From 98ebfa06e3c7fd7d371747658b7932bfcf7026af Mon Sep 17 00:00:00 2001 From: kairos Date: Fri, 23 Feb 2024 13:43:23 +0000 Subject: [PATCH] Built site for kairos@977db1d --- kairos/dev/CODE_OF_CONDUCT.html | 3 +- kairos/dev/CONTRIBUTING.html | 3 +- kairos/dev/LICENSE.html | 3 +- kairos/dev/articles/bibliography.html | 336 ------------------ kairos/dev/articles/event.html | 116 +++--- kairos/dev/articles/index.html | 7 +- kairos/dev/articles/seriation.html | 104 +++--- kairos/dev/authors.html | 9 +- kairos/dev/index.html | 18 +- kairos/dev/news/index.html | 7 +- kairos/dev/paper.html | 3 +- kairos/dev/pkgdown.yml | 3 +- kairos/dev/pull_request_template.html | 3 +- kairos/dev/reference/AoristicSum-class.html | 3 +- .../dev/reference/CountApportion-class.html | 3 +- kairos/dev/reference/EventDate-class.html | 3 +- kairos/dev/reference/IncrementTest-class.html | 3 +- kairos/dev/reference/MeanDate-class.html | 3 +- .../dev/reference/PermutationOrder-class.html | 3 +- kairos/dev/reference/RateOfChange-class.html | 3 +- .../RefinePermutationOrder-class.html | 3 +- kairos/dev/reference/aoristic.html | 3 +- kairos/dev/reference/apportion.html | 3 +- kairos/dev/reference/data.frame.html | 3 +- kairos/dev/reference/event.html | 3 +- .../reference/figures/README-aoristic-2.png | Bin 23773 -> 24451 bytes kairos/dev/reference/fit.html | 3 +- kairos/dev/reference/index.html | 3 +- kairos/dev/reference/kairos-deprecated.html | 3 +- kairos/dev/reference/kairos-package.html | 3 +- kairos/dev/reference/mcd.html | 3 +- kairos/dev/reference/model.html | 3 +- kairos/dev/reference/mutators.html | 3 +- kairos/dev/reference/permute.html | 3 +- kairos/dev/reference/plot_aoristic.html | 3 +- kairos/dev/reference/plot_event.html | 3 +- kairos/dev/reference/plot_fit.html | 3 +- kairos/dev/reference/plot_mcd.html | 3 +- kairos/dev/reference/plot_time.html | 3 +- kairos/dev/reference/predict_event.html | 3 +- kairos/dev/reference/reexports.html | 3 +- kairos/dev/reference/resample_event.html | 3 +- kairos/dev/reference/resample_mcd.html | 3 +- kairos/dev/reference/roc.html | 3 +- kairos/dev/reference/seriate_average.html | 3 +- kairos/dev/reference/seriate_rank.html | 3 +- kairos/dev/reference/seriate_refine.html | 3 +- kairos/dev/reference/series.html | 3 +- kairos/dev/reference/subset.html | 3 +- kairos/dev/search.json | 2 +- kairos/dev/sitemap.xml | 3 - 51 files changed, 156 insertions(+), 569 deletions(-) delete mode 100644 kairos/dev/articles/bibliography.html diff --git a/kairos/dev/CODE_OF_CONDUCT.html b/kairos/dev/CODE_OF_CONDUCT.html index 4ce54bce2..b1d088056 100644 --- a/kairos/dev/CODE_OF_CONDUCT.html +++ b/kairos/dev/CODE_OF_CONDUCT.html @@ -24,9 +24,8 @@
  • - Seriation Methods Event Date Model - Bibliography + Seriation
  • diff --git a/kairos/dev/CONTRIBUTING.html b/kairos/dev/CONTRIBUTING.html index e5f5e1b2c..49fe5fe66 100644 --- a/kairos/dev/CONTRIBUTING.html +++ b/kairos/dev/CONTRIBUTING.html @@ -24,9 +24,8 @@
  • - Seriation Methods Event Date Model - Bibliography + Seriation
  • diff --git a/kairos/dev/LICENSE.html b/kairos/dev/LICENSE.html index de5746269..9950e8c12 100644 --- a/kairos/dev/LICENSE.html +++ b/kairos/dev/LICENSE.html @@ -24,9 +24,8 @@
  • - Seriation Methods Event Date Model - Bibliography + Seriation
  • diff --git a/kairos/dev/articles/bibliography.html b/kairos/dev/articles/bibliography.html deleted file mode 100644 index b12024594..000000000 --- a/kairos/dev/articles/bibliography.html +++ /dev/null @@ -1,336 +0,0 @@ - - - - - - - - -Bibliography • kairos - - - - - - - - - - - - - - - - - - - - - - Skip to contents - - -
    - - - - -
    -
    -

