From b741609abada58b90a9b88e0817908a5f3c6fcad Mon Sep 17 00:00:00 2001
From: tabula <tabula@tesselle.org>
Date: Tue, 28 Nov 2023 14:52:16 +0000
Subject: [PATCH] Built site for tabula@4a95d98

---
 tabula/index.html                     |   9 +++---
 tabula/pkgdown.yml                    |   2 +-
 tabula/reference/Rplot002.png         | Bin 20417 -> 20436 bytes
 tabula/reference/bootstrap.html       |  42 +++++++++++++-------------
 tabula/reference/jackknife.html       |  40 ++++++++++++------------
 tabula/reference/plot_diversity-1.png | Bin 59284 -> 59627 bytes
 tabula/reference/plot_diversity-2.png | Bin 54899 -> 55455 bytes
 tabula/reference/resample.html        |  16 +++++-----
 tabula/reference/simulate-1.png       | Bin 59623 -> 59641 bytes
 tabula/reference/simulate-2.png       | Bin 54912 -> 54919 bytes
 tabula/search.json                    |   2 +-
 11 files changed, 55 insertions(+), 56 deletions(-)

diff --git a/tabula/index.html b/tabula/index.html
index 5abcdb205..1beabefc5 100644
--- a/tabula/index.html
+++ b/tabula/index.html
@@ -97,11 +97,10 @@
 </div>
 <!-- badges: start -->
 
-
-
-
-
-<!-- badges: end --><div class="section level2">
+<p> <a href="https://cran.r-project.org/package=tabula" class="external-link pkgdown-release"><img src="http://www.r-pkg.org/badges/version/tabula" alt="CRAN Version"></a> <a href="https://cran.r-project.org/web/checks/check_results_tabula.html" class="external-link pkgdown-release"><img src="https://badges.cranchecks.info/worst/tabula.svg" alt="CRAN checks"></a> <a href="https://cran.r-project.org/package=tabula" class="external-link pkgdown-release"><img src="http://cranlogs.r-pkg.org/badges/tabula" alt="CRAN Downloads"></a></p>
+<p><a href="https://www.repostatus.org/#active" class="external-link"><img src="https://www.repostatus.org/badges/latest/active.svg" alt="Project Status: Active – The project has reached a stable, usable state and is being actively developed."></a></p>
+<p><a href="https://doi.org/10.5281/zenodo.1489944" class="external-link"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.1489944.svg" alt="DOI Zenodo"></a> <a href="https://doi.org/10.21105/joss.01821" class="external-link"><img src="https://joss.theoj.org/papers/10.21105/joss.01821/status.svg" alt="DOI JOSS"></a> <!-- badges: end --></p>
+<div class="section level2">
 <h2 id="overview">Overview<a class="anchor" aria-label="anchor" href="#overview"></a>
 </h2>
 <p>An easy way to examine archaeological count data. This package provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). It allows to easily visualize count data and statistical thresholds: rank vs. abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams, etc. <strong>tabula</strong> provides methods for:</p>
diff --git a/tabula/pkgdown.yml b/tabula/pkgdown.yml
index 4f4c63ab1..72c38c62f 100644
--- a/tabula/pkgdown.yml
+++ b/tabula/pkgdown.yml
@@ -4,7 +4,7 @@ pkgdown_sha: ~
 articles:
   bibliography: bibliography.html
   diversity: diversity.html
-last_built: 2023-11-28T14:39Z
+last_built: 2023-11-28T14:50Z
 urls:
   reference: https://packages.tesselle.org/tabula/reference
   article: https://packages.tesselle.org/tabula/articles
diff --git a/tabula/reference/Rplot002.png b/tabula/reference/Rplot002.png
index 5fa34e1e8a300d64b4f4729439cdd4d2d4bc3e40..0fbc725e2e3e2b09de12223e216e14d9c9b07bd1 100644
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diff --git a/tabula/reference/bootstrap.html b/tabula/reference/bootstrap.html
index 1ec0dc95d..9bb2d969f 100644
--- a/tabula/reference/bootstrap.html
+++ b/tabula/reference/bootstrap.html
@@ -124,28 +124,28 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Bootstrap resampling</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://packages.tesselle.org/arkhe/reference/bootstrap.html" class="external-link">bootstrap</a></span><span class="op">(</span><span class="va">h</span>, f <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                  original     mean         bias      error</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         3.269200 3.280424  0.011224650 0.08204044</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.955298 2.971820  0.016521885 0.14395138</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          2.491683 2.526812  0.035128757 0.19383895</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        2.485604 2.494510  0.008905781 0.21561494</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        2.329187 2.315151 -0.014035738 0.23872709</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         3.269200 3.277468  0.008268001 0.07791396</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.955298 2.975532  0.020234070 0.15122621</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          2.491683 2.527648  0.035964818 0.20193422</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        2.485604 2.505578  0.019974086 0.21690891</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        2.329187 2.309894 -0.019293066 0.24264598</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://packages.tesselle.org/arkhe/reference/bootstrap.html" class="external-link">bootstrap</a></span><span class="op">(</span><span class="va">h</span>, f <span class="op">=</span> <span class="va">summary</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         3.036801 3.235090 3.283645 3.281269 3.332792 3.518316</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.441228 2.871436 2.975610 2.970540 3.069886 3.387301</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          1.820076 2.400101 2.542248 2.527153 2.667644 3.076234</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        1.585100 2.351945 2.503939 2.486466 2.643975 2.987453</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        1.054920 2.147735 2.339372 2.309957 2.489357 2.947994</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         2.996500 3.225490 3.281419 3.276505 3.331537 3.528705</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.470390 2.877698 2.982996 2.976349 3.079448 3.367807</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          1.696646 2.392367 2.526090 2.517395 2.659698 3.140119</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        1.540306 2.342038 2.499932 2.483194 2.644036 3.121281</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        1.039721 2.161424 2.343933 2.311562 2.490940 2.944079</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">quant</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html" class="external-link">quantile</a></span><span class="op">(</span><span class="va">x</span>, probs <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">0.50</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://packages.tesselle.org/arkhe/reference/bootstrap.html" class="external-link">bootstrap</a></span><span class="op">(</span><span class="va">h</span>, f <span class="op">=</span> <span class="va">quant</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                       25%      50%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         3.229431 3.283692</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.882662 2.984654</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          2.399893 2.544043</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        2.350143 2.496380</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        2.166085 2.355616</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         3.227563 3.286466</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.870349 2.978058</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          2.409175 2.542272</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        2.336426 2.491478</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        2.157161 2.338372</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Jackknife resampling</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://packages.tesselle.org/arkhe/reference/jackknife.html" class="external-link">jackknife</a></span><span class="op">(</span><span class="va">h</span><span class="op">)</span></span></span>
@@ -157,12 +157,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        2.329187 2.305876 -1.0023704 0.25266291</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://packages.tesselle.org/arkhe/reference/bootstrap.html" class="external-link">bootstrap</a></span><span class="op">(</span><span class="va">h</span>, f <span class="op">=</span> <span class="va">summary</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         2.866067 3.227303 3.287246 3.279038 3.334867 3.527037</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.308211 2.883436 2.983872 2.975072 3.072375 3.372968</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          1.717637 2.396265 2.542052 2.528269 2.665672 3.091073</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        1.564280 2.333501 2.494036 2.478880 2.651471 3.086374</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        1.153742 2.168155 2.344928 2.315985 2.491494 2.955980</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>                       Min.  1st Qu.   Median     Mean  3rd Qu.     Max.</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         2.9693487 3.228514 3.280549 3.276755 3.329597 3.500539</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.4545190 2.881676 2.976237 2.970362 3.071785 3.349421</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          1.6893027 2.404125 2.540315 2.523551 2.655424 3.036905</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        0.9002561 2.348662 2.491524 2.471091 2.621709 3.133622</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        0.8675632 2.141848 2.344928 2.304303 2.502661 2.963117</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/tabula/reference/jackknife.html b/tabula/reference/jackknife.html
index 32c40a3c6..b4f38d79b 100644
--- a/tabula/reference/jackknife.html
+++ b/tabula/reference/jackknife.html
