooth_triangular(x, y, m = 5) plot(weighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Loess smoothing loess <- smooth_loess(x, y, span = 0.75) plot(loess, type = \"l\", xlab = \"\", ylab = \"\")   ## Savitzky\u2013Golay filter savitzky <- smooth_savitzky(x, y, m = 21, p = 2) plot(savitzky, type = \"l\", xlab = \"\", ylab = \"\")   ## Whittaker smoothing whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2) plot(whittaker, type = \"l\", xlab = \"\", ylab = \"\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":null,"dir":"Reference","previous_headings":"","what":"Savitzky-Golay Filter \u2014 smooth_savitzky","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"Savitzky-Golay Filter","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"","code":"smooth_savitzky(x, y, ...)  # S4 method for numeric,numeric smooth_savitzky(x, y, m = 3, p = 2)  # S4 method for ANY,missing smooth_savitzky(x, m, p)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. m odd integer giving window size (.e. number adjacent points used). p integer giving degree polynomial used.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"method based least-squares fitting polynomials segments \\(m\\) adjacent points.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, original \\((m - 1) / 2\\) points ends data series preserved.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"Gorry, P. . (1990). General Least-Squares Smoothing Differentiation Convolution (Savitzky-Golay) Method. Analytical Chemistry, 62(6), p. 570-573. doi:10.1021/ac00205a007 . Savitzky, . & Golay, M. J. E. (1964). Smoothing Differentiation Data Simplified Least Squares Procedures. Analytical Chemistry, 36(8), p. 1627-1639. doi:10.1021/ac60214a047 .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"","code":"## Simulate data with some noise x <- seq(-4, 4, length = 100) y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)  ## Plot spectrum plot(x, y, type = \"l\", xlab = \"\", ylab = \"\")   ## Rectangular smoothing unweighted <- smooth_rectangular(x, y, m = 3) plot(unweighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Triangular smoothing weighted <- smooth_triangular(x, y, m = 5) plot(weighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Loess smoothing loess <- smooth_loess(x, y, span = 0.75) plot(loess, type = \"l\", xlab = \"\", ylab = \"\")   ## Savitzky\u2013Golay filter savitzky <- smooth_savitzky(x, y, m = 21, p = 2) plot(savitzky, type = \"l\", xlab = \"\", ylab = \"\")   ## Whittaker smoothing whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2) plot(whittaker, type = \"l\", xlab = \"\", ylab = \"\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":null,"dir":"Reference","previous_headings":"","what":"Triangular Smoothing \u2014 smooth_triangular","title":"Triangular Smoothing \u2014 smooth_triangular","text":"Weighted sliding-average triangular smoothing.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Triangular Smoothing \u2014 smooth_triangular","text":"","code":"smooth_triangular(x, y, ...)  # S4 method for numeric,numeric smooth_triangular(x, y, m = 3)  # S4 method for ANY,missing smooth_triangular(x, m)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Triangular Smoothing \u2014 smooth_triangular","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. m odd integer giving window size (.e. number adjacent points used; see window_sliding()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Triangular Smoothing \u2014 smooth_triangular","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Triangular Smoothing \u2014 smooth_triangular","text":"replaces point signal weighted mean \\(m\\) adjacent points.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Triangular Smoothing \u2014 smooth_triangular","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, progressively wider/narrower windows used ends data series.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Triangular Smoothing \u2014 smooth_triangular","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_triangular.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Triangular Smoothing \u2014 smooth_triangular","text":"","code":"## Simulate data with some noise x <- seq(-4, 4, length = 100) y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)  ## Plot spectrum plot(x, y, type = \"l\", xlab = \"\", ylab = \"\")   ## Rectangular smoothing unweighted <- smooth_rectangular(x, y, m = 3) plot(unweighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Triangular smoothing weighted <- smooth_triangular(x, y, m = 5) plot(weighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Loess smoothing loess <- smooth_loess(x, y, span = 0.75) plot(loess, type = \"l\", xlab = \"\", ylab = \"\")   ## Savitzky\u2013Golay filter savitzky <- smooth_savitzky(x, y, m = 21, p = 2) plot(savitzky, type = \"l\", xlab = \"\", ylab = \"\")   ## Whittaker smoothing whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2) plot(whittaker, type = \"l\", xlab = \"\", ylab = \"\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":null,"dir":"Reference","previous_headings":"","what":"Whittaker Smoothing \u2014 smooth_whittaker","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"Whittaker Smoothing","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"","code":"smooth_whittaker(x, y, ...)  # S4 method for numeric,numeric smooth_whittaker(x, y, lambda = 1600, d = 2, sparse = FALSE)  # S4 method for ANY,missing smooth_whittaker(x, lambda = 1600, d = 2, sparse = FALSE)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. lambda integer giving smoothing parameter. larger lambda , smoother curve. d integer specifying order penalty. sparse logical scalar: sparse matrices used computation? TRUE, Matrix required.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"Eilers, P. H. C. (2003). Perfect Smoother. Analytical Chemistry, 75(14): 3631-36. doi:10.1021/ac034173t .","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"","code":"## Simulate data with some noise x <- seq(-4, 4, length = 100) y <- dnorm(x) + rnorm(100, mean = 0, sd = 0.01)  ## Plot spectrum plot(x, y, type = \"l\", xlab = \"\", ylab = \"\")   ## Rectangular smoothing unweighted <- smooth_rectangular(x, y, m = 3) plot(unweighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Triangular smoothing weighted <- smooth_triangular(x, y, m = 5) plot(weighted, type = \"l\", xlab = \"\", ylab = \"\")   ## Loess smoothing loess <- smooth_loess(x, y, span = 0.75) plot(loess, type = \"l\", xlab = \"\", ylab = \"\")   ## Savitzky\u2013Golay filter savitzky <- smooth_savitzky(x, y, m = 21, p = 2) plot(savitzky, type = \"l\", xlab = \"\", ylab = \"\")   ## Whittaker smoothing whittaker <- smooth_whittaker(x, y, lambda = 1600, d = 2) plot(whittaker, type = \"l\", xlab = \"\", ylab = \"\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset \u2014 subset","title":"Subset \u2014 subset","text":"signal_select() allows subset values x. signal_slice() allows subset position along x.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset \u2014 subset","text":"","code":"signal_select(x, y, ...)  signal_slice(x, y, ...)  # S4 method for numeric,numeric signal_select(x, y, from, to)  # S4 method for ANY,missing signal_select(x, from, to)  # S4 method for numeric,numeric signal_slice(x, y, subset)  # S4 method for ANY,missing signal_slice(x, subset)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset \u2014 subset","text":"x, y numeric vector. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. , numeric value giving first last value (x unit) selected. subset integer vector giving either positive values keep, negative values drop. values provided must either positive negative (coerced integer .integer()).","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset \u2014 subset","text":"Returns list two components x y.","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Subset \u2014 subset","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset \u2014 subset","text":"","code":"## gamma-ray spectrometry data(\"BEGe\")  ## Plot spectrum plot(BEGe, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Subset from 2.75 keV to 200 keV BEGe_1 <- signal_select(BEGe, from = 3, to = 200)  ## Plot spectrum plot(BEGe_1, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")   ## Subset from the 20th to the 1250th value BEGe_2 <- signal_slice(BEGe, subset = 20:1250)  ## Plot spectrum plot(BEGe_2, type = \"l\", xlab = \"Energy (keV)\", ylab = \"Count\")","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":null,"dir":"Reference","previous_headings":"","what":"Sliding Windows \u2014 window_sliding","title":"Sliding Windows \u2014 window_sliding","text":"\\((m - 1) / 2\\) points beginning end data series complete \\(m\\)-width window obtained. prevent data loss, progressively wider/narrower windows evaluated ends data series.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sliding Windows \u2014 window_sliding","text":"","code":"window_sliding(n, m, ...)  # S4 method for integer,integer window_sliding(n, m, i = NULL)  # S4 method for numeric,numeric window_sliding(n, m, i = NULL)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sliding Windows \u2014 window_sliding","text":"n integer giving length data series (coerced .integer() hence truncated toward zero). m odd integer giving window size, .e. number adjacent points used (coerced .integer() hence truncated toward zero). ... Currently used. vector integer specifying indices data points windows returned. NULL (default), windows evaluated data point.","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sliding Windows \u2014 window_sliding","text":"Returns length-\\(n\\)  list integer vectors (indices data points window).","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sliding Windows \u2014 window_sliding","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_sliding.