    Bibliography

    -

    Comprehensive list of -references cited in kairos

    - -

    2024-02-13

    - - Source: vignettes/bibliography.Rmd -
    bibliography.Rmd
    -
    - - - -
    -
    -Baxter, M. J., and H. E. M. Cool. 2016. “Reinventing the Wheel? -Modelling Temporal Uncertainty with Applications to Brooch -Distributions in Roman Britain.” Journal of -Archaeological Science 66 (February): 120–27. https://doi.org/10.1016/j.jas.2015.12.007. -
    -
    -Bellanger, L., and P. Husi. 2013. “Mesurer et modéliser le temps -inscrit dans la matière à partir d’une source matérielle : la céramique -médiévale.” In Mesure et histoire médiévale, 119–34. -Histoire ancienne et médiévale. Paris: Publication de -la Sorbonne. -
    -
    -Bellanger, L., Ph. Husi, and R. Tomassone. 2006a. “Une approche -statistique pour la datation de contextes archéologiques.” -Revue de statistique appliquée 54 (2): 65–81. http://www.numdam.org/item/RSA_2006__54_2_65_0/. -
    -
    -———. 2006b. “Statistical Aspects of Pottery -Quantification for the Dating of Some -Archaeological Contexts.” Archaeometry 48 (1): -169–83. https://doi.org/10.1111/j.1475-4754.2006.00249.x. -
    -
    -Bellanger, Lise, and Philippe Husi. 2012. “Statistical -Tool for Dating and Interpreting -Archaeological Contexts Using Pottery.” Journal of -Archaeological Science 39 (4): 777–90. https://doi.org/10.1016/j.jas.2011.06.031. -
    -
    -Bellanger, L., R. Tomassone, and P. Husi. 2008. “A -Statistical Approach for Dating Archaeological -Contexts.” Journal of Data Science 6: 135–54. -
    -
    -Brainerd, George W. 1951. “The Place of -Chronological Ordering in Archaeological -Analysis.” American Antiquity 16 (04): 301–13. https://doi.org/10.2307/276979. -
    -
    -Colman, Steven M., Kenneth L. Pierce, and Peter W. Birkeland. 1987. -“Suggested Terminology for Quaternary Dating -Methods.” Quaternary Research 28 (2): 314–19. https://doi.org/10.1016/0033-5894(87)90070-6. -
    -
    -Crema, Enrico R. 2012. “Modelling Temporal -Uncertainty in Archaeological Analysis.” -Journal of Archaeological Method and Theory 19 (3): 440–61. https://doi.org/10.1007/s10816-011-9122-3. -
    -
    -Crema, Enrico R., Anne Kandler, and Stephen Shennan. 2016. -“Revealing Patterns of Cultural -Transmission from Frequency Data: -Equilibrium and Non-Equilibrium -Assumptions.” Scientific Reports 6 (1). https://doi.org/10.1038/srep39122. -
    -
    -Dean, Jeffrey S. 1978. “Independent Dating in -Archaeological Analysis.” In Advances in -Archaeological Method and Theory, 223–55. -Elsevier. https://doi.org/10.1016/B978-0-12-003101-6.50013-5. -
    -
    -Desachy, Bruno. 2004. “Le sériographe EPPM: un outil informatisé -de sériation graphique pour tableaux de comptages.” Revue -archéologique de Picardie 3 (1): 39–56. https://doi.org/10.3406/pica.2004.2396. -
    -
    -Dunnell, Robert C. 1970. “Seriation Method and -Its Evaluation.” American Antiquity 35 -(03): 305–19. https://doi.org/10.2307/278341. -
    -
    -Dye, Thomas S. 2016. “Long-Term Rhythms in the Development of -Hawaiian Social Stratification.” Journal of -Archaeological Science 71 (July): 1–9. https://doi.org/10.1016/j.jas.2016.05.006. -
    -
    -Feder, Alison F., Sergey Kryazhimskiy, and Joshua B. Plotkin. 2014. -“Identifying Signatures of Selection in -Genetic Time Series.” Genetics 196 (2): -509–22. https://doi.org/10.1534/genetics.113.158220. -
    -
    -Ihm, Peter. 2005. “A Contribution to the -History of Seriation in -Archaeology.” In Classification – the -Ubiquitous Challenge, edited by Claus Weihs and -Wolfgang Gaul, 307–16. Berlin Heidelberg: -Springer. https://doi.org/10.1007/3-540-28084-7_34. -
    -
    -Johnson, I. 2004. “Aoristic Analysis: -Seeds of a New Approach to Mapping -Archaeological Distributions Through Time.” -In Enter the Past - The -E-way into the Four Dimensions of Cultural -Heritage, edited by K. F. Ausserer, W. Börner, M. Goriany, -and L. Karlhuber-Vöckl, 448–52. BAR International Series -1227. Oxford: Archaeopress. -
    -
    -Kintigh, Keith. 2006. “Ceramic Dating and Type -Associations.” In Managing Archaeological -Data: Essays in Honor of Sylvia -W. Gaines, edited by Jeffrey Hantman and Rachel -Most, 17–26. Anthropological Research Paper 57. Tempe, AZ: -Arizona State University. https://doi.org/10.6067/XCV8J38QSS. -
    -
    -Lipo, Carl P., Mark E. Madsen, and Robert C. Dunnell. 2015. “A -Theoretically-Sufficient and -Computationally-Practical Technique for Deterministic -Frequency Seriation.” PLOS ONE 10 (4): e0124942. -https://doi.org/10.1371/journal.pone.0124942. -
    -
    -Müller-Scheeßel, Nils, and Martin Hinz. 2018. “Aoristic Research -in R: Correcting Temporal Categorizations in -Archaeology.” Presented at the Human History and -Digital Future (CAA 2018), -Tubingen, March 21. https://www.youtube.com/watch?v=bUBukex30QI. -
    -
    -O’Brien, Michael J, and R. Lee Lyman. 2002. Seriation, -Stratigraphy, and Index Fossils: The -Backbone of Archaeological Dating. -Dordrecht: Springer. -
    -
    -Orton, David, James Morris, and Alan Pipe. 2017. “Catch Per -Unit Research Effort: Sampling Intensity, -Chronological Uncertainty, and the Onset of -Marine Fish Consumption in Historic -London.” Open Quaternary 3 (January): 1. https://doi.org/10.5334/oq.29. -
    -
    -Palmisano, Alessio, Andrew Bevan, and Stephen Shennan. 2017. -“Comparing Archaeological Proxies for Long-Term Population -Patterns: An Example from Central -Italy.” Journal of Archaeological Science -87 (November): 59–72. https://doi.org/10.1016/j.jas.2017.10.001. -
    -
    -Peeples, Matthew A., and Gregson Schachner. 2012. “Refining -Correspondence Analysis-Based Ceramic Seriation of -Regional Data Sets.” Journal of Archaeological -Science 39 (8): 2818–27. https://doi.org/10.1016/j.jas.2012.04.040. -
    -
    -Petrie, W. M. F. 1899. “Sequences in Prehistoric -Remains.” The Journal of the Anthropological Institute -of Great Britain and Ireland 29 (3/4): 295–301. https://doi.org/10.2307/2843012. -
    -
    -Philippe, Anne, and Marie-Anne Vibet. 2020. “Analysis of -Archaeological Phases Using the R Package -ArchaeoPhases.” Journal of Statistical Software -93. https://doi.org/10.18637/jss.v093.c01. -
    -
    -Poblome, J., and P. J. F. Groenen. 2003. “Constrained -Correspondence Analysis for Seriation of -Sagalassos Tablewares.” In The Digital -Heritage of Archaeology, edited by M. Doerr and -A. Sarris. Athens: Hellenic Ministry of -Culture. -
    -
    -Porčić, M. 2013. “The Goodness of Fit -and Statistical Significance of Seriation -Solutions.” Journal of Archaeological Science 40 -(12): 4552–59. https://doi.org/10.1016/j.jas.2013.07.013. -
    -
    -Ratcliffe, Jerry H. 2000. “Aoristic Analysis: The Spatial -Interpretation of Unspecific Temporal Events.” International -Journal of Geographical Information Science 14 (7): 669–79. https://doi.org/10.1080/136588100424963. -
    -
    -———. 2002. “Aoristic Signatures and the -Spatio-Temporal Analysis of High Volume Crime -Patterns.” Journal of Quantitative Criminology 18 -(1): 23–43. https://doi.org/10.1023/A:1013240828824. -
    -
    -Roberts, John M., Barbara J. Mills, Jeffery J. Clark, W. Randall Haas, -Deborah L. Huntley, and Meaghan A. Trowbridge. 2012. “A Method for -Chronological Apportioning of Ceramic Assemblages.” Journal -of Archaeological Science 39 (5): 1513–20. https://doi.org/10.1016/j.jas.2011.12.022. -
    -
    -Robinson, W. S. 1951. “A Method for -Chronologically Ordering Archaeological Deposits.” -American Antiquity 16 (04): 293–301. https://doi.org/10.2307/276978. -
    -
    -South, S. A. 1977. Method and Theory in -Historical Archaeology. Studies in Archeology. -New York: Academic Press. -
    -
    -Velden, M. vande, P. J. F. Groenen, and J. Poblome. 2009. -“Seriation by Constrained Correspondence Analysis: -A Simulation Study.” Computational Statistics -& Data Analysis 53 (8): 3129–38. https://doi.org/10.1016/j.csda.2008.08.020. -
    -
    -
    -
    - - - -
    -

    kairos is a part of the tesselle project, a collection of packages for research and teaching in archaeology. Learn more at tesselle.org.

    - -

    -

    Developed by Nicolas Frerebeau, Université Bordeaux Montaigne, CNRS.

    -
    - -
    -

    -

    Site built with pkgdown 2.0.7.

    -
    - -
    -
    - - - - - - - diff --git a/kairos/dev/articles/event.html b/kairos/dev/articles/event.html index b031f3b14..498e04211 100644 --- a/kairos/dev/articles/event.html +++ b/kairos/dev/articles/event.html @@ -53,9 +53,8 @@
  • - Seriation Methods Event Date Model - Bibliography + Seriation
  • @@ -89,7 +88,7 @@

    N. Frerebeau

    -

    2024-02-13

    +

    2024-02-23

    Source: vignettes/event.Rmd
    event.Rmd
    @@ -108,26 +107,26 @@

    2024-02-13

    Definition

    Event and accumulation dates are density estimates of the occupation -and duration of an archaeological site (L. -Bellanger, Husi, and Tomassone 2006; L. Bellanger, Tomassone, and Husi -2008; Lise Bellanger and Husi 2012).

    +and duration of an archaeological site (Bellanger, Husi and Tomassone +2006; Bellanger, Tomassone and Husi 2008; Bellanger and Husi 2012).

    The event date is an estimation of the terminus post-quem of an archaeological assemblage. The accumulation date represents the -“chronological profile” of the assemblage. According to Lise Bellanger and Husi (2012), accumulation -date can be interpreted “at best […] as a formation process reflecting -the duration or succession of events on the scale of archaeological -time, and at worst, as imprecise dating due to contamination of the -context by residual or intrusive material.” In other words, accumulation -dates estimate occurrence of archaeological events and rhythms of the -long term.

    +“chronological profile” of the assemblage. According to Bellanger and +Husi (2012), accumulation date can be interpreted “at best […] as a +formation process reflecting the duration or succession of events on the +scale of archaeological time, and at worst, as imprecise dating due to +contamination of the context by residual or intrusive material.” In +other words, accumulation dates estimate occurrence of archaeological +events and rhythms of the long term.

    Event Date

    Event dates are estimated by fitting a Gaussian multiple linear regression model on the factors resulting from a correspondence analysis -- somewhat similar to the idea introduced by Poblome and Groenen (2003). This model results -from the known dates of a selection of reliable contexts and allows to -predict the event dates of the remaining assemblages.

    +- somewhat similar to the idea introduced by Poblome and Groenen (2003). +This model results from the known dates of a selection of reliable +contexts and allows to predict the event dates of the remaining +assemblages.

    First, a correspondence analysis (CA) is carried out to summarize the information in the count matrix \(X\). The correspondence analysis of \(X\) @@ -222,15 +221,15 @@

    Accumulation DateDye (2016).

    +by Dye (2016).

    Limitation

    Event and accumulation dates estimation relies on the same conditions -and assumptions as the matrix seriation problem. Dunnell (1970) summarizes these conditions and -assumptions as follows.

    +and assumptions as the matrix seriation problem. Dunnell (1970) +summarizes these conditions and assumptions as follows.

    The homogeneity conditions state that all the groups included in a seriation must:

      @@ -256,11 +255,10 @@

      LimitationUsage

      This package provides an implementation of the chronological modeling -method developed by Lise Bellanger and Husi -(2012). This method is slightly modified here and allows the -construction of different probability density curves of archaeological -assemblage dates (event, activity and -tempo).

      +method developed by Bellanger and Husi (2012). This method is slightly +modified here and allows the construction of different probability +density curves of archaeological assemblage dates (event, +activity and tempo).