@@ -119,28 +119,28 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co">## Bootstrap resampling</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://packages.tesselle.org/arkhe/reference/bootstrap.html" class="external-link">bootstrap</a></span><span class="op">(</span><span class="va">h</span>, f <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                  original     mean         bias      error</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         3.269200 3.275041  0.005841410 0.08100019</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.955298 2.965930  0.010631703 0.14936948</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          2.491683 2.517061  0.025378432 0.20015458</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        2.485604 2.494633  0.009028802 0.21942339</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        2.329187 2.312100 -0.017086138 0.26537362</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         3.269200 3.276387  0.007187177 0.07898434</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.955298 2.969622  0.014323584 0.14767427</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          2.491683 2.527443  0.035760100 0.19008643</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        2.485604 2.496768  0.011164532 0.21925152</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        2.329187 2.311561 -0.017625150 0.26586913</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://packages.tesselle.org/arkhe/reference/bootstrap.html" class="external-link">bootstrap</a></span><span class="op">(</span><span class="va">h</span>, f <span class="op">=</span> <span class="va">summary</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         2.919724 3.231574 3.282771 3.280339 3.335804 3.525117</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.362727 2.883083 2.988360 2.978060 3.080194 3.395615</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          1.897940 2.398613 2.537757 2.524738 2.655577 3.108854</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        1.468060 2.353715 2.511613 2.493106 2.651066 3.021974</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        1.153742 2.149637 2.334549 2.309251 2.498709 2.913273</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         2.978914 3.227173 3.282467 3.279539 3.332837 3.527708</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.320023 2.885541 2.986768 2.973539 3.077347 3.382234</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          1.683147 2.395898 2.533444 2.525511 2.670677 3.134431</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        1.073543 2.355150 2.511009 2.487578 2.638419 3.082724</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        1.153742 2.164958 2.338372 2.313802 2.491505 3.004767</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">quant</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html" class="external-link">quantile</a></span><span class="op">(</span><span class="va">x</span>, probs <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">0.50</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://packages.tesselle.org/arkhe/reference/bootstrap.html" class="external-link">bootstrap</a></span><span class="op">(</span><span class="va">h</span>, f <span class="op">=</span> <span class="va">quant</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                       25%      50%</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         3.222301 3.278352</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.864728 2.977705</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          2.418509 2.549336</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        2.346582 2.506681</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        2.153532 2.340683</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         3.223862 3.281929</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.877900 2.974724</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          2.398734 2.528180</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        2.335431 2.496618</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        2.161390 2.344928</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Jackknife resampling</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://packages.tesselle.org/arkhe/reference/jackknife.html" class="external-link">jackknife</a></span><span class="op">(</span><span class="va">h</span><span class="op">)</span></span></span>
@@ -153,11 +153,11 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://packages.tesselle.org/arkhe/reference/bootstrap.html" class="external-link">bootstrap</a></span><span class="op">(</span><span class="va">h</span>, f <span class="op">=</span> <span class="va">summary</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         2.942779 3.227784 3.278687 3.279983 3.338470 3.473174</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.521301 2.877166 2.979804 2.972763 3.074055 3.363443</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          1.733997 2.382691 2.522209 2.515038 2.650176 3.145394</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        1.401393 2.366126 2.513885 2.498322 2.646803 3.069173</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        1.213008 2.148366 2.331598 2.306090 2.505290 3.002004</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Altamira         2.936245 3.230904 3.285962 3.283055 3.340088 3.509117</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Cueto de la Mina 2.475108 2.879928 2.975665 2.972969 3.067305 3.337684</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Juyo          1.740512 2.402962 2.542425 2.521768 2.655274 3.082136</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> El Cierro        1.412745 2.351439 2.508167 2.485174 2.639294 3.051197</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> La Paloma        1.039721 2.161390 2.334549 2.314882 2.492804 2.961354</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/tabula/reference/resample.html b/tabula/reference/resample.html
index 7ced6c8fc..75f94a72c 100644
--- a/tabula/reference/resample.html
+++ b/tabula/reference/resample.html
@@ -117,19 +117,19 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co">## Sample observations from a multinomial distribution</span></span></span>
 <span class="r-in"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">100</span>, <span class="fl">50</span>, <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu">resample</span><span class="op">(</span><span class="va">x</span>, do <span class="op">=</span> <span class="va">median</span>, n <span class="op">=</span> <span class="fl">100</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 52.5 50.0 47.5 43.0 48.5 52.0 48.0 51.5 49.5 50.0 50.5 49.5 52.0 48.5 52.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [16] 47.5 53.0 47.0 50.5 48.0 48.5 46.5 52.0 49.0 50.0 51.0 50.0 50.0 53.0 49.0</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [31] 50.0 51.0 44.0 47.0 49.5 49.0 51.5 48.5 51.0 47.0 48.0 54.0 52.0 51.0 52.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [46] 52.0 52.5 56.0 53.5 48.5 49.0 51.5 49.5 56.0 50.5 48.0 51.0 52.0 52.0 49.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [61] 50.0 50.0 47.0 48.5 55.0 46.0 51.5 52.5 51.5 52.0 53.0 50.5 49.0 47.0 45.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 51.0 48.5 50.0 50.0 48.0 50.5 51.0 47.5 49.0 56.5 51.5 53.5 54.5 54.0 45.5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [91] 52.0 47.0 51.5 47.5 49.0 47.5 48.5 50.5 48.5 46.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 35.0 39.0 36.0 33.0 34.0 40.0 32.0 34.5 36.5 37.0 36.0 41.5 38.5 40.5 34.0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [16] 36.5 35.5 36.0 36.5 35.0 36.5 37.0 42.5 36.0 31.5 38.0 41.5 37.0 32.5 31.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [31] 38.0 35.5 35.0 36.0 36.0 36.5 42.5 30.5 36.0 32.5 35.5 36.5 37.0 38.5 35.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [46] 32.0 39.0 38.0 40.5 34.0 33.0 40.5 35.0 38.5 36.5 36.5 32.5 39.5 34.5 32.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [61] 39.0 37.5 38.5 34.5 36.5 44.0 41.0 36.0 37.5 32.5 33.0 36.5 31.5 36.0 38.0</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 34.5 39.0 34.5 34.0 37.0 36.5 38.0 32.0 33.5 35.5 34.0 38.0 32.0 37.0 32.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [91] 35.0 36.5 39.0 36.5 35.0 35.0 37.0 40.5 34.5 36.0</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co">## Estimate the 25th, 50th and 95th percentiles</span></span></span>
 <span class="r-in"><span><span class="va">quant</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="op">{</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html" class="external-link">quantile</a></span><span class="op">(</span><span class="va">x</span>, probs <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.25</span>, <span class="fl">0.50</span>, <span class="fl">0.75</span><span class="op">)</span><span class="op">)</span> <span class="op">}</span></span></span>
 <span class="r-in"><span><span class="fu">resample</span><span class="op">(</span><span class="va">x</span>, n <span class="op">=</span> <span class="fl">100</span>, do <span class="op">=</span> <span class="va">median</span>, f <span class="op">=</span> <span class="va">quant</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>  25%  50%  75% </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 48.5 50.0 52.0 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 34.5 36.0 38.0 </span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/tabula/reference/simulate-1.png b/tabula/reference/simulate-1.png
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diff --git a/tabula/search.json b/tabula/search.json
index 9387accad..5231e29a2 100644
--- a/tabula/search.json
+++ b/tabula/search.json
@@ -1 +1 @@