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sliding Windows \u2014 window_sliding","text":"","code":"## Length of the data series n <- 10  ## Progressive sliding windows sliding <- window_sliding(n = n, m = 5)  plot(NULL, xlim = c(1, n), ylim = c(1, 10.5), xlab = \"Index\", ylab = \"Window\") for (i in seq_along(sliding)) {   w <- sliding[[i]]   text(x = w, y = rep(i, length(w)), labels = w, pos = 3)   lines(w, rep(i, length(w)), type = \"l\", lwd = 2) }   ## Tumbling windows ## (compare with drop = TRUE) tumbling <- window_tumbling(n = n, m = 3, drop = FALSE)  plot(NULL, xlim = c(1, n), ylim = c(1, 5.5), xlab = \"Index\", ylab = \"Window\") for (i in seq_along(tumbling)) {   w <- tumbling[[i]]   text(x = w, y = rep(i, length(w)), labels = w, pos = 3)   lines(w, rep(i, length(w)), type = \"l\", lwd = 2) }","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":null,"dir":"Reference","previous_headings":"","what":"Tumbling Windows \u2014 window_tumbling","title":"Tumbling Windows \u2014 window_tumbling","text":"Tumbling Windows","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tumbling Windows \u2014 window_tumbling","text":"","code":"window_tumbling(n, m, ...)  # S4 method for integer,integer window_tumbling(n, m, drop = FALSE)  # S4 method for numeric,numeric window_tumbling(n, m, drop = FALSE)","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tumbling Windows \u2014 window_tumbling","text":"n integer giving length data series (coerced .integer() hence truncated toward zero). m integer giving window size, .e. number adjacent points used (coerced .integer() hence truncated toward zero). ... Currently used. drop logical scalar: m multiple n, last data points removed windows length?","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tumbling Windows \u2014 window_tumbling","text":"Returns list integer vectors (indices data points window).","code":"","package":"alkahest"},{"path":[],"package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Tumbling Windows \u2014 window_tumbling","text":"N. Frerebeau","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/window_tumbling.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tumbling Windows \u2014 window_tumbling","text":"","code":"## Length of the data series n <- 10  ## Progressive sliding windows sliding <- window_sliding(n = n, m = 5)  plot(NULL, xlim = c(1, n), ylim = c(1, 10.5), xlab = \"Index\", ylab = \"Window\") for (i in seq_along(sliding)) {   w <- sliding[[i]]   text(x = w, y = rep(i, length(w)), labels = w, pos = 3)   lines(w, rep(i, length(w)), type = \"l\", lwd = 2) }   ## Tumbling windows ## (compare with drop = TRUE) tumbling <- window_tumbling(n = n, m = 3, drop = FALSE)  plot(NULL, xlim = c(1, n), ylim = c(1, 5.5), xlab = \"Index\", ylab = \"Window\") for (i in seq_along(tumbling)) {   w <- tumbling[[i]]   text(x = w, y = rep(i, length(w)), labels = w, pos = 3)   lines(w, rep(i, length(w)), type = \"l\", lwd = 2) }","package":"alkahest"}]
\ No newline at end of file
diff --git a/references.json b/references.json
index f0fd9ea85..b8fb65dcc 100644
--- a/references.json
+++ b/references.json
@@ -1 +1 @@
-[{"path":"https://packages.tesselle.org/arkhe/reference/interval_hdr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Highest Density Regions \u2014 interval_hdr","text":"Hyndman, R. J. (1996). Computing graphing highest density regions. American Statistician, 50: 120-126. doi:10.2307/2684423 .","code":"","package":"arkhe"},{"path":"https://packages.tesselle.org/kairos/reference/aoristic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Aoristic Analysis \u2014 aoristic","text":"Crema, E. R. (2012). Modelling Temporal Uncertainty Archaeological Analysis. Journal Archaeological Method Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3 . Johnson, . (2004). Aoristic Analysis: Seeds New Approach Mapping Archaeological Distributions Time. Ausserer, K. F., B\u00f6rner, W., Goriany, M. & Karlhuber-V\u00f6ckl, L. (ed.), Enter Past - E-Way Four Dimensions Cultural Heritage, Oxford: Archaeopress, p. 448-52. BAR International Series 1227. doi:10.15496/publikation-2085 M\u00fcller-Schee\u00dfel, N. & Hinz, M. (2018). Aoristic Research R: Correcting Temporal Categorizations Archaeology. Presented Human History Digital Future (CAA 2018), Tubingen, March 21. https://www.youtube.com/watch?v=bUBukex30QI. Palmisano, ., Bevan, . & Shennan, S. (2017). Comparing Archaeological Proxies Long-Term Population Patterns: Example Central Italy. Journal Archaeological Science, 87: 59-72. doi:10.1016/j.jas.2017.10.001 . Ratcliffe, J. H. (2000). Aoristic Analysis: Spatial Interpretation Unspecific Temporal Events. International Journal Geographical Information Science, 14(7): 669-79. doi:10.1080/136588100424963 . Ratcliffe, J. H. (2002). Aoristic Signatures Spatio-Temporal Analysis High Volume Crime