       ## Bellanger et al. did not publish the data supporting their demonstration: 
 ## no replication of their results is possible. 
@@ -395,52 +393,34 @@ 
      Usage
 boot <- bootstrap(model, n = 30)
 head(boot)
 #>             min     mean      max       Q5      Q95
-#> LZ1105 411915.0 425263.8 439858.2 414935.1 436043.2
-#> LZ1103 398948.6 420824.7 445273.4 406639.4 440084.3
-#> LZ1100 392277.7 422550.5 448846.9 399557.0 440925.6
-#> LZ1099 396680.9 400828.0 403547.7 397742.1 403290.9
-#> LZ1097 332384.6 397254.3 433582.3 374202.4 426407.7
-#> LZ1096 264824.9 306408.7 373883.4 264824.9 361765.8
      +#> LZ1105 410334.3 426428.6 439120.1 417496.8 434740.8 +#> LZ1103 399338.3 419530.2 439358.0 404374.2 434841.2 +#> LZ1100 402595.0 424568.7 444349.5 406580.8 439192.0 +#> LZ1099 396859.2 401138.7 405976.8 398557.6 405506.1 +#> LZ1097 351380.8 396090.8 422539.6 360559.3 420506.6 +#> LZ1096 264824.9 317752.5 410236.2 285727.7 358410.1
    -
    -

    References +
    +

    References

    -
    -
    -Bellanger, L., Ph. Husi, and R. Tomassone. 2006. “Une approche -statistique pour la datation de contextes archéologiques.” -Revue de statistique appliquée 54 (2): 65–81. http://www.numdam.org/item/RSA_2006__54_2_65_0/. -
    -
    -Bellanger, Lise, and Philippe Husi. 2012. “Statistical -Tool for Dating and Interpreting -Archaeological Contexts Using Pottery.” Journal of -Archaeological Science 39 (4): 777–90. https://doi.org/10.1016/j.jas.2011.06.031. -
    -
    -Bellanger, L., R. Tomassone, and P. Husi. 2008. “A -Statistical Approach for Dating Archaeological -Contexts.” Journal of Data Science 6: 135–54. -
    -
    -Dunnell, Robert C. 1970. “Seriation Method and -Its Evaluation.” American Antiquity 35 -(03): 305–19. https://doi.org/10.2307/278341. -
    -
    -Dye, Thomas S. 2016. “Long-Term Rhythms in the Development of -Hawaiian Social Stratification.” Journal of -Archaeological Science 71 (July): 1–9. https://doi.org/10.1016/j.jas.2016.05.006. -
    -
    -Poblome, J., and P. J. F. Groenen. 2003. “Constrained -Correspondence Analysis for Seriation of -Sagalassos Tablewares.” In The Digital -Heritage of Archaeology, edited by M. Doerr and -A. Sarris. Athens: Hellenic Ministry of -Culture. -
    -
    +

    Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche +statistique pour la datation de contextes archéologiques. Revue de +statistique appliquée, 54(2), 65-81. URL: http://www.numdam.org/item/RSA_2006__54_2_65_0/.

    +

    Bellanger, L. & Husi, P. (2012). Statistical Tool for Dating and +Interpreting Archaeological Contexts Using Pottery. Journal of +Archaeological Science, 39(4), 777-790. DOI: 10.1016/j.jas.2011.06.031.

    +

    Bellanger, L. & Tomassone, R. & Husi, P. (2008). A +Statistical Approach for Dating Archaeological Contexts. Journal of +Data Science, 6, 135-154.

    +

    Dunnell, R. C. (1970). Seriation Method and Its Evaluation. +American Antiquity, 35(3), 305-319. DOI: 10.2307/278341.

    +

    Dye, T. S. (2016). Long-Term Rhythms in the Development of Hawaiian +Social Stratification. Journal of Archaeological Science, 71, +1-9. DOI: 10.1016/j.jas.2016.05.006.

    +

    Poblome, J. & Groenen, P. J. F. (2003). Constrained +Correspondence Analysis for Seriation of Sagalassos Tablewares. In M. +Doerr & A. Sarris (Eds.), The Digital Heritage of +Archaeology Athens: Hellenic Ministry of Culture.

    diff --git a/kairos/dev/articles/index.html b/kairos/dev/articles/index.html index f7a7094c7..e3265251d 100644 --- a/kairos/dev/articles/index.html +++ b/kairos/dev/articles/index.html @@ -24,9 +24,8 @@
  • - Seriation Methods Event Date Model - Bibliography + Seriation
  • @@ -55,9 +54,7 @@

    All vignettes

    -
    Bibliography
    -
    -
    Event Date Model
    +
    Event Date Model
    Seriation
    diff --git a/kairos/dev/articles/seriation.html b/kairos/dev/articles/seriation.html index 88c06c0d4..4cf8f4015 100644 --- a/kairos/dev/articles/seriation.html +++ b/kairos/dev/articles/seriation.html @@ -53,9 +53,8 @@
  • - Seriation Methods Event Date Model - Bibliography + Seriation
  • @@ -89,7 +88,7 @@

    N. Frerebeau

    -

    2024-02-13

    +

    2024-02-23

    Source: vignettes/seriation.Rmd
    seriation.Rmd
    @@ -110,8 +109,8 @@

    2024-02-13

    Introduction

    The matrix seriation problem in archaeology is based on three -conditions and two assumptions, which Dunnell -(1970) summarizes as follows.

    +conditions and two assumptions, which Dunnell (1970) summarizes as +follows.

    The homogeneity conditions state that all the groups included in a seriation must:

    •  ## Permute data matrix
@@ -245,33 +244,36 @@ 
    Seriation
 
    Refining
 
    
-
    Peeples and Schachner (2012) propose a
-procedure to identify samples that are subject to sampling error or
-samples that have underlying structural relationships and might be
-influencing the ordering along the CA space. This relies on a partial
-bootstrap approach to CA-based seriation where each sample is replicated
-n times. The maximum dimension length of the convex hull
-around the sample point cloud allows to remove samples for a given
-cutoff value.
    
-
    According to Peeples and Schachner
-(2012), “[this] point removal procedure [results in] a reduced
-dataset where the position of individuals within the CA are highly
-stable and which produces an ordering consistent with the assumptions of
-frequency seriation.”
    
+
    Peeples and Schachner (2012) propose a procedure to identify samples
+that are subject to sampling error or samples that have underlying
+structural relationships and might be influencing the ordering along the
+CA space. This relies on a partial bootstrap approach to CA-based
+seriation where each sample is replicated n times. The
+maximum dimension length of the convex hull around the sample point
+cloud allows to remove samples for a given cutoff
+value.
    
+
    According to Peeples and Schachner (2012), “[this] point removal
+procedure [results in] a reduced dataset where the position of
+individuals within the CA are highly stable and which produces an
+ordering consistent with the assumptions of frequency seriation.”
    
 
     ## Partial bootstrap CA
 ## Warning: this may take a few seconds!
-zuni_boot <- dimensio::bootstrap(zun_indices, n = 30)
+zuni_boot <- bootstrap(zun_indices, n = 30)
 
 ## Bootstrap CA results for the rows
 ## (add convex hull)
 zuni_boot |> 
-  dimensio::viz_rows(col = "lightgrey", pch = 16, legend = NULL) |> 
-  dimensio::viz_hull(col = adjustcolor("#004488", alpha = 0.2))
+  viz_rows(col = "lightgrey", pch = 16, legend = NULL) |> 
+  viz_hull(col = adjustcolor("#004488", alpha = 0.2))
+#> Warning: Consider specifying symbols manually: more than 6 becomes difficult to
+#> discriminate.
 
 ## Bootstrap CA results for the columns
 zuni_boot |> 
-  dimensio::viz_columns(pch = 16, legend = list(x = "topright"))
    
+  viz_columns(pch = 16, legend = list(x = "topright"))
+#> Warning: Consider specifying symbols manually: more than 6 becomes difficult to
+#> discriminate.

     ## Replicates Peeples and Schachner 2012 results
@@ -289,7 +291,7 @@ 
    Refining#> - Rows to keep: 360 of 420 (86%)
 
 ## Plot CA results for the rows
-dimensio::viz_rows(zuni_refine, highlight = "observation", pch = c(16, 15))
    +viz_rows(zuni_refine, highlight = "observation", pch = c(16, 15)) 

     ## Histogram of convex hull maximum dimension length
@@ -305,35 +307,21 @@ 
    Refining
    -
    -

    References +
    +

    References

    -
    -
    -Desachy, Bruno. 2004. “Le sériographe EPPM: un outil informatisé -de sériation graphique pour tableaux de comptages.” Revue -archéologique de Picardie 3 (1): 39–56. https://doi.org/10.3406/pica.2004.2396. -
    -
    -Dunnell, Robert C. 1970. “Seriation Method and -Its Evaluation.” American Antiquity 35 -(03): 305–19. https://doi.org/10.2307/278341. -
    -
    -Ihm, Peter. 2005. “A Contribution to the -History of Seriation in -Archaeology.” In Classification – the -Ubiquitous Challenge, edited by Claus Weihs and -Wolfgang Gaul, 307–16. Berlin Heidelberg: -Springer. https://doi.org/10.1007/3-540-28084-7_34. -
    -
    -Peeples, Matthew A., and Gregson Schachner. 2012. “Refining -Correspondence Analysis-Based Ceramic Seriation of -Regional Data Sets.” Journal of Archaeological -Science 39 (8): 2818–27. https://doi.org/10.1016/j.jas.2012.04.040. -
    -
    +

    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.