-[{"path":[]},{"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":""},{"path":[]},{"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"},{"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":""},{"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":""},{"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":""},{"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":""},{"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":""},{"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. open 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) “number species present collection containing specified number individuals” (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. taxa 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’s 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)"},{"path":[]},{"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":""},{"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’s 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":""},{"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. \\(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":""},{"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’s 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":""},{"path":[]},{"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":""},{"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))"},{"path":[]},{"path":[]},{"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. abundance 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(), matrigraph(). 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_. Université Bordeaux Montaigne, Pessac,   France. doi:10.5281/zenodo.1489944   <https://doi.org/10.5281/zenodo.1489944>, R package version 3.0.1,   <https://packages.tesselle.org/tabula/>.  This package is a part of the tesselle project <https://www.tesselle.org>."},{"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\")"},{"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()"},{"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":""},{"path":[]},{"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—first formulated @petrie1899—allowed 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":""},{"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. ade4 [@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. abundance plots, heatmaps, @ford1962 @bertin1977 diagrams.","code":""},{"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":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Composition Index — CompositionIndex-class","title":"Composition Index — CompositionIndex-class","text":"S4 class represent composition measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Composition Index — CompositionIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Composition Index — CompositionIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Index — DiversityIndex-class","title":"Diversity Index — DiversityIndex-class","text":"S4 class represent diversity measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Diversity Index — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Diversity Index — DiversityIndex-class","text":"class inherits base numeric.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Diversity Index — DiversityIndex-class","text":"code snippets , x DiversityIndex object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Index — DiversityIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Evenness Index — EvennessIndex-class","title":"Evenness Index — EvennessIndex-class","text":"S4 class represent evenness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Evenness Index — EvennessIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Evenness Index — EvennessIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity Index — HeterogeneityIndex-class","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"S4 class represent heterogeneity measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Index — RarefactionIndex-class","title":"Rarefaction Index — RarefactionIndex-class","text":"S4 class represent rarefaction measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Rarefaction Index — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rarefaction Index — RarefactionIndex-class","text":"class inherits base matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Index — RarefactionIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness Index — RichnessIndex-class","title":"Richness Index — RichnessIndex-class","text":"S4 class represent richness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Richness Index — RichnessIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness Index — RichnessIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":null,"dir":"Reference","previous_headings":"","what":"Birds Species and Abundances — aves","title":"Birds Species and Abundances — aves","text":"dataset birds species abundances managed unmanaged areas along River Wye (UK).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Birds Species and Abundances — aves","text":"","code":"aves"},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Birds Species and Abundances — aves","text":"data.frame 2 rows 26 variables (bird species).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Birds Species and Abundances — aves","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Estimation — bootstrap_diversity","title":"Bootstrap Estimation — bootstrap_diversity","text":"Samples randomly elements object replacement.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Estimation — bootstrap_diversity","text":"","code":"# S4 method for DiversityIndex bootstrap(object, n = 1000, f = NULL)"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Estimation — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Estimation — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Estimation — bootstrap_diversity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Estimation — 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 3.280424  0.011224650 0.08204044 #> Cueto de la Mina 2.955298 2.971820  0.016521885 0.14395138 #> El Juyo          2.491683 2.526812  0.035128757 0.19383895 #> El Cierro        2.485604 2.494510  0.008905781 0.21561494 #> La Paloma        2.329187 2.315151 -0.014035738 0.23872709  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         3.036801 3.235090 3.283645 3.281269 3.332792 3.518316 #> Cueto de la Mina 2.441228 2.871436 2.975610 2.970540 3.069886 3.387301 #> El Juyo          1.820076 2.400101 2.542248 2.527153 2.667644 3.076234 #> El Cierro        1.585100 2.351945 2.503939 2.486466 2.643975 2.987453 #> La Paloma        1.054920 2.147735 2.339372 2.309957 2.489357 2.947994  quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant) #>                       25%      50% #> Altamira         3.229431 3.283692 #> Cueto de la Mina 2.882662 2.984654 #> El Juyo          2.399893 2.544043 #> El Cierro        2.350143 2.496380 #> La Paloma        2.166085 2.355616  ## 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         2.866067 3.227303 3.287246 3.279038 3.334867 3.527037 #> Cueto de la Mina 2.308211 2.883436 2.983872 2.975072 3.072375 3.372968 #> El Juyo          1.717637 2.396265 2.542052 2.528269 2.665672 3.091073 #> El Cierro        1.564280 2.333501 2.494036 2.478880 2.651471 3.086374 #> La Paloma        1.153742 2.168155 2.344928 2.315985 2.491494 2.955980"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":null,"dir":"Reference","previous_headings":"","what":"Early Magdalenian Engraved Bones — cantabria","title":"Early Magdalenian Engraved Bones — cantabria","text":"dataset design elements engraved bones Cantabrian Spain.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Early Magdalenian Engraved Bones — cantabria","text":"","code":"cantabria"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Early Magdalenian Engraved Bones — cantabria","text":"data.frame 5 rows 44 variables (designs).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Early Magdalenian Engraved Bones — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity and Evenness — heterogeneity","title":"Heterogeneity and Evenness — heterogeneity","text":"heterogeneity() returns heterogeneity dominance index. evenness() returns evenness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heterogeneity and Evenness — 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, ...)"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heterogeneity and Evenness — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heterogeneity and Evenness — heterogeneity","text":"heterogeneity() returns HeterogeneityIndex object. evenness() returns EvennessIndex object. index_*() return numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Heterogeneity and Evenness — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Heterogeneity and Evenness — heterogeneity","text":"Ramanujan approximation used \\(x!\\) computation \\(x > 170\\).","code":""},{"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 — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Heterogeneity and Evenness — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity and Evenness — heterogeneity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heterogeneity and Evenness — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":null,"dir":"Reference","previous_headings":"","what":"Jackknife Estimation — jackknife_diversity","title":"Jackknife Estimation — jackknife_diversity","text":"Jackknife Estimation","code":""},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Jackknife Estimation — jackknife_diversity","text":"","code":"# S4 method for DiversityIndex jackknife(object, f = NULL)"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Jackknife Estimation — jackknife_diversity","text":"object R object (typically DiversityIndex object). f function takes single numeric vector (leave-one-values ) argument.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Jackknife Estimation — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Jackknife Estimation — jackknife_diversity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Jackknife Estimation — 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 3.275041  0.005841410 0.08100019 #> Cueto de la Mina 2.955298 2.965930  0.010631703 0.14936948 #> El Juyo          2.491683 2.517061  0.025378432 0.20015458 #> El Cierro        2.485604 2.494633  0.009028802 0.21942339 #> La Paloma        2.329187 2.312100 -0.017086138 0.26537362  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         2.919724 3.231574 3.282771 3.280339 3.335804 3.525117 #> Cueto de la Mina 2.362727 2.883083 2.988360 2.978060 3.080194 3.395615 #> El Juyo          1.897940 2.398613 2.537757 2.524738 2.655577 3.108854 #> El Cierro        1.468060 2.353715 2.511613 2.493106 2.651066 3.021974 #> La Paloma        1.153742 2.149637 2.334549 2.309251 2.498709 2.913273  quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant) #>                       25%      50% #> Altamira         3.222301 3.278352 #> Cueto de la Mina 2.864728 2.977705 #> El Juyo          2.418509 2.549336 #> El Cierro        2.346582 2.506681 #> La Paloma        2.153532 2.340683  ## 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         2.942779 3.227784 3.278687 3.279983 3.338470 3.473174 #> Cueto de la Mina 2.521301 2.877166 2.979804 2.972763 3.074055 3.363443 #> El Juyo          1.733997 2.382691 2.522209 2.515038 2.650176 3.145394 #> El Cierro        1.401393 2.366126 2.513885 2.498322 2.646803 3.069173 #> La Paloma        1.213008 2.148366 2.331598 2.306090 2.505290 3.002004"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrigraph — matrigraph","title":"Matrigraph — matrigraph","text":"matrigraph() produces heatmap highlighting deviations independence. pvi() computes cell numeric matrix percentage column theoretical independence value.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrigraph — 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, ...)"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrigraph — 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? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrigraph — matrigraph","text":"matrigraph() called side-effects: results graphic displayed (invisibly returns object). pvi() returns numeric matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Matrigraph — matrigraph","text":"PVI (french \"pourcentages de valeur d'indépendance\") 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":""},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Matrigraph — matrigraph","text":"Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Matrigraph — matrigraph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrigraph — 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))"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or Set Parts of an Object — mutators","title":"Get or Set Parts of an Object — mutators","text":"Getters setters extract replace parts object.","code":""},{"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 — 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)"},{"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 — mutators","text":"object, x R object get set element(s).","code":""},{"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 — mutators","text":"labels() returns suitable set labels object use printing plotting.","code":""},{"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 — mutators","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Co-Occurrence — occurrence","title":"Co-Occurrence — occurrence","text":"Co-Occurrence","code":""},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Co-Occurrence — occurrence","text":"","code":"occurrence(object, ...)  # S4 method for matrix occurrence(object)  # S4 method for data.frame occurrence(object)"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Co-Occurrence — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Co-Occurrence — occurrence","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Co-Occurrence — occurrence","text":"co-occurrence matrix symmetric matrix zeros main diagonal, works many times pairs taxa/types occur together least one sample.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Co-Occurrence — occurrence","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Co-Occurrence — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":null,"dir":"Reference","previous_headings":"","what":"Bertin Diagram — plot_bertin","title":"Bertin Diagram — plot_bertin","text":"Plots Bertin diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bertin Diagram — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bertin Diagram — 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? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bertin Diagram — plot_bertin","text":"plot_bertin() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"bertin-matrix","dir":"Reference","previous_headings":"","what":"Bertin Matrix","title":"Bertin Diagram — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bertin Diagram — plot_bertin","text":"Bertin, J. (1977). La graphique et le traitement graphique de l'information. Paris: Flammarion. Nouvelle Bibliothèque 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bertin Diagram — plot_bertin","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bertin Diagram — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":null,"dir":"Reference","previous_headings":"","what":"Dice-Leraas Diagram — plot_diceleraas","title":"Dice-Leraas Diagram — plot_diceleraas","text":"Plots Dice-Leraas diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dice-Leraas Diagram — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dice-Leraas Diagram — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dice-Leraas Diagram — plot_diceleraas","text":"plot_diceleraas() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dice-Leraas Diagram — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dice-Leraas Diagram — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dice-Leraas Diagram — plot_diceleraas","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dice-Leraas Diagram — plot_diceleraas","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Plot a Dice-Leraas diagram plot_diceleraas(compiegne)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Plot — plot_diversity","title":"Diversity Plot — plot_diversity","text":"Diversity Plot","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Plot — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Plot — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Plot — plot_diversity","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Plot — plot_diversity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Plot — 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)  # }"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":null,"dir":"Reference","previous_headings":"","what":"Ford Diagram — plot_ford","title":"Ford Diagram — plot_ford","text":"Plots Ford (battleship curve) diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ford Diagram — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ford Diagram — 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? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ford Diagram — plot_ford","text":"plot_ford() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Ford Diagram — plot_ford","text":"Ford, J. . (1962). quantitative method deriving cultural chronology. Washington, DC: Pan American Union. Technical manual 1.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ford Diagram — plot_ford","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ford Diagram — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap — plot_heatmap","title":"Heatmap — plot_heatmap","text":"Plots heatmap.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap — 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? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed?","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap — plot_heatmap","text":"plot_heatmap() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heatmap — plot_heatmap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heatmap — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix Plot — plot_matrix","title":"Matrix Plot — plot_matrix","text":"Matrix Plot","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix Plot — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix Plot — 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? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed? asp length-one numeric vector, giving aspect ratio \\(y/x\\). ... arguments passed panel.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Rank Plot — plot_rank","title":"Rank Plot — plot_rank","text":"Plots rank vs relative abundance diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rank Plot — 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\"),   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rank Plot — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rank Plot — plot_rank","text":"plot_rank() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rank Plot — plot_rank","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rank Plot — plot_rank","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rank Plot — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Plot — plot_rarefaction","title":"Rarefaction Plot — plot_rarefaction","text":"Rarefaction Plot","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction Plot — 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\"),   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction Plot — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction Plot — plot_rarefaction","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Plot — plot_rarefaction","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction Plot — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":null,"dir":"Reference","previous_headings":"","what":"Spot Plot — plot_spot","title":"Spot Plot — plot_spot","text":"Plots spot matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spot Plot — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spot Plot — 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? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed?","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spot Plot — plot_spot","text":"plot_spot() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spot Plot — plot_spot","text":"spot matrix can considered variant Bertin diagram data first transformed relative frequencies.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Spot Plot — plot_spot","text":"Adapted Dan Gopstein's original idea.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spot Plot — plot_spot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spot Plot — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":null,"dir":"Reference","previous_headings":"","what":"Pueblo IV Period Ceramics — pueblo","title":"Pueblo IV Period Ceramics — pueblo","text":"dataset ceramic counts Zuni region.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pueblo IV Period Ceramics — pueblo","text":"","code":"pueblo"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pueblo IV Period Ceramics — pueblo","text":"data.frame 9 rows 5 variables (compositional groups).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Pueblo IV Period Ceramics — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction — rarefaction","title":"Rarefaction — rarefaction","text":"Rarefaction","code":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction — 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, ...)"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction — rarefaction","text":"rarefaction() returns RarefactionIndex object. index_*() return numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"rarefaction-measures","dir":"Reference","previous_headings":"","what":"Rarefaction Measures","title":"Rarefaction — rarefaction","text":"following rarefaction measures available count data: baxter Baxter's rarefaction. hurlbert Hurlbert's unbiased estimate Sander's rarefaction.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rarefaction — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rarefaction — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction — rarefaction","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. arkhe bootstrap, jackknife, remove_NA, remove_zero, replace_NA, replace_zero","code":""},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample — resample","title":"Resample — resample","text":"Simulates observations multinomial distribution.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample — resample","text":"","code":"resample(object, ...)  # S4 method for numeric resample(object, do, n, size = sum(object), ..., f = NULL)"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample — resample","text":"f NULL, resample() returns n values . Else, returns result f applied n values .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample — resample","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resample — resample","text":"","code":"## Sample observations from a multinomial distribution x <- sample(1:100, 50, TRUE) resample(x, do = median, n = 100) #>   [1] 52.5 50.0 47.5 43.0 48.5 52.0 48.0 51.5 49.5 50.0 50.5 49.5 52.0 48.5 52.5 #>  [16] 47.5 53.0 47.0 50.5 48.0 48.5 46.5 52.0 49.0 50.0 51.0 50.0 50.0 53.0 49.0 #>  [31] 50.0 51.0 44.0 47.0 49.5 49.0 51.5 48.5 51.0 47.0 48.0 54.0 52.0 51.0 52.5 #>  [46] 52.0 52.5 56.0 53.5 48.5 49.0 51.5 49.5 56.0 50.5 48.0 51.0 52.0 52.0 49.5 #>  [61] 50.0 50.0 47.0 48.5 55.0 46.0 51.5 52.5 51.5 52.0 53.0 50.5 49.0 47.0 45.5 #>  [76] 51.0 48.5 50.0 50.0 48.0 50.5 51.0 47.5 49.0 56.5 51.5 53.5 54.5 54.0 45.5 #>  [91] 52.0 47.0 51.5 47.5 49.0 47.5 48.5 50.5 48.5 46.5  ## 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%  #> 48.5 50.0 52.0"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness — richness","title":"Richness — richness","text":"richness() returns sample richness. composition() returns asymptotic species richness.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Richness — 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, ...)"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Richness — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Richness — richness","text":"richness() returns RichnessIndex object. composition() returns CompositionIndex object. index_*() return numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Richness — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"richness-measures","dir":"Reference","previous_headings":"","what":"Richness Measures","title":"Richness — richness","text":"following richness measures available count data: count Returns number observed taxa/types. margalef Margalef richness index. menhinick Menhinick richness index.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"asymptotic-species-richness","dir":"Reference","previous_headings":"","what":"Asymptotic Species Richness","title":"Richness — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Richness — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness — richness","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Richness — 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"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":null,"dir":"Reference","previous_headings":"","what":"Seriograph — seriograph","title":"Seriograph — seriograph","text":"seriograph() produces Ford diagram highlighting relationships rows columns. eppm() computes cell numeric matrix positive difference column mean percentage.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Seriograph — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Seriograph — 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? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Seriograph — seriograph","text":"seriograph() called side-effects: results graphic displayed (invisibly returns object). eppm() returns numeric matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Seriograph — seriograph","text":"positive difference column mean percentage (french \"écart 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":""},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Seriograph — seriograph","text":"Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Seriograph — seriograph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seriograph — 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))"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Similarity — similarity","title":"Similarity — similarity","text":"Similarity","code":""},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Similarity — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Similarity — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Similarity — similarity","text":"similarity() returns stats::dist object. index_*() return numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Similarity — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Similarity — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Similarity — similarity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Similarity — 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"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Measure Diversity by Comparing to Simulated Assemblages — simulate","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"Measure Diversity Comparing Simulated Assemblages","code":""},{"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 — 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\") )"},{"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 — 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":""},{"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 — simulate","text":"Returns DiversityIndex object.","code":""},{"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 — 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":""},{"path":[]},{"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 — simulate","text":"N. Frerebeau","code":""},{"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 — 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)  # }"},{"path":"https://packages.tesselle.org/tabula/reference/tabula-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in tabula — tabula-deprecated","title":"Deprecated Functions in tabula — tabula-deprecated","text":"functions still work removed (defunct) next version.","code":""},{"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 — tabula-package","title":"tabula: Analysis and Visualization of Archaeological Count Data — 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":""},{"path":[]},{"path":[]},{"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 — tabula-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Archéosciences Bordeaux (UMR 6034) Maison de l'Archéologie Université Bordeaux Montaigne 33607 Pessac cedex France","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Test — test_diversity","title":"Diversity Test — test_diversity","text":"Compares Shannon diversity samples.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Test — 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\", ...)"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Test — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Test — test_diversity","text":"numeric  matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diversity Test — test_diversity","text":"test produces two sided pairwise comparisons: returns matrix adjusted \\(p\\) values.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diversity Test — test_diversity","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Test — test_diversity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Test — 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"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":null,"dir":"Reference","previous_headings":"","what":"Turnover — turnover","title":"Turnover — turnover","text":"Returns degree turnover taxa composition along gradient transect.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turnover — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turnover — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turnover — turnover","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Turnover — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Turnover — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Turnover — turnover","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turnover — 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"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":null,"dir":"Reference","previous_headings":"","what":"Trees Incidences — woodland","title":"Trees Incidences — woodland","text":"dataset presence absence trees six (10 x 10 m) quadarts along transect deciduous woodland.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trees Incidences — woodland","text":"","code":"woodland"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Trees Incidences — woodland","text":"data.frame 6 rows (quadarts) 6 variables (tree species).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Trees Incidences — woodland","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]}]
+[{"path":[]},{"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":""},{"path":[]},{"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"},{"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":""},{"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":""},{"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":""},{"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":""},{"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":""},{"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. open 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) “number species present collection containing specified number individuals” (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. taxa 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’s 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)"},{"path":[]},{"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":""},{"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’s 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":""},{"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. \\(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":""},{"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’s 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":""},{"path":[]},{"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":""},{"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))"},{"path":[]},{"path":[]},{"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. abundance 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(), matrigraph(). 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_. Université Bordeaux Montaigne, Pessac,   France. doi:10.5281/zenodo.1489944   <https://doi.org/10.5281/zenodo.1489944>, R package version 3.0.1,   <https://packages.tesselle.org/tabula/>.  This package is a part of the tesselle project <https://www.tesselle.org>."