Patterns. Journal Quantitative Criminology, 18(1): 23-43. doi:10.1023/:1013240828824 .","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/apportion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chronological Apportioning \u2014 apportion","text":"Roberts, J. M., Mills, B. J., Clark, J. J., Haas, W. R., Huntley, D. L. & Trowbridge, M. . (2012). Method Chronological Apportioning Ceramic Assemblages. Journal Archaeological Science, 39(5): 1513-20. doi:10.1016/j.jas.2011.12.022 .","code":"","package":"kairos"},{"path":"https://packages.tesselle.org/kairos/reference/event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Event and Accumulation Dates \u2014 event","text":"Bellanger, L. & Husi, P. (2013). Mesurer et mod\u00e9liser le temps inscrit dans la mati\u00e8re \u00e0 partir d'une source mat\u00e9rielle : la c\u00e9ramique m\u00e9di\u00e9vale. Mesure et Histoire M\u00e9di\u00e9vale. Histoire ancienne et m\u00e9di\u00e9vale. Paris: Publication de la Sorbonne, p. 119-134. Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Bellanger, L., Tomassone, R. & Husi, P. (2008). Statistical Approach Dating Archaeological Contexts. Journal Data Science, 6, 135-154. Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes arch\u00e9ologiques. Revue de Statistique Appliqu\u00e9e, 54(2), 65-81. Bellanger, L., Husi, P. & Tomassone, R. (2006). Statistical Aspects Pottery Quantification Dating Archaeological Contexts. Archaeometry, 48(1), 169-183. doi:10.1111/j.1475-4754.2006.00249.x . Poblome, J. & Groenen, P. J. F. (2003). Constrained Correspondence Analysis Seriation Sagalassos Tablewares. Doerr, M. & Apostolis, S. (eds.), Digital Heritage Archaeology. 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Color Research & Application, 24(4), p. 243-52. doi:10.1002/(SICI)1520-6378(199908)24:4<243::AID-COL5>3.0.CO;2-3 .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamako.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bamako Sequential Color Scheme \u2014 scale_crameri_bamako","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlow.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's batlow Sequential Color Scheme \u2014 scale_crameri_batlow","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowK.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's batlowK Sequential Color Scheme \u2014 scale_crameri_batlowK","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_batlowW.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's batlowW Sequential Color Scheme \u2014 scale_crameri_batlowW","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_berlin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's berlin Diverging Color Scheme \u2014 scale_crameri_berlin","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bilbao.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bilbao Sequential Color Scheme \u2014 scale_crameri_bilbao","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_broc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's broc Diverging Color Scheme \u2014 scale_crameri_broc","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_brocO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's brocO Cyclic Color Scheme \u2014 scale_crameri_brocO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_buda.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's buda Sequential Color Scheme \u2014 scale_crameri_buda","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bukavu.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bukavu Multi-Sequential Color Scheme \u2014 scale_crameri_bukavu","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cork.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's cork Diverging Color Scheme \u2014 scale_crameri_cork","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_corkO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's corkO Cyclic Color Scheme \u2014 scale_crameri_corkO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_cyclic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Cyclic Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_cyclic","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_davos.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's davos Sequential Color Scheme \u2014 scale_crameri_davos","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_devon.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's devon Sequential Color Scheme \u2014 scale_crameri_devon","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_diverging.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Diverging Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_diverging","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_fes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's fes Multi-Sequential Color Scheme \u2014 scale_crameri_fes","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_grayC.