    +

    Dunnell, R. C. (1970). Seriation Method and Its Evaluation. +American Antiquity, 35(03), 305-319. DOI: 10.2307/278341.

    +

    Ihm, P. (2005). A Contribution to the History of Seriation in +Archaeology. In C. Weihs & W. Gaul (Eds.), Classification: The +Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. DOI: +10.1007/3-540-28084-7_34.

    +

    Peeples, M. A. & Schachner, G. (2012). Refining correspondence +analysis-based ceramic seriation of regional data sets. Journal of +Archaeological Science, 39(8), 2818-2827. DOI: 10.1016/j.jas.2012.04.040.

    diff --git a/kairos/dev/authors.html b/kairos/dev/authors.html index e0836a692..f7fe9386c 100644 --- a/kairos/dev/authors.html +++ b/kairos/dev/authors.html @@ -24,9 +24,8 @@
  • - Seriation Methods Event Date Model - Bibliography + Seriation
  • @@ -84,15 +83,15 @@

    Authors

    Citation

    Source: inst/CITATION

    -

    Frerebeau N (2023). +

    Frerebeau N (2024). kairos: Analysis of Chronological Patterns from Archaeological Count Data. Université Bordeaux Montaigne, Pessac, France. doi:10.5281/zenodo.5653896, R package version 2.0.2.9000, https://packages.tesselle.org/kairos/.

    - 
    @Manual{,
+      
    @Manual{R-kairos,
   author = {Nicolas Frerebeau},
   title = {{kairos: Analysis of Chronological Patterns from Archaeological Count Data}},
-  year = {2023},
+  year = {2024},
   organization = {Université Bordeaux Montaigne},
   address = {Pessac, France},
   note = {R package version 2.0.2.9000},
diff --git a/kairos/dev/index.html b/kairos/dev/index.html
index 5580395a4..5971dc88e 100644
--- a/kairos/dev/index.html
+++ b/kairos/dev/index.html
@@ -63,9 +63,8 @@
 
  • 
   
   
    
-    Seriation Methods
     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
@@ -119,7 +118,7 @@ 
    Overviewtabula is a companion package to kairos that provides functions for visualization and analysis of archaeological count data.
    
 
    To cite kairos in publications use:
 
-  Frerebeau N (2023). _kairos: Analysis of Chronological Patterns from
+  Frerebeau N (2024). _kairos: Analysis of Chronological Patterns from
   Archaeological Count Data_. Université Bordeaux Montaigne, Pessac,
   France. doi:10.5281/zenodo.5653896
   <https://doi.org/10.5281/zenodo.5653896>, R package version 2.0.2,
@@ -127,10 +126,10 @@ 
    Overview
 A BibTeX entry for LaTeX users is
 
-  @Manual{,
+  @Manual{R-kairos,
     author = {Nicolas Frerebeau},
     title = {{kairos: Analysis of Chronological Patterns from Archaeological Count Data}},
-    year = {2023},
+    year = {2024},
     organization = {Université Bordeaux Montaigne},
     address = {Pessac, France},
     note = {R package version 2.0.2},
@@ -157,7 +156,8 @@ 
    Usage
 
    
 
     ## Load packages
-library(kairos)
    
+library(kairos)
+#> Loading required package: dimensio
    
 
    kairos v2.0 uses aion for internal date representation. Look at vignette("aion") before you start.
    
 
    It assumes that you keep your data tidy: each variable (type/taxa) must be saved in its own column and each observation (sample/case) must be saved in its own row.
    
 
    @@ -192,13 +192,13 @@ 
    References
 
    
-Bellanger, Lise, and Philippe Husi. 2012. “Statistical Tool for Dating and Interpreting Archaeological Contexts Using Pottery.” Journal of Archaeological Science 39 (4): 777–90. https://doi.org/10.1016/j.jas.2011.06.031.
+Bellanger, L., and P. Husi. 2012. “Statistical Tool for Dating and Interpreting Archaeological Contexts Using Pottery.” Journal of Archaeological Science 39 (4): 777–90. https://doi.org/10.1016/j.jas.2011.06.031.
 
    
 
    
-Ratcliffe, Jerry H. 2000. “Aoristic Analysis: The Spatial Interpretation of Unspecific Temporal Events.” International Journal of Geographical Information Science 14 (7): 669–79. https://doi.org/10.1080/136588100424963.
+Ratcliffe, J. H. 2000. “Aoristic Analysis: The Spatial Interpretation of Unspecific Temporal Events.” International Journal of Geographical Information Science 14 (7): 669–79. https://doi.org/10.1080/136588100424963.
 
    
 
    
-Roberts, John M., Barbara J. Mills, Jeffery J. Clark, W. Randall Haas, Deborah L. Huntley, and Meaghan A. Trowbridge. 2012. “A Method for Chronological Apportioning of Ceramic Assemblages.” Journal of Archaeological Science 39 (5): 1513–20. https://doi.org/10.1016/j.jas.2011.12.022.
+Roberts, J. M., B. J. Mills, J. J. Clark, W. R. Haas, D. L. Huntley, and M. A. Trowbridge. 2012. “A Method for Chronological Apportioning of Ceramic Assemblages.” Journal of Archaeological Science 39 (5): 1513–20. https://doi.org/10.1016/j.jas.2011.12.022.
 
    
 
    
 South, S. A. 1977. Method and Theory in Historical Archaeology. Studies in Archeology. New York: Academic Press.
diff --git a/kairos/dev/news/index.html b/kairos/dev/news/index.html
index 6099ff4c5..e5e995402 100644
--- a/kairos/dev/news/index.html
+++ b/kairos/dev/news/index.html
@@ -24,9 +24,8 @@
 
  • 
   
   
    
-    Seriation Methods
     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
@@ -54,6 +53,10 @@
 
     
    
 
    kairos 2.0.2.9000
    
+
    
+
    Internals
    
+
    • List dimensio in Depends.
      • 
+
    
 
    
     
    
 
    kairos 2.0.2
    CRAN release: 2023-11-27
    
diff --git a/kairos/dev/paper.html b/kairos/dev/paper.html
index c2d10a265..d16883111 100644
--- a/kairos/dev/paper.html
+++ b/kairos/dev/paper.html
@@ -24,9 +24,8 @@
 
  • 
   
   
    
-    Seriation Methods
     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
diff --git a/kairos/dev/pkgdown.yml b/kairos/dev/pkgdown.yml
index a533f7f68..907fe7283 100644
--- a/kairos/dev/pkgdown.yml
+++ b/kairos/dev/pkgdown.yml
@@ -2,10 +2,9 @@ pandoc: 2.19.2
 pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
-  bibliography: bibliography.html
   event: event.html
   seriation: seriation.html
-last_built: 2024-02-13T12:19Z
+last_built: 2024-02-23T13:42Z
 urls:
   reference: https://packages.tesselle.org/kairos/reference
   article: https://packages.tesselle.org/kairos/articles
diff --git a/kairos/dev/pull_request_template.html b/kairos/dev/pull_request_template.html
index 4cdf142fa..e2c2b6540 100644
--- a/kairos/dev/pull_request_template.html
+++ b/kairos/dev/pull_request_template.html
@@ -24,9 +24,8 @@
 
  • 
   
   
    
-    Seriation Methods
     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
diff --git a/kairos/dev/reference/AoristicSum-class.html b/kairos/dev/reference/AoristicSum-class.html
index 568841d74..abd3b70ca 100644
--- a/kairos/dev/reference/AoristicSum-class.html
+++ b/kairos/dev/reference/AoristicSum-class.html
@@ -24,9 +24,8 @@
 
  • 
   
   
    
-    Seriation Methods
     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
diff --git a/kairos/dev/reference/CountApportion-class.html b/kairos/dev/reference/CountApportion-class.html
index 5018b9ac0..ee9f660a4 100644
--- a/kairos/dev/reference/CountApportion-class.html
+++ b/kairos/dev/reference/CountApportion-class.html
@@ -28,9 +28,8 @@
 
  • 
   
   
    
-    Seriation Methods
     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
diff --git a/kairos/dev/reference/EventDate-class.html b/kairos/dev/reference/EventDate-class.html
index cb509ed71..0c5ee31fc 100644
--- a/kairos/dev/reference/EventDate-class.html
+++ b/kairos/dev/reference/EventDate-class.html
@@ -26,9 +26,8 @@
 
  • 
   
   
    
-    Seriation Methods
     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
diff --git a/kairos/dev/reference/IncrementTest-class.html b/kairos/dev/reference/IncrementTest-class.html
index aea7b95b2..356c408c3 100644
--- a/kairos/dev/reference/IncrementTest-class.html
+++ b/kairos/dev/reference/IncrementTest-class.html
@@ -24,9 +24,8 @@
 
  • 
   
   
    