},{"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\")"},{"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()"},{"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":""},{"path":[]},{"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—first formulated @petrie1899—allowed 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":""},{"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. ade4 [@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. abundance plots, heatmaps, @ford1962 @bertin1977 diagrams.","code":""},{"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":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Composition Index — CompositionIndex-class","title":"Composition Index — CompositionIndex-class","text":"S4 class represent composition measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Composition Index — CompositionIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/CompositionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Composition Index — CompositionIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Index — DiversityIndex-class","title":"Diversity Index — DiversityIndex-class","text":"S4 class represent diversity measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Diversity Index — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Diversity Index — DiversityIndex-class","text":"class inherits base numeric.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Diversity Index — DiversityIndex-class","text":"code snippets , x DiversityIndex object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/DiversityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Index — DiversityIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Evenness Index — EvennessIndex-class","title":"Evenness Index — EvennessIndex-class","text":"S4 class represent evenness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Evenness Index — EvennessIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/EvennessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Evenness Index — EvennessIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity Index — HeterogeneityIndex-class","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"S4 class represent heterogeneity measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/HeterogeneityIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity Index — HeterogeneityIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Index — RarefactionIndex-class","title":"Rarefaction Index — RarefactionIndex-class","text":"S4 class represent rarefaction measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Rarefaction Index — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rarefaction Index — RarefactionIndex-class","text":"class inherits base matrix.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/RarefactionIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Index — RarefactionIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness Index — RichnessIndex-class","title":"Richness Index — RichnessIndex-class","text":"S4 class represent richness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Richness Index — RichnessIndex-class","text":"class inherits DiversityIndex.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/RichnessIndex-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness Index — RichnessIndex-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":null,"dir":"Reference","previous_headings":"","what":"Birds Species and Abundances — aves","title":"Birds Species and Abundances — aves","text":"dataset birds species abundances managed unmanaged areas along River Wye (UK).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Birds Species and Abundances — aves","text":"","code":"aves"},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Birds Species and Abundances — aves","text":"data.frame 2 rows 26 variables (bird species).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/aves.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Birds Species and Abundances — aves","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Estimation — bootstrap_diversity","title":"Bootstrap Estimation — bootstrap_diversity","text":"Samples randomly elements object replacement.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Estimation — bootstrap_diversity","text":"","code":"# S4 method for DiversityIndex bootstrap(object, n = 1000, f = NULL)"},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Estimation — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap Estimation — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap Estimation — bootstrap_diversity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap Estimation — 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 3.277468  0.008268001 0.07791396 #> Cueto de la Mina 2.955298 2.975532  0.020234070 0.15122621 #> El Juyo          2.491683 2.527648  0.035964818 0.20193422 #> El Cierro        2.485604 2.505578  0.019974086 0.21690891 #> La Paloma        2.329187 2.309894 -0.019293066 0.24264598  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         2.996500 3.225490 3.281419 3.276505 3.331537 3.528705 #> Cueto de la Mina 2.470390 2.877698 2.982996 2.976349 3.079448 3.367807 #> El Juyo          1.696646 2.392367 2.526090 2.517395 2.659698 3.140119 #> El Cierro        1.540306 2.342038 2.499932 2.483194 2.644036 3.121281 #> La Paloma        1.039721 2.161424 2.343933 2.311562 2.490940 2.944079  quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant) #>                       25%      50% #> Altamira         3.227563 3.286466 #> Cueto de la Mina 2.870349 2.978058 #> El Juyo          2.409175 2.542272 #> El Cierro        2.336426 2.491478 #> La Paloma        2.157161 2.338372  ## 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         2.9693487 3.228514 3.280549 3.276755 3.329597 3.500539 #> Cueto de la Mina 2.4545190 2.881676 2.976237 2.970362 3.071785 3.349421 #> El Juyo          1.6893027 2.404125 2.540315 2.523551 2.655424 3.036905 #> El Cierro        0.9002561 2.348662 2.491524 2.471091 2.621709 3.133622 #> La Paloma        0.8675632 2.141848 2.344928 2.304303 2.502661 2.963117"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":null,"dir":"Reference","previous_headings":"","what":"Early Magdalenian Engraved Bones — cantabria","title":"Early Magdalenian Engraved Bones — cantabria","text":"dataset design elements engraved bones Cantabrian Spain.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Early Magdalenian Engraved Bones — cantabria","text":"","code":"cantabria"},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Early Magdalenian Engraved Bones — cantabria","text":"data.frame 5 rows 44 variables (designs).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/cantabria.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Early Magdalenian Engraved Bones — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":null,"dir":"Reference","previous_headings":"","what":"Heterogeneity and Evenness — heterogeneity","title":"Heterogeneity and Evenness — heterogeneity","text":"heterogeneity() returns heterogeneity dominance index. evenness() returns evenness measure.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heterogeneity and Evenness — 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, ...)"},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heterogeneity and Evenness — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heterogeneity and Evenness — heterogeneity","text":"heterogeneity() returns HeterogeneityIndex object. evenness() returns EvennessIndex object. index_*() return numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Heterogeneity and Evenness — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Heterogeneity and Evenness — heterogeneity","text":"Ramanujan approximation used \\(x!\\) computation \\(x > 170\\).","code":""},{"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 — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Heterogeneity and Evenness — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heterogeneity and Evenness — heterogeneity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/heterogeneity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heterogeneity and Evenness — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":null,"dir":"Reference","previous_headings":"","what":"Jackknife Estimation — jackknife_diversity","title":"Jackknife Estimation — jackknife_diversity","text":"Jackknife Estimation","code":""},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Jackknife Estimation — jackknife_diversity","text":"","code":"# S4 method for DiversityIndex jackknife(object, f = NULL)"},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Jackknife Estimation — jackknife_diversity","text":"object R object (typically DiversityIndex object). f function takes single numeric vector (leave-one-values ) argument.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Jackknife Estimation — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Jackknife Estimation — jackknife_diversity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/jackknife.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Jackknife Estimation — 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 3.276387  0.007187177 0.07898434 #> Cueto de la Mina 2.955298 2.969622  0.014323584 0.14767427 #> El Juyo          2.491683 2.527443  0.035760100 0.19008643 #> El Cierro        2.485604 2.496768  0.011164532 0.21925152 #> La Paloma        2.329187 2.311561 -0.017625150 0.26586913  bootstrap(h, f = summary) #>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max. #> Altamira         2.978914 3.227173 3.282467 3.279539 3.332837 3.527708 #> Cueto de la Mina 2.320023 2.885541 2.986768 2.973539 3.077347 3.382234 #> El Juyo          1.683147 2.395898 2.533444 2.525511 2.670677 3.134431 #> El Cierro        1.073543 2.355150 2.511009 2.487578 2.638419 3.082724 #> La Paloma        1.153742 2.164958 2.338372 2.313802 2.491505 3.004767  quant <- function(x) quantile(x, probs = c(0.25, 0.50)) bootstrap(h, f = quant) #>                       25%      50% #> Altamira         3.223862 3.281929 #> Cueto de la Mina 2.877900 2.974724 #> El Juyo          2.398734 2.528180 #> El Cierro        2.335431 2.496618 #> La Paloma        2.161390 2.344928  ## 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         2.936245 3.230904 3.285962 3.283055 3.340088 3.509117 #> Cueto de la Mina 2.475108 2.879928 2.975665 2.972969 3.067305 3.337684 #> El Juyo          1.740512 2.402962 2.542425 2.521768 2.655274 3.082136 #> El Cierro        1.412745 2.351439 2.508167 2.485174 2.639294 3.051197 #> La Paloma        1.039721 2.161390 2.334549 2.314882 2.492804 2.961354"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrigraph — matrigraph","title":"Matrigraph — matrigraph","text":"matrigraph() produces heatmap highlighting deviations independence. pvi() computes cell numeric matrix percentage column theoretical independence value.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrigraph — 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, ...)"},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrigraph — 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? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Matrigraph — matrigraph","text":"matrigraph() called side-effects: results graphic displayed (invisibly returns object). pvi() returns numeric matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Matrigraph — matrigraph","text":"PVI (french \"pourcentages de valeur d'indépendance\") 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":""},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Matrigraph — matrigraph","text":"Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Matrigraph — matrigraph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/matrigraph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Matrigraph — 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))"},{"path":"https://packages.tesselle.org/tabula/reference/mutators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or Set Parts of an Object — mutators","title":"Get or Set Parts of an Object — mutators","text":"Getters setters extract replace parts object.","code":""},{"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 — 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)"},{"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 — mutators","text":"object, x R object get set element(s).","code":""},{"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 — mutators","text":"labels() returns suitable set labels object use printing plotting.","code":""},{"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 — mutators","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":null,"dir":"Reference","previous_headings":"","what":"Co-Occurrence — occurrence","title":"Co-Occurrence — occurrence","text":"Co-Occurrence","code":""},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Co-Occurrence — occurrence","text":"","code":"occurrence(object, ...)  # S4 method for matrix occurrence(object)  # S4 method for data.frame occurrence(object)"},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Co-Occurrence — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Co-Occurrence — occurrence","text":"stats::dist object.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Co-Occurrence — occurrence","text":"co-occurrence matrix symmetric matrix zeros main diagonal, works many times pairs taxa/types occur together least one sample.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Co-Occurrence — occurrence","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/occurrence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Co-Occurrence — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":null,"dir":"Reference","previous_headings":"","what":"Bertin Diagram — plot_bertin","title":"Bertin Diagram — plot_bertin","text":"Plots Bertin diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bertin Diagram — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bertin Diagram — 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? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bertin Diagram — plot_bertin","text":"plot_bertin() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"bertin-matrix","dir":"Reference","previous_headings":"","what":"Bertin Matrix","title":"Bertin Diagram — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Bertin Diagram — plot_bertin","text":"Bertin, J. (1977). La graphique et le traitement graphique de l'information. Paris: Flammarion. Nouvelle Bibliothèque 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bertin Diagram — plot_bertin","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_bertin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bertin Diagram — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":null,"dir":"Reference","previous_headings":"","what":"Dice-Leraas Diagram — plot_diceleraas","title":"Dice-Leraas Diagram — plot_diceleraas","text":"Plots Dice-Leraas diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dice-Leraas Diagram — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dice-Leraas Diagram — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dice-Leraas Diagram — plot_diceleraas","text":"plot_diceleraas() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dice-Leraas Diagram — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dice-Leraas Diagram — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Dice-Leraas Diagram — plot_diceleraas","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diceleraas.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dice-Leraas Diagram — plot_diceleraas","text":"","code":"## Data from Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Plot a Dice-Leraas diagram plot_diceleraas(compiegne)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Plot — plot_diversity","title":"Diversity Plot — plot_diversity","text":"Diversity Plot","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Plot — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Plot — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Plot — plot_diversity","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Plot — plot_diversity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_diversity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Plot — 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)  # }"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":null,"dir":"Reference","previous_headings":"","what":"Ford Diagram — plot_ford","title":"Ford Diagram — plot_ford","text":"Plots Ford (battleship curve) diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ford Diagram — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ford Diagram — 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? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ford Diagram — plot_ford","text":"plot_ford() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Ford Diagram — plot_ford","text":"Ford, J. . (1962). quantitative method deriving cultural chronology. Washington, DC: Pan American Union. Technical manual 1.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Ford Diagram — plot_ford","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_ford.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Ford Diagram — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap — plot_heatmap","title":"Heatmap — plot_heatmap","text":"Plots heatmap.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap — 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? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed?","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap — plot_heatmap","text":"plot_heatmap() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Heatmap — plot_heatmap","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heatmap — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Matrix Plot — plot_matrix","title":"Matrix Plot — plot_matrix","text":"Matrix Plot","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Matrix Plot — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Matrix Plot — 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? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed? asp length-one numeric vector, giving aspect ratio \\(y/x\\). ... arguments passed panel.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Rank Plot — plot_rank","title":"Rank Plot — plot_rank","text":"Plots rank vs relative abundance diagram.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rank Plot — 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\"),   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rank Plot — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rank Plot — plot_rank","text":"plot_rank() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rank Plot — plot_rank","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rank Plot — plot_rank","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rank Plot — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction Plot — plot_rarefaction","title":"Rarefaction Plot — plot_rarefaction","text":"Rarefaction Plot","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction Plot — 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\"),   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction Plot — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction Plot — plot_rarefaction","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction Plot — plot_rarefaction","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction Plot — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":null,"dir":"Reference","previous_headings":"","what":"Spot Plot — plot_spot","title":"Spot Plot — plot_spot","text":"Plots spot matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spot Plot — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spot Plot — 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? omit labels abut overlap previously drawn labels. legend logical scalar: legend displayed?","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spot Plot — plot_spot","text":"plot_spot() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spot Plot — plot_spot","text":"spot matrix can considered variant Bertin diagram data first transformed relative frequencies.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Spot Plot — plot_spot","text":"Adapted Dan Gopstein's original idea.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Spot Plot — plot_spot","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/plot_spot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spot Plot — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":null,"dir":"Reference","previous_headings":"","what":"Pueblo IV Period Ceramics — pueblo","title":"Pueblo IV Period Ceramics — pueblo","text":"dataset ceramic counts Zuni region.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pueblo IV Period Ceramics — pueblo","text":"","code":"pueblo"},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pueblo IV Period Ceramics — pueblo","text":"data.frame 9 rows 5 variables (compositional groups).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/pueblo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Pueblo IV Period Ceramics — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Rarefaction — rarefaction","title":"Rarefaction — rarefaction","text":"Rarefaction","code":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rarefaction — 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, ...)"},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rarefaction — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rarefaction — rarefaction","text":"rarefaction() returns RarefactionIndex object. index_*() return numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"rarefaction-measures","dir":"Reference","previous_headings":"","what":"Rarefaction Measures","title":"Rarefaction — rarefaction","text":"following rarefaction measures available count data: baxter Baxter's rarefaction. hurlbert Hurlbert's unbiased estimate Sander's rarefaction.