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's grayC Sequential Color Scheme \u2014 scale_crameri_grayC","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_hawaii.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's hawaii Sequential Color Scheme \u2014 scale_crameri_hawaii","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_imola.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's imola Sequential Color Scheme \u2014 scale_crameri_imola","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lapaz.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's lapaz Sequential Color Scheme \u2014 scale_crameri_lapaz","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lisbon.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's lisbon Diverging Color Scheme \u2014 scale_crameri_lisbon","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_nuuk.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's nuuk Sequential Color Scheme \u2014 scale_crameri_nuuk","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oleron.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's oleron Multi-Sequential Color Scheme \u2014 scale_crameri_oleron","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_roma.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's roma Diverging Color Scheme \u2014 scale_crameri_roma","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_romaO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's romaO Cyclic Color Scheme \u2014 scale_crameri_romaO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tofino.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's tofino Diverging Color Scheme \u2014 scale_crameri_tofino","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_tokyo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's tokyo Sequential Color Scheme \u2014 scale_crameri_tokyo","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_turku.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's turku Sequential Color Scheme \u2014 scale_crameri_turku","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. 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Doctoral thesis, University College London. URL: https://discovery.ucl.ac.uk/id/eprint/1349806.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"Spratt, R. M. Lisiecki, L. E. (2016). Late Pleistocene sea level stack. Climate Past, 12, 1079-1092. doi:10.5194/cp-12-1079-2016","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"Cohen, K. M., Finney, S. C., Gibbard, P. L. Fan, J.-X. (2013). ICS International Chronostratigraphic Chart. Episodes, 36(3): 199-204. doi:10.18814/epiiugs/2013/v36i3/002","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"French Medieval Glass Composition \u2014 verre","text":"Barrera J., Velde B. (1989). study french medieval glass composition. Arch\u00e9ologie m\u00e9di\u00e9vale, 19, 81-130. doi:10.3406/arcme.1989.953 .","code":"","package":"folio"},{"path":"https://packages.tesselle.org/nexus/reference/covariance.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Variance and Covariance \u2014 covariance","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. 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London: Chapman Hall. doi:10.1007/978-94-009-4109-0 .","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/lia_age.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Geological Model Age from Lead Isotope Analysis \u2014 lia_age","text":"Albar\u00e8de, F., Desaulty, .-M. & Blichert-Toft, J. (2012). Geological Perspective Use Pb Isotopes Archaeometry. Archaeometry, 54: 853-867. doi:10.1111/j.1475-4754.2011.00653.x . Albar\u00e8de, F. & Juteau, M. (1984). Unscrambling Lead Model Ages. Geochimica et Cosmochimica Acta, 48(1): 207-12. doi:10.1016/0016-7037(84)90364-8 .","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/mean.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compositional Mean \u2014 mean","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall, p. 64-91. doi:10.1007/978-94-009-4109-0 .","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Outlier Detection \u2014 outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. Computers & Geosciences, 39, 77-85. doi:10.1016/j.cageo.2011.06.014 . Rousseeuw, P. J. & van Zomeren, B. C. (1990). Unmasking Multivariate Outliers Leverage Points. Journal American Statistical Association, 85(411): 633-639. doi:10.1080/01621459.1990.10474920 . Santos, F. (2020). Modern methods old data: overview robust methods outliers detection applications osteology. Journal Archaeological Science: Reports, 32, 102423. doi:10.1016/j.jasrep.2020.102423 .","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/plot_outliers.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot Outliers \u2014 plot_outliers","text":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate outlier detection exploration geochemistry. Computers & Geosciences, 31(5), 579-587. doi:10.1016/j.cageo.2004.11.013 . Filzmoser, P. & Hron, K. (2008). Outlier Detection Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233-248. doi:10.1007/s11004-007-9141-5 . Filzmoser, P., Hron, K. & Reimann, C. (2012). Interpretation multivariate outliers compositional data. 