-    Seriation Methods
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-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
diff --git a/kairos/dev/reference/MeanDate-class.html b/kairos/dev/reference/MeanDate-class.html
index d0fea67d5..c99a16731 100644
--- a/kairos/dev/reference/MeanDate-class.html
+++ b/kairos/dev/reference/MeanDate-class.html
@@ -26,9 +26,8 @@
 
  • 
   
   
    
-    Seriation Methods
     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
diff --git a/kairos/dev/reference/PermutationOrder-class.html b/kairos/dev/reference/PermutationOrder-class.html
index a470f9189..cfbf09ed2 100644
--- a/kairos/dev/reference/PermutationOrder-class.html
+++ b/kairos/dev/reference/PermutationOrder-class.html
@@ -24,9 +24,8 @@
 
  • 
   
   
    
-    Seriation Methods
     Event Date Model
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+    Seriation
   
    
 
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diff --git a/kairos/dev/reference/RateOfChange-class.html b/kairos/dev/reference/RateOfChange-class.html
index 35ff53fe4..0976c507d 100644
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+++ b/kairos/dev/reference/RateOfChange-class.html
@@ -24,9 +24,8 @@
 
  • 
   
   
    
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     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
diff --git a/kairos/dev/reference/RefinePermutationOrder-class.html b/kairos/dev/reference/RefinePermutationOrder-class.html
index 06e5264d4..03e74cb69 100644
--- a/kairos/dev/reference/RefinePermutationOrder-class.html
+++ b/kairos/dev/reference/RefinePermutationOrder-class.html
@@ -24,9 +24,8 @@
 
  • 
   
   
    
-    Seriation Methods
     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
  • 
diff --git a/kairos/dev/reference/aoristic.html b/kairos/dev/reference/aoristic.html
index 3690cd745..25e2b68b3 100644
--- a/kairos/dev/reference/aoristic.html
+++ b/kairos/dev/reference/aoristic.html
@@ -24,9 +24,8 @@
 
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-    Seriation Methods
     Event Date Model
-    Bibliography
+    Seriation
   
    
 
    • 
 
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diff --git a/kairos/dev/reference/apportion.html b/kairos/dev/reference/apportion.html
index a158cfc31..40b5694cb 100644
--- a/kairos/dev/reference/apportion.html
+++ b/kairos/dev/reference/apportion.html
@@ -24,9 +24,8 @@
 
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-    Seriation Methods
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+    Seriation
   
    
 
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diff --git a/kairos/dev/reference/data.frame.html b/kairos/dev/reference/data.frame.html
index 24cc39909..55ba9f98a 100644
--- a/kairos/dev/reference/data.frame.html
+++ b/kairos/dev/reference/data.frame.html
@@ -24,9 +24,8 @@
 
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-    Seriation Methods
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-    Bibliography
+    Seriation
   
    
 
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diff --git a/kairos/dev/reference/event.html b/kairos/dev/reference/event.html
index 131c057cb..bc0bfe197 100644
--- a/kairos/dev/reference/event.html
+++ b/kairos/dev/reference/event.html
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-    Bibliography
+    Seriation
   
    
 