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rarefaction — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rarefaction — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rarefaction — rarefaction","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/rarefaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rarefaction — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. arkhe bootstrap, jackknife, remove_NA, remove_zero, replace_NA, replace_zero","code":""},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample — resample","title":"Resample — resample","text":"Simulates observations multinomial distribution.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample — resample","text":"","code":"resample(object, ...)  # S4 method for numeric resample(object, do, n, size = sum(object), ..., f = NULL)"},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample — resample","text":"f NULL, resample() returns n values . Else, returns result f applied n values .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample — resample","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/resample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resample — resample","text":"","code":"## Sample observations from a multinomial distribution x <- sample(1:100, 50, TRUE) resample(x, do = median, n = 100) #>   [1] 35.0 39.0 36.0 33.0 34.0 40.0 32.0 34.5 36.5 37.0 36.0 41.5 38.5 40.5 34.0 #>  [16] 36.5 35.5 36.0 36.5 35.0 36.5 37.0 42.5 36.0 31.5 38.0 41.5 37.0 32.5 31.5 #>  [31] 38.0 35.5 35.0 36.0 36.0 36.5 42.5 30.5 36.0 32.5 35.5 36.5 37.0 38.5 35.5 #>  [46] 32.0 39.0 38.0 40.5 34.0 33.0 40.5 35.0 38.5 36.5 36.5 32.5 39.5 34.5 32.5 #>  [61] 39.0 37.5 38.5 34.5 36.5 44.0 41.0 36.0 37.5 32.5 33.0 36.5 31.5 36.0 38.0 #>  [76] 34.5 39.0 34.5 34.0 37.0 36.5 38.0 32.0 33.5 35.5 34.0 38.0 32.0 37.0 32.5 #>  [91] 35.0 36.5 39.0 36.5 35.0 35.0 37.0 40.5 34.5 36.0  ## 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%  #> 34.5 36.0 38.0"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":null,"dir":"Reference","previous_headings":"","what":"Richness — richness","title":"Richness — richness","text":"richness() returns sample richness. composition() returns asymptotic species richness.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Richness — 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, ...)"},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Richness — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Richness — richness","text":"richness() returns RichnessIndex object. composition() returns CompositionIndex object. index_*() return numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Richness — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"richness-measures","dir":"Reference","previous_headings":"","what":"Richness Measures","title":"Richness — richness","text":"following richness measures available count data: count Returns number observed taxa/types. margalef Margalef richness index. menhinick Menhinick richness index.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"asymptotic-species-richness","dir":"Reference","previous_headings":"","what":"Asymptotic Species Richness","title":"Richness — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Richness — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Richness — richness","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/richness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Richness — 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"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":null,"dir":"Reference","previous_headings":"","what":"Seriograph — seriograph","title":"Seriograph — seriograph","text":"seriograph() produces Ford diagram highlighting relationships rows columns. eppm() computes cell numeric matrix positive difference column mean percentage.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Seriograph — 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,   ... )"},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Seriograph — 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? omit labels abut overlap previously drawn labels.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Seriograph — seriograph","text":"seriograph() called side-effects: results graphic displayed (invisibly returns object). eppm() returns numeric matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Seriograph — seriograph","text":"positive difference column mean percentage (french \"écart 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":""},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Seriograph — seriograph","text":"Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Seriograph — seriograph","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/seriograph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seriograph — 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))"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Similarity — similarity","title":"Similarity — similarity","text":"Similarity","code":""},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Similarity — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Similarity — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Similarity — similarity","text":"similarity() returns stats::dist object. index_*() return numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Similarity — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Similarity — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Similarity — similarity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/similarity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Similarity — 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"},{"path":"https://packages.tesselle.org/tabula/reference/simulate.html","id":null,"dir":"Reference","previous_headings":"","what":"Measure Diversity by Comparing to Simulated Assemblages — simulate","title":"Measure Diversity by Comparing to Simulated Assemblages — simulate","text":"Measure Diversity Comparing Simulated Assemblages","code":""},{"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 — 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\") )"},{"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 — 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":""},{"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 — simulate","text":"Returns DiversityIndex object.","code":""},{"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 — 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":""},{"path":[]},{"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 — simulate","text":"N. Frerebeau","code":""},{"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 — 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)  # }"},{"path":"https://packages.tesselle.org/tabula/reference/tabula-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in tabula — tabula-deprecated","title":"Deprecated Functions in tabula — tabula-deprecated","text":"functions still work removed (defunct) next version.","code":""},{"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 — tabula-package","title":"tabula: Analysis and Visualization of Archaeological Count Data — 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":""},{"path":[]},{"path":[]},{"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 — tabula-package","text":"Full list authors contributors (alphabetic order) Package maintainer Nicolas Frerebeaunicolas.frerebeau@u-bordeaux-montaigne.fr Archéosciences Bordeaux (UMR 6034) Maison de l'Archéologie Université Bordeaux Montaigne 33607 Pessac cedex France","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":null,"dir":"Reference","previous_headings":"","what":"Diversity Test — test_diversity","title":"Diversity Test — test_diversity","text":"Compares Shannon diversity samples.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diversity Test — 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\", ...)"},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diversity Test — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diversity Test — test_diversity","text":"numeric  matrix.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diversity Test — test_diversity","text":"test produces two sided pairwise comparisons: returns matrix adjusted \\(p\\) values.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diversity Test — test_diversity","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diversity Test — test_diversity","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/test_diversity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diversity Test — 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"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":null,"dir":"Reference","previous_headings":"","what":"Turnover — turnover","title":"Turnover — turnover","text":"Returns degree turnover taxa composition along gradient transect.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Turnover — 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)"},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Turnover — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Turnover — turnover","text":"numeric vector.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Turnover — 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":""},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Turnover — 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":""},{"path":[]},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Turnover — turnover","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/tabula/reference/turnover.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Turnover — 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"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":null,"dir":"Reference","previous_headings":"","what":"Trees Incidences — woodland","title":"Trees Incidences — woodland","text":"dataset presence absence trees six (10 x 10 m) quadarts along transect deciduous woodland.","code":""},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trees Incidences — woodland","text":"","code":"woodland"},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Trees Incidences — woodland","text":"data.frame 6 rows (quadarts) 6 variables (tree species).","code":""},{"path":"https://packages.tesselle.org/tabula/reference/woodland.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Trees Incidences — woodland","text":"Magurran, . E. (1988). Ecological Diversity Measurement. Princeton, NJ: Princeton University Press. doi:10.1007/978-94-015-7358-0 .","code":""},{"path":[]}]