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J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barcel\u00f3-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Fi\u0161erov\u00e1, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455\u2011468. doi:10.1007/s11004-011-9333-x . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"Fi\u0161erov\u00e1, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455\u2011468. doi:10.1007/s11004-011-9333-x . Hron, K., Filzmoser, P., de Caritat, P., Fi\u0161erov\u00e1, E. & Gardlo, . (2017). Weighted Pivot Coordinates Compositional Data Application Geochemical Mapping. Mathematical Geosciences, 49(6), 797-814. doi:10.1007/s11004-017-9684-z . Greenacre, M. J. (2019). Compositional Data Analysis Practice. 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MethodsX, 2, 135-140. doi:10.1016/j.mex.2015.02.009 .","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"Lieber, C. . Mahadevan-Jansen, . (2003). Automated Method Subtraction Fluorescence Biological Raman Spectra. Applied Spectroscopy, 57(11): 1363-67. doi:10.1366/000370203322554518 .","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"Kneen, M. . Annegarn, H. J. (1996). Algorithm Fitting XRF, SEM PIXE X-Ray Spectra Backgrounds. 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\ No newline at end of file
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Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_ilr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Isometric Log-Ratios (ILR) \u2014 transform_ilr","text":"Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barcel\u00f3-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_inverse.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Inverse Log-Ratio Transformation \u2014 transform_inverse","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barcel\u00f3-Vidal, C. (2003). Isometric Logratio Transformations Compositional Data Analysis. Mathematical Geology, 35(3), 279-300. doi:10.1023/:1023818214614 . Fi\u0161erov\u00e1, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455\u2011468. doi:10.1007/s11004-011-9333-x . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_lr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pairwise Log-Ratios (LR) \u2014 transform_lr","text":"Aitchison, J. (1986). Statistical Analysis Compositional Data. London: Chapman Hall. doi:10.1007/978-94-009-4109-0 . Greenacre, M. J. (2019). Compositional Data Analysis Practice. Boca Raton: CRC Press.","code":"","package":"nexus"},{"path":"https://packages.tesselle.org/nexus/reference/transform_plr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pivot Log-Ratios (PLR) \u2014 transform_plr","text":"Fi\u0161erov\u00e1, E. & Hron, K. (2011). Interpretation Orthonormal Coordinates Compositional Data. Mathematical Geosciences, 43(4), 455\u2011468. doi:10.1007/s11004-011-9333-x . Hron, K., Filzmoser, P., de Caritat, P., Fi\u0161erov\u00e1, E. & Gardlo, . (2017). Weighted Pivot Coordinates Compositional Data Application Geochemical Mapping. Mathematical Geosciences, 49(6), 797-814. doi:10.1007/s11004-017-9684-z . Greenacre, M. J. (2019). Compositional Data Analysis Practice. 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Color Research & Application, 24(4), p. 243-52. doi:10.1002/(SICI)1520-6378(199908)24:4<243::AID-COL5>3.0.CO;2-3 .","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_acton.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's acton Sequential Color Scheme \u2014 scale_crameri_acton","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bam.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bam Diverging Color Scheme \u2014 scale_crameri_bam","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_bamO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's bamO Cyclic Color Scheme \u2014 scale_crameri_bamO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. 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Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_lajolla.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's lajolla Sequential Color Scheme \u2014 scale_crameri_lajolla","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. 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Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_mutlisequential.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Multi-Sequential Color Schemes for ggplot2\nand ggraph \u2014 scale_crameri_mutlisequential","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. 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Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_oslo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's oslo Sequential Color Scheme \u2014 scale_crameri_oslo","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. 