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-[{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"definition","dir":"Articles","previous_headings":"","what":"Definition","title":"Event Date Model","text":"Event accumulation dates density estimates occupation duration archaeological site (L. Bellanger, Husi, Tomassone 2006; L. Bellanger, Tomassone, Husi 2008; Lise Bellanger Husi 2012). event date estimation terminus post-quem archaeological assemblage. accumulation date represents “chronological profile” assemblage. According Lise Bellanger Husi (2012), accumulation date can interpreted “best […] formation process reflecting duration succession events scale archaeological time, worst, imprecise dating due contamination context residual intrusive material.” words, accumulation dates estimate occurrence archaeological events rhythms long term.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"event-date","dir":"Articles","previous_headings":"Definition","what":"Event Date","title":"Event Date Model","text":"Event dates estimated fitting Gaussian multiple linear regression model factors resulting correspondence analysis - somewhat similar idea introduced Poblome Groenen (2003). model results known dates selection reliable contexts allows predict event dates remaining assemblages. First, correspondence analysis (CA) carried summarize information count matrix \\(X\\). correspondence analysis \\(X\\) provides coordinates \\(m\\) rows along \\(q\\) factorial components, denoted \\(f_{ik} ~\\forall \\\\left[ 1,m \\right], k \\\\left[ 1,q \\right]\\). , assuming \\(n\\) assemblages reliably dated another source, Gaussian multiple linear regression model fitted factorial components \\(n\\) dated assemblages: \\[ t^E_i = \\beta_{0} + \\sum_{k = 1}^{q} \\beta_{k} f_{ik} + \\epsilon_i ~\\forall \\[1,n] \\] \\(t^E_i\\) known date point estimate \\(\\)th assemblage, \\(\\beta_k\\) regression coefficients \\(\\epsilon_i\\) normally, identically independently distributed random variables, \\(\\epsilon_i \\sim \\mathcal{N}(0,\\sigma^2)\\). \\(n\\) equations stacked together written matrix notation \\[ t^E = F \\beta + \\epsilon \\] \\(\\epsilon \\sim \\mathcal{N}_{n}(0,\\sigma^2 I_{n})\\), \\(\\beta = \\left[ \\beta_0 \\cdots \\beta_q \\right]' \\\\mathbb{R}^{q+1}\\) \\[ F = \\begin{bmatrix} 1 & f_{11} & \\cdots & f_{1q} \\\\ 1 & f_{21} & \\cdots & f_{2q} \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ 1 & f_{n1} & \\cdots & f_{nq} \\end{bmatrix} \\] Assuming \\(F'F\\) nonsingular, ordinary least squares estimator unknown parameter vector \\(\\beta\\) : \\[ \\widehat{\\beta} = \\left( F'F \\right)^{-1} F' t^E \\] Finally, given vector CA coordinates \\(f_i\\), predicted event date assemblage \\(t^E_i\\) : \\[ \\widehat{t^E_i} = f_i \\hat{\\beta} \\] endpoints \\(100(1 − \\alpha)\\)% associated prediction confidence interval given : \\[ \\widehat{t^E_i} \\pm t_{\\alpha/2,n-q-1} \\sqrt{\\widehat{V}} \\] \\(\\widehat{V_i}\\) estimator variance prediction error:  \\[ \\widehat{V_i} = \\widehat{\\sigma}^2 \\left( f_i^T \\left( F'F \\right)^{-1} f_i + 1 \\right) \\] \\(\\widehat{\\sigma} = \\frac{\\sum_{=1}^{n} \\left( t_i - \\widehat{t^E_i} \\right)^2}{n - q - 1}\\). probability density event date \\(t^E_i\\) can described normal distribution: \\[ t^E_i \\sim \\mathcal{N}(\\widehat{t^E_i},\\widehat{V_i}) \\]","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"accumulation-date","dir":"Articles","previous_headings":"Definition","what":"Accumulation Date","title":"Event Date Model","text":"row (assemblages) columns (types) CA coordinates linked together -called transition formulae, event dates type \\(t^E_j\\) can predicted following procedure . , accumulation date \\(t^A_i\\) defined weighted mean event date ceramic types found given assemblage. weights conditional frequencies respective types assemblage (akin MCD). accumulation date estimated : \\[ \\widehat{t^A_i} = \\sum_{j = 1}^{p} \\widehat{t^E_j} \\times \\frac{x_{ij}}{x_{\\cdot}} \\] probability density accumulation date \\(t^A_i\\) can described Gaussian mixture: \\[ t^A_i \\sim \\frac{x_{ij}}{x_{\\cdot}} \\mathcal{N}(\\widehat{t^E_j},\\widehat{V_j}^2) \\] Interestingly, integral accumulation date offers estimates cumulative occurrence archaeological events, close enough definition tempo plot introduced Dye (2016).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"limitation","dir":"Articles","previous_headings":"","what":"Limitation","title":"Event Date Model","text":"Event accumulation dates estimation relies conditions assumptions matrix seriation problem. Dunnell (1970) summarizes conditions assumptions follows. homogeneity conditions state groups included seriation must: comparable duration. Belong cultural tradition. Come local area. mathematical assumptions state distribution historical temporal class: continuous time. Exhibits form unimodal curve. Theses assumptions create distributional model ordering accomplished arranging matrix class distributions approximate required pattern. resulting order inferred chronological. Predicted dates interpreted care: dates highly dependent range known dates fit regression.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"usage","dir":"Articles","previous_headings":"","what":"Usage","title":"Event Date Model","text":"package provides implementation chronological modeling method developed Lise Bellanger Husi (2012). method slightly modified allows construction different probability density curves archaeological assemblage dates (event, activity tempo).    Resampling methods can used check stability resulting model. jackknife() used, one type/fabric removed time statistics recalculated. way, one can assess whether certain type/fabric substantial influence date estimate. bootstrap() used, large number new bootstrap assemblages created, sample size, resampling original assemblage replacement. , examination bootstrap statistics makes possible pinpoint assemblages require investigation.","code":"## Bellanger et al. did not publish the data supporting their demonstration:  ## no replication of their results is possible.  ## Here is an example using the Zuni dataset from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) ## The names of the vector entries must match the names of the assemblages zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, dates = zuni_dates, rank = 10)  ## Model summary ## (results are expressed in rata die) summary(model) #>  #> Call: #> stats::lm(formula = date ~ ., data = contexts) #>  #> Residuals: #>   LZ0852   LZ0610   LZ0578   LZ0569   LZ0563   LZ0329   LZ0322   LZ0279  #>  -479.32   351.48  1283.51   163.57 -1626.71  -290.90   950.04 -1427.33  #>   LZ0227   LZ0067   LZ0066  LZ0005Q     CS16    CS144   LZ1209  #>  -280.59   -50.24   266.02    45.83  -105.47  1016.24   183.86  #> attr(,\"class\") #> [1] \"RataDie\" #> attr(,\"class\")attr(,\"package\") #> [1] \"aion\" #>  #> Coefficients: #>             Estimate Std. Error t value Pr(>|t|)     #> (Intercept) 425015.4     1064.9 399.095 2.36e-10 *** #> F1          -57813.4      602.6 -95.938 7.08e-08 *** #> F2            9387.7      615.2  15.260 0.000108 *** #> F3           -2047.2      789.1  -2.594 0.060411 .   #> F4            3928.9     2127.1   1.847 0.138452     #> F5           -1146.2     1442.3  -0.795 0.471276     #> F6             995.1      485.4   2.050 0.109663     #> F7            1667.0     1906.7   0.874 0.431304     #> F8            4126.0     1264.9   3.262 0.031027 *   #> F9           -1889.1     1079.6  -1.750 0.155039     #> F10           -144.8      967.9  -0.150 0.888300     #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Residual standard error: 1494 on 4 degrees of freedom #> Multiple R-squared:  0.9997, Adjusted R-squared:  0.999  #> F-statistic:  1433 on 10 and 4 DF,  p-value: 1.168e-06  ## Extract model coefficients ## (convert results to Gregorian years) coef(model, calendar = CE()) #>  (Intercept)           F1           F2           F3           F4           F5  #> 1163.6512430 -158.2887134   25.7005062   -5.6059619   10.7559523   -3.1402928  #>           F6           F7           F8           F9          F10  #>    2.7236281    4.5617156   11.2951729   -5.1728421   -0.3984238  ## Extract residual standard deviation ## (convert results to Gregorian years) sigma(model, calendar = CE()) #> [1] 4.088072  ## Extract model residuals ## (convert results to Gregorian years) resid(model, calendar = CE()) #>  [1] -1.3132066  0.9602214  3.5123344  0.4453877 -4.4554781 -0.7975492 #>  [7]  2.6001038 -3.9104984 -0.7693659 -0.1399926  0.7260925  0.1228117 #> [13] -0.2908912  2.7814906  0.5009809  ## Extract model fitted values ## (convert results to Gregorian years) fitted(model, calendar = CE()) #>  [1] 1217.3105 1073.0370 1176.4849 1096.5531 1210.4540 1076.7948 1106.3999 #>  [8] 1122.9078 1104.7666  863.1376 1110.2712  858.8744 1328.2882 1259.2185 #> [15] 1250.4963 ## Estimate event dates ## (results are expressed in rata die) eve <- predict_event(model, margin = 1, level = 0.95) head(eve) #>            date    lower    upper    error #> LZ1105 426454.0 420445.5 432462.4 2164.087 #> LZ1103 417145.8 414689.9 419601.7  884.558 #> LZ1100 421968.1 416394.9 427541.4 2007.317 #> LZ1099 400974.7 396642.1 405307.4 1560.503 #> LZ1097 397176.5 393404.6 400948.3 1358.530 #> LZ1096 306371.0 300801.2 311940.7 2006.057 ## Activity plot plot(model, type = \"activity\", event = TRUE, select = 1:6) plot(model, type = \"activity\", event = TRUE, select = \"LZ1105\") ## Tempo plot plot(model, type = \"tempo\", select = \"LZ1105\") ## Check model variability ## (results are expressed in rata die) ## Warning: this may take a few seconds  ## Jackknife fabrics jack <- jackknife(model) head(jack) #>             date     lower     upper    error       bias #> LZ1105 155634807 155628798 155640815 2164.087 2638541997 #> LZ1103 152396320 152393864 152398776  884.558 2583645955 #> LZ1100 154171872 154166299 154177445 2007.317 2613748366 #> LZ1099 146238877 146234544 146243210 1560.503 2479244339 #> LZ1097 145631868 145628096 145635640 1358.530 2468989755 #> LZ1096 112226452 112220882 112232021 2006.057 1902641373  ## Bootstrap of assemblages boot <- bootstrap(model, n = 30) head(boot) #>             min     mean      max       Q5      Q95 #> LZ1105 411915.0 425263.8 439858.2 414935.1 436043.2 #> LZ1103 398948.6 420824.7 445273.4 406639.4 440084.3 #> LZ1100 392277.7 422550.5 448846.9 399557.0 440925.6 #> LZ1099 396680.9 400828.0 403547.7 397742.1 403290.9 #> LZ1097 332384.6 397254.3 433582.3 374202.4 426407.7 #> LZ1096 264824.9 306408.7 373883.4 264824.9 361765.8"},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/articles/seriation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Seriation","text":"matrix seriation problem archaeology based three conditions two assumptions, Dunnell (1970) summarizes follows. homogeneity conditions state groups included seriation must: comparable duration, Belong cultural tradition, Come local area. mathematical assumptions state distribution historical temporal class: continuous time, Exhibits form unimodal curve. Theses assumptions create distributional model ordering accomplished arranging matrix class distributions approximate required pattern. resulting order inferred chronological.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/seriation.html","id":"reciprocal-ranking","dir":"Articles","previous_headings":"","what":"Reciprocal ranking","title":"Seriation","text":"Reciprocal ranking iteratively rearrange rows /columns according weighted rank data matrix convergence (Ihm 2005). given incidence matrix \\(C\\): rows \\(C\\) rearranged increasing order : \\[ x_{} = \\sum_{j = 1}^{p} j \\frac{c_{ij}}{c_{\\cdot}} \\] columns \\(C\\) rearranged similar way: \\[ y_{j} = \\sum_{= 1}^{m} \\frac{c_{ij}}{c_{\\cdot j}} \\] two steps repeated convergence. Note procedure enter infinite loop.  positive difference column mean percentage (french “écart positif au pourcentage moyen”, EPPM) represents deviation situation statistical independence (Desachy 2004). independence can interpreted absence relationships types chronological order assemblages, EPPM useful graphical tool explore significance relationship rows columns related seriation (Desachy 2004).","code":"## Build an incidence matrix with random data set.seed(12345) bin <- sample(c(TRUE, FALSE), 400, TRUE, c(0.6, 0.4)) incidence1 <- matrix(bin, nrow = 20)  ## Get seriation order on rows and columns ## If no convergence is reached before the maximum number of iterations (100),  ## it stops with a warning. (indices <- seriate_rank(incidence1, margin = c(1, 2), stop = 100)) #>  #> Permutation order for matrix seriation: #> - Row order: 6 15 12 14 2 5 10 8 13 20 16 18 19 7 9 1 3 11 17 4... #> - Column order: 9 4 2 8 3 1 6 5 16 20 15 14 12 17 13 10 19 7 11 18...  ## Permute matrix rows and columns incidence2 <- permute(incidence1, indices)  ## Plot matrix tabula::plot_heatmap(incidence1, col = c(\"white\", \"black\")) tabula::plot_heatmap(incidence2, col = c(\"white\", \"black\")) ## Replicates Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Plot frequencies and EPPM values tabula::seriograph(compiegne) ## Get seriation order for columns on EPPM using the reciprocal ranking method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #>  #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Permute columns compiegne_permuted <- permute(compiegne, indices)  ## Plot frequencies and EPPM values tabula::seriograph(compiegne_permuted)"},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/articles/seriation.html","id":"seriation","dir":"Articles","previous_headings":"Correspondence analysis","what":"Seriation","title":"Seriation","text":"Correspondence Analysis (CA) effective method seriation archaeological assemblages. order rows columns given coordinates along one dimension CA space, assumed account temporal variation. direction temporal change within correspondence analysis space arbitrary: additional information needed determine actual order time.","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni) ## Get row permutations from CA coordinates (zun_indices <- seriate_average(zuni, margin = c(1, 2))) #>  #> Permutation order for matrix seriation: #> - Row order: 372 387 350 367 110 417 364 407 357 160 344 348 35... #> - Column order: 18 14 17 16 13 15 9 8 12 11 6 7 5 10 4 2 3 1...  ## Plot CA results dimensio::biplot(zun_indices) ## Permute data matrix zuni_permuted <- permute(zuni, zun_indices)  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni_permuted)"},{"path":"https://packages.tesselle.org/kairos/dev/articles/seriation.html","id":"refining","dir":"Articles","previous_headings":"Correspondence analysis","what":"Refining","title":"Seriation","text":"Peeples Schachner (2012) propose procedure identify samples subject sampling error samples underlying structural relationships might influencing ordering along CA space. relies partial bootstrap approach CA-based seriation sample replicated n times. maximum dimension length convex hull around sample point cloud allows remove samples given cutoff value. According Peeples Schachner (2012), “[] point removal procedure [results ] reduced dataset position individuals within CA highly stable produces ordering consistent assumptions frequency seriation.”","code":"## Partial bootstrap CA ## Warning: this may take a few seconds! zuni_boot <- dimensio::bootstrap(zun_indices, n = 30)  ## Bootstrap CA results for the rows ## (add convex hull) zuni_boot |>    dimensio::viz_rows(col = \"lightgrey\", pch = 16, legend = NULL) |>    dimensio::viz_hull(col = adjustcolor(\"#004488\", alpha = 0.2))  ## Bootstrap CA results for the columns zuni_boot |>    dimensio::viz_columns(pch = 16, legend = list(x = \"topright\")) ## Replicates Peeples and Schachner 2012 results ## Samples with convex hull maximum dimension length greater than the cutoff ## value will be marked for removal. ## Define cutoff as one standard deviation above the mean fun <- function(x) { mean(x) + sd(x) } (zuni_refine <- seriate_refine(zun_indices, cutoff = fun, margin = 1)) #>  #> Permutation order for matrix seriation: #> - Row order: 372 350 387 367 110 417 364 357 407 160 344 348 35... #> - Column order: 17 18 14 16 13 15 9 8 12 11 6 10 7 5 4 2 3 1... #> Partial bootstrap refinement: #> - Cutoff value: 1.77 #> - Rows to keep: 360 of 420 (86%)  ## Plot CA results for the rows dimensio::viz_rows(zuni_refine, highlight = \"observation\", pch = c(16, 15)) ## Histogram of convex hull maximum dimension length hist(zuni_refine) ## Permute data matrix zuni_permuted2 <- permute(zuni, zuni_refine)  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni_permuted2)"},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"convenient reproducible toolkit relative absolute dating analysis chronological patterns. package includes functions chronological modeling dating archaeological assemblages count data. provides methods matrix seriation. also allows compute time point estimates density estimates occupation duration archaeological site. kairos provides methods : Matrix seriation: seriate_rank() seriate_average() Mean ceramic date estimation (South 1977): mcd() Event accumulation date estimation (Bellanger Husi 2012): event() Aoristic analysis (Ratcliffe 2000): aoristic() Chronological apportioning (Roberts et al. 2012): apportion() tabula companion package kairos provides functions visualization analysis archaeological count data.","code":"To cite kairos in publications use:    Frerebeau N (2023). _kairos: Analysis of Chronological Patterns from   Archaeological Count Data_. Université Bordeaux Montaigne, Pessac,   France. doi:10.5281/zenodo.5653896   , R package version 2.0.2,   .  A BibTeX entry for LaTeX users is    @Manual{,     author = {Nicolas Frerebeau},     title = {{kairos: Analysis of Chronological Patterns from Archaeological Count Data}},     year = {2023},     organization = {Université Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 2.0.2},     url = {https://packages.tesselle.org/kairos/},     doi = {10.5281/zenodo.5653896},   }  This package is a part of the tesselle project ."},{"path":"https://packages.tesselle.org/kairos/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"can install released version kairos CRAN : development version GitHub :","code":"install.packages(\"kairos\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/kairos\")"},{"path":"https://packages.tesselle.org/kairos/dev/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"kairos v2.0 uses aion internal date representation. Look vignette(\"aion\") start. assumes keep data tidy: variable (type/taxa) must saved column observation (sample/case) must saved row.","code":"## Load packages library(kairos) ## Data from Husi 2022 data(\"loire\", package = \"folio\") keep <- c(\"Anjou\", \"Blésois\", \"Orléanais\", \"Haut-Poitou\", \"Touraine\") loire <- subset(loire, area %in% keep)  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (weights) by group ao <- aoristic(loire_range, step = 50, weight = TRUE, groups = loire$area)  ## Plot plot(ao, col = \"grey\") ## Rate of change by group ro <- roc(ao, n = 30) plot(ro)"},{"path":"https://packages.tesselle.org/kairos/dev/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"Please note kairos project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/paper.html","id":"functionality","dir":"","previous_headings":"","what":"Functionality","title":"Summary","text":"toolkit absolute relative dating analysis chronological patterns. package includes functions chronological modeling dating archaeological assemblages count data. provides methods matrix seriation. also allows compute time point estimates density estimates occupation duration archaeological site.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/AoristicSum-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Aoristic Sum — AoristicSum-class","title":"Aoristic Sum — AoristicSum-class","text":"S4 class represent aoristic analysis results.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/AoristicSum-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Aoristic Sum — AoristicSum-class","text":"breaks RataDie vector giving date break time-blocks. weights numeric vector. groups character vector store group names (). p numeric array giving aorisitic probabilities.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/AoristicSum-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Aoristic Sum — AoristicSum-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/AoristicSum-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Aoristic Sum — AoristicSum-class","text":"code snippets , x AoristicSum object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/AoristicSum-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Aoristic Sum — AoristicSum-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/CountApportion-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Apportioning — CountApportion-class","title":"Count Apportioning — CountApportion-class","text":"S4 class represent artifact apportioning results. Gives apportioning artifact types (columns) per site (rows) per period (dim. 3).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/CountApportion-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Count Apportioning — CountApportion-class","text":".Data \\(m \\times p \\times k\\) array giving proportion artifact type (\\(p\\)) given period (\\(k\\)). p \\(m \\times p \\times k\\) array giving probability apportioning artifact type (\\(p\\)) given period (\\(k\\)). method character string specifying distribution used apportioning (type popularity curve). length-one numeric vector giving beginning period interest (years AD). length-one numeric vector giving end period interest (years AD). step length-one numeric vector giving step size, .e. width time step apportioning (years AD).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/CountApportion-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count Apportioning — CountApportion-class","text":"class inherits base array.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/CountApportion-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Count Apportioning — CountApportion-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/EventDate-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Date Model — EventDate-class","title":"Date Model — EventDate-class","text":"S4 class store event accumulation times archaeological assemblages.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/EventDate-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Date Model — EventDate-class","text":"dates length-\\(m\\) numeric vector dates expressed rata die. model multiple linear model: Gaussian multiple linear regression model fitted event date estimation prediction. keep integer vector giving subscripts CA components keep.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/EventDate-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Date Model — EventDate-class","text":"Dates internally stored rata die. class inherits dimensio::CA.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/EventDate-class.html","id":"extract","dir":"Reference","previous_headings":"","what":"Extract","title":"Date Model — EventDate-class","text":"code snippets , x EventDate object. time(x) Extract dates assemblages. coef(x) Extract model coefficients. fitted(x) Extract model fitted values. residuals(x) Extract model residuals. sigma(x) Extract residual standard deviation. terms(x) Extract model terms.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/EventDate-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Date Model — EventDate-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/IncrementTest-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Frequency Increment Test — IncrementTest-class","title":"Frequency Increment Test — IncrementTest-class","text":"S4 class represent Frequency Increment Test results.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/IncrementTest-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Frequency Increment Test — IncrementTest-class","text":"statistic numeric vector giving values t-statistic. parameter integer giving degrees freedom t-statistic. p_value numeric vector giving p-value test.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/IncrementTest-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Frequency Increment Test — IncrementTest-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/IncrementTest-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Frequency Increment Test — IncrementTest-class","text":"code snippets , x IncrementTest object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/IncrementTest-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Frequency Increment Test — IncrementTest-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/MeanDate-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Date — MeanDate-class","title":"Mean Date — MeanDate-class","text":"S4 class store weighted mean date (e.g. Mean Ceramic Date) archaeological assemblages.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/MeanDate-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Mean Date — MeanDate-class","text":"dates length-\\(p\\) numeric vector giving dates (ceramic) types expressed rata die. replications numeric matrix giving replications.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/MeanDate-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mean Date — MeanDate-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/MeanDate-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Mean Date — MeanDate-class","text":"code snippets , x MeanDate object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/MeanDate-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean Date — MeanDate-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/PermutationOrder-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutation Order — PermutationOrder-class","title":"Permutation Order — PermutationOrder-class","text":"S4 classes represent permutation order.