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Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_sequential.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's Sequential Color Schemes for ggplot2 and ggraph \u2014 scale_crameri_sequential","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. 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Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. Nature Communications, 11, 5444. doi:10.1038/s41467-020-19160-7","code":"","package":"khroma"},{"path":"https://packages.tesselle.org/khroma/reference/scale_crameri_vikO.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fabio Crameri's vikO Cyclic Color Scheme \u2014 scale_crameri_vikO","text":"Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation StagLab 3.0. Geosci. Model Dev., 11, 2541-2562. doi:10.5194/gmd-11-2541-2018 Crameri, F., Shephard, G. E. & Heron, P. J. (2020). misuse colour science communication. 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Science, 310, 1313-1317. doi:10.1126/science.1120130","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/kommos.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Transport Jars from Kommos (Crete). \u2014 kommos","text":"Day, P. M., Quinn, P. S., Rutter, J. B. & Kilikoglou, V. (2011). World Goods: Transport Jars Commodity Exchange Late Bronze Age Harbor Kommos, Crete. Hesperia, 80, 511-558. doi:10.2972/hesperia.80.4.0511","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/law2006.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Law Dome Ice Core \u2014 law2006","text":"MacFarling Meure, C., Etheridge, D., Trudinger, C., Steele, P., Langenfelds, R., van Ommen, T., Smith, . Elkins, J. (2006). Law Dome CO2, CH4 N2O Ice Core Records Extended 2000 years BP. Geophysical Research Letters, 33(14), L14810. doi:10.1029/2006GL026152 .","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/lisiecki2005.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Benthic \\(\\delta^{18}O\\) Stack \u2014 lisiecki2005","text":"Lisiecki, L. E. Raymo, M. E. (2005). Pliocene-Pleistocene stack 57 globally distributed benthic d18O records. Paleoceanography, 20, PA1003. doi:10.1029/2004PA001071","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/loire.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Medieval Ceramics from the Loire Basin \u2014 loire","text":"Husi, P. (dir.). (2022). La c\u00e9ramique m\u00e9di\u00e9vale et moderne du bassin de la Loire moyenne, chrono-typologie et transformation des aires culturelles dans la longue dur\u00e9e (6e-19e s.). 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(2006). synchronized dating three Greenland ice cores throughout Holocene. Journal Geophysical Research, 111, D13102. doi:10.1029/2005JD006921 . Rasmussen, S. O., Andersen, K. K., Svensson, . M., Steffensen, J. P., Vinther, B. M., Clausen, H. B., Siggaard-Andersen, M.-L., Johnsen, S. J., Larsen, L. B., Dahl-Jensen, D., Bigler, M., R\u00f6thlisberger, R., Fischer, H., Goto-Azuma, K., Hansson, M. E. & Ruth, U. (2006). new Greenland ice core chronology last glacial termination. Journal Geophysical Research, 111, D06102. doi:10.1029/2005JD006079 . Andersen, K. K., Svensson, ., Johnsen, S. J., Rasmussen, S. O., Bigler, M., R\u00f6thlisberger, R., Ruth, U., Siggaard-Andersen, M.-L., Steffensen, J. P., Dahl-Jensen, D., Vinther, B. M. & Clausen, H.B. (2005). Greenland Ice Core Chronology 2005, 15-42 ka. Part 1: Constructing time scale. Quaternary Science Reviews, 25(23-24):3246-3257. doi:10.1016/j.quascirev.2006.08.002 . Svensson, ., Andersen, K. K., Bigler, M., Clausen, H. B., Dahl-Jensen, D., Davies, S. M., Johnsen, S. J., Muscheler, R., Rasmussen, S. O., R\u00f6thlisberger, R., Seierstad, ., Steffensen, J. P. & Vinther, B. M. (2008). 60,000 year Greenland stratigraphic ice core chronology. Climate Past, 4:47\u201357. doi:10.5194/cp-4-47-2008 . Wolff, E. W., Chappellaz, J., Blunier, T., Rasmussen, S. O. & Svensson, . (2010). Millennial-scale variability last glacial: ice core record. Quaternary Science Reviews, 29:2828-2838. doi:10.1016/j.quascirev.2009.10.013 .","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/shipwrecks.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mediterranean Shipwrecks \u2014 shipwrecks","text":"Parker, . J. (1992). Ancient Shipwrecks Mediterranean Roman Provinces. British Archaeological Reports International Series 580. Oxford. Strauss, E. J. (2007). Roman Cargoes: Underwater Evidence Eastern Mediterranean. Doctoral thesis, University College London. URL: https://discovery.ucl.ac.uk/id/eprint/1349806.","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/spratt2016.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Late Pleistocene Sea Level Stack \u2014 spratt2016","text":"Spratt, R. M. Lisiecki, L. E. (2016). Late Pleistocene sea level stack. Climate Past, 12, 1079-1092. doi:10.5194/cp-12-1079-2016","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/stratigraphy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chronostratigraphic Chart \u2014 stratigraphy","text":"Cohen, K. M., Finney, S. C., Gibbard, P. L. Fan, J.-X. (2013). ICS International Chronostratigraphic Chart. Episodes, 36(3): 199-204. doi:10.18814/epiiugs/2013/v36i3/002","code":"","package":"folio"},{"path":"https://packages.tesselle.org/folio/reference/verre.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"French Medieval Glass Composition \u2014 verre","text":"Barrera J., Velde B. (1989). study french medieval glass composition. Arch\u00e9ologie m\u00e9di\u00e9vale, 19, 81-130. doi:10.3406/arcme.1989.953 .","code":"","package":"folio"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_peakfilling.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"4S Peak Filling \u2014 baseline_peakfilling","text":"Liland, K. H. (2015). 4S Peak Filling - baseline estimation iterative mean suppression. MethodsX, 2, 135-140. doi:10.1016/j.mex.2015.02.009 .","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_polynomial.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Polynomial Baseline Estimation \u2014 baseline_polynomial","text":"Lieber, C. . Mahadevan-Jansen, . (2003). Automated Method Subtraction Fluorescence Biological Raman Spectra. Applied Spectroscopy, 57(11): 1363-67. doi:10.1366/000370203322554518 .","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_rollingball.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rolling Ball Baseline Estimation \u2014 baseline_rollingball","text":"Kneen, M. . Annegarn, H. J. (1996). Algorithm Fitting XRF, SEM PIXE X-Ray Spectra Backgrounds. Nuclear Instruments Methods Physics Research Section B: Beam Interactions Materials Atoms, 109/110: 209-213. doi:10.1016/0168-583X(95)00908-6 .","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/baseline_snip.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SNIP Baseline Estimation \u2014 baseline_snip","text":"Morh\u00e1\u010d, M., Kliman, J., Matou\u0161ek, V., Veselsk\u00fd, M. & Turzo, . (1997). Background elimination methods multidimensional gamma-ray spectra. Nuclear Instruments Methods Physics Research Section : Accelerators, Spectrometers, Detectors Associated Equipment, 401(1), p. 113-132. doi:10.1016/S0168-9002(97)01023-1 Morh\u00e1\u010d, M. & Matou\u0161ek, V. (2008). Peak Clipping Algorithms Background Estimation Spectroscopic Data. Applied Spectroscopy, 62(1), p. 91-106. doi:10.1366/000370208783412762 Ryan, C. G., Clayton, E., Griffin, W. L., Sie, S. H. & Cousens, D. R. (1988). SNIP, statistics-sensitive background treatment quantitative analysis PIXE spectra geoscience applications. Nuclear Instruments Methods Physics Research Section B: Beam Interactions Materials Atoms, 34(3), p. 396-402. doi:10.1016/0168-583X(88)90063-8","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/ka2_strip_penalized.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strip XRD ka2 \u2014 ka2_strip_penalized","text":"de Rooi, J. J., van der Pers, N. M., Hendrikx, R. W. ., Delhez, R., B\u00f6ttger . J. Eilers, P. H. C. (2014). Smoothing X-ray diffraction data Ka2 elimination using penalized likelihood composite link model. Journal Applied Crystallography, 47: 852-860. doi:10.1107/S1600576714005809","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_likelihood.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Penalized Likelihood Smoothing \u2014 smooth_likelihood","text":"de Rooi, J. J., van der Pers, N. M., Hendrikx, R. W. ., Delhez, R., B\u00f6ttger . J. Eilers, P. H. C. (2014). Smoothing X-ray diffraction data Ka2 elimination using penalized likelihood composite link model. Journal Applied Crystallography, 47: 852-860. doi:10.1107/S1600576714005809","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_savitzky.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Savitzky-Golay Filter \u2014 smooth_savitzky","text":"Gorry, P. . (1990). General Least-Squares Smoothing Differentiation Convolution (Savitzky-Golay) Method. Analytical Chemistry, 62(6), p. 570-573. doi:10.1021/ac00205a007 . Savitzky, . & Golay, M. J. E. (1964). Smoothing Differentiation Data Simplified Least Squares Procedures. Analytical Chemistry, 36(8), p. 1627-1639. doi:10.1021/ac60214a047 .","code":"","package":"alkahest"},{"path":"https://packages.tesselle.org/alkahest/reference/smooth_whittaker.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Whittaker Smoothing \u2014 smooth_whittaker","text":"Eilers, P. H. C. (2003). Perfect Smoother. Analytical Chemistry, 75(14): 3631-36. doi:10.1021/ac034173t .","code":"","package":"alkahest"}]
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index 63e338d6c..a2aabd525 100644
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