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/PermutationOrder-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Permutation Order — PermutationOrder-class","text":"rows_order integer vector giving rows permutation. columns_order integer vector giving columns permutation.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/PermutationOrder-class.html","id":"subset","dir":"Reference","previous_headings":"","what":"Subset","title":"Permutation Order — PermutationOrder-class","text":"code snippets , x PermutationOrder object. x[[]] Extract information slot selected subscript . length-one character vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/PermutationOrder-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Permutation Order — PermutationOrder-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RateOfChange-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate of Change — RateOfChange-class","title":"Rate of Change — RateOfChange-class","text":"S4 class represent rates change aoristic analysis.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RateOfChange-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Rate of Change — RateOfChange-class","text":"replicates non-negative integer giving number replications. groups character vector store group names ().","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RateOfChange-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rate of Change — RateOfChange-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RateOfChange-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Rate of Change — RateOfChange-class","text":"code snippets , x AoristicSum object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/RateOfChange-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rate of Change — RateOfChange-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RefinePermutationOrder-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial Bootstrap CA — RefinePermutationOrder-class","title":"Partial Bootstrap CA — RefinePermutationOrder-class","text":"S4 class store partial bootstrap correspondence analysis results.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RefinePermutationOrder-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Partial Bootstrap CA — RefinePermutationOrder-class","text":"length numeric vector giving convex hull maximum dimension length. cutoff length-one numeric vector giving cutoff value samples selection. keep integer vector giving subscript variables kept.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/RefinePermutationOrder-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Partial Bootstrap CA — RefinePermutationOrder-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":null,"dir":"Reference","previous_headings":"","what":"Aoristic Analysis — aoristic","title":"Aoristic Analysis — aoristic","text":"Computes aoristic sum.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aoristic Analysis — aoristic","text":"","code":"aoristic(x, y, ...)  # S4 method for numeric,numeric aoristic(   x,   y,   step = 1,   start = min(x),   end = max(y),   calendar = CE(),   weight = TRUE,   groups = NULL )  # S4 method for ANY,missing aoristic(   x,   step = 1,   start = NULL,   end = NULL,   calendar = CE(),   weight = TRUE,   groups = NULL )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aoristic Analysis — aoristic","text":"x, y numeric vector giving lower upper boundaries time intervals, respectively. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. step length-one integer vector giving step size, .e. width time step time series (defaults \\(1\\), .e. annual level). start length-one numeric vector giving beginning time window. end length-one numeric vector giving end time window. calendar TimeScale object specifying calendar x y (see calendar()). Defaults Gregorian Common Era. weight logical scalar: aoristic sum weighted length periods (default). FALSE aoristic sum number elements within time block. groups factor vector sense .factor(groups) defines grouping. x list (data.frame), groups can length-one vector giving index grouping component (column) x.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aoristic Analysis — aoristic","text":"AoristicSum object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aoristic Analysis — aoristic","text":"Aoristic analysis used determine probability contemporaneity archaeological sites assemblages. aoristic analysis distributes probability event uniformly temporal fraction period considered. aoristic sum distribution total number events assumed within period. Muller Hinz (2018) pointed overlapping temporal intervals related period categorization dating accuracy likely bias analysis. proposed weighting method overcome problem. method implemented (moment), see aoristAAR package.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Aoristic Analysis — aoristic","text":"Crema, E. R. (2012). Modelling Temporal Uncertainty Archaeological Analysis. Journal Archaeological Method Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3 . Johnson, . (2004). Aoristic Analysis: Seeds New Approach Mapping Archaeological Distributions Time. Ausserer, K. F., Börner, W., Goriany, M. & Karlhuber-Vöckl, L. (ed.), Enter Past - E-Way Four Dimensions Cultural Heritage, Oxford: Archaeopress, p. 448-52. BAR International Series 1227. doi:10.15496/publikation-2085 Müller-Scheeßel, N. & Hinz, M. (2018). Aoristic Research R: Correcting Temporal Categorizations Archaeology. Presented Human History Digital Future (CAA 2018), Tubingen, March 21. https://www.youtube.com/watch?v=bUBukex30QI. Palmisano, ., Bevan, . & Shennan, S. (2017). Comparing Archaeological Proxies Long-Term Population Patterns: Example Central Italy. Journal Archaeological Science, 87: 59-72. doi:10.1016/j.jas.2017.10.001 . Ratcliffe, J. H. (2000). Aoristic Analysis: Spatial Interpretation Unspecific Temporal Events. International Journal Geographical Information Science, 14(7): 669-79. doi:10.1080/136588100424963 . Ratcliffe, J. H. (2002). Aoristic Signatures Spatio-Temporal Analysis High Volume Crime Patterns. Journal Quantitative Criminology, 18(1): 23-43. doi:10.1023/:1013240828824 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Aoristic Analysis — aoristic","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aoristic Analysis — aoristic","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Chronological Apportioning — apportion","title":"Chronological Apportioning — apportion","text":"Chronological Apportioning","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chronological Apportioning — apportion","text":"","code":"apportion(object, ...)  # S4 method for data.frame apportion(   object,   s0,   s1,   t0,   t1,   from = min(s0),   to = max(s1),   step = 25,   method = c(\"uniform\", \"truncated\"),   z = 2,   progress = getOption(\"kairos.progress\") )  # S4 method for matrix apportion(   object,   s0,   s1,   t0,   t1,   from = min(s0),   to = max(s1),   step = 25,   method = c(\"uniform\", \"truncated\"),   z = 2,   progress = getOption(\"kairos.progress\") )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chronological Apportioning — apportion","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. s0 length-\\(m\\) numeric vector giving site beginning dates expressed CE years (BCE years must given negative numbers). s1 length-\\(m\\) numeric vector giving site end dates expressed CE years (BCE years must given negative numbers). t0 length-\\(p\\) numeric vector giving type beginning dates expressed CE years (BCE years must given negative numbers). t1 length-\\(p\\) numeric vector giving type end dates expressed CE years (BCE years must given negative numbers). length-one numeric vector giving beginning period interest (years CE). length-one numeric vector giving end period interest (years CE). step length-one integer vector giving step size, .e. width time step apportioning (years CE; defaults \\(25\\)). method character string specifying distribution used (type popularity curve). must one \"uniform\" (uniform distribution) \"truncated\" (truncated standard normal distribution). unambiguous substring can given. z integer value giving lower upper truncation points (defaults \\(2\\)). used method \"truncated\". progress logical scalar: progress bar displayed?","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Chronological Apportioning — apportion","text":"CountApportion object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chronological Apportioning — apportion","text":"Roberts, J. M., Mills, B. J., Clark, J. J., Haas, W. R., Huntley, D. L. & Trowbridge, M. . (2012). Method Chronological Apportioning Ceramic Assemblages. Journal Archaeological Science, 39(5): 1513-20. doi:10.1016/j.jas.2011.12.022 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Chronological Apportioning — apportion","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/data.frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Data Frame — data.frame","title":"Coerce to a Data Frame — data.frame","text":"Coerce Data Frame","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/data.frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Data Frame — data.frame","text":"","code":"# S4 method for MeanDate as.data.frame(x, ..., calendar = getOption(\"kairos.calendar\"))  # S4 method for AoristicSum as.data.frame(x, ..., calendar = getOption(\"kairos.calendar\"))  # S4 method for IncrementTest as.data.frame(x, row.names = NULL, optional = FALSE, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/data.frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Data Frame — data.frame","text":"x object. ... parameters passed data.frame(). calendar TimeScale object specifying target calendar (see calendar()). NULL, rata die returned. row.names, optional Currently used.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/data.frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Data Frame — data.frame","text":"data.frame extra time column giving (decimal) years time series sampled.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/data.frame.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Data Frame — data.frame","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":null,"dir":"Reference","previous_headings":"","what":"Event and Accumulation Dates — event","title":"Event and Accumulation Dates — event","text":"Fits date event model.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Event and Accumulation Dates — event","text":"","code":"event(object, dates, ...)  # S4 method for data.frame,numeric event(object, dates, rank = NULL, sup_row = NULL, calendar = CE(), ...)  # S4 method for matrix,numeric event(object, dates, rank = NULL, sup_row = NULL, calendar = CE(), ...)  # S4 method for EventDate summary(object, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Event and Accumulation Dates — event","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates numeric vector dates. named, names must match row names object. ... arguments passed internal methods. rank integer specifying number CA factorial components use linear model fitting (see details). NULL (default), axes corresponding least 60% inertia used. sup_row numeric logical vector specifying indices supplementary rows. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Event and Accumulation Dates — event","text":"EventDate object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Event and Accumulation Dates — event","text":"implementation chronological modeling method proposed Bellanger Husi (2012, 2013). Event accumulation dates density estimates occupation duration archaeological site (Bellanger Husi 2012, 2013). event date estimation terminus post-quem archaeological assemblage. accumulation date represents \"chronological profile\" assemblage. According Bellanger Husi (2012), accumulation date can interpreted \"best [...] formation process reflecting duration succession events scale archaeological time, worst, imprecise dating due contamination context residual intrusive material.\" words, accumulation dates estimate occurrence archaeological events rhythms long term. Dates converted rata die computation. method relies strong archaeological statistical assumptions (see vignette(\"event\")).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Event and Accumulation Dates — event","text":"Bellanger, L. & Husi, P. (2013). Mesurer et modéliser le temps inscrit dans la matière à partir d'une source matérielle : la céramique médiévale. Mesure et Histoire Médiévale. Histoire ancienne et médiévale. Paris: Publication de la Sorbonne, p. 119-134. Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Bellanger, L., Tomassone, R. & Husi, P. (2008). Statistical Approach Dating Archaeological Contexts. Journal Data Science, 6, 135-154. Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes archéologiques. Revue de Statistique Appliquée, 54(2), 65-81. Bellanger, L., Husi, P. & Tomassone, R. (2006). Statistical Aspects Pottery Quantification Dating Archaeological Contexts. Archaeometry, 48(1), 169-183. doi:10.1111/j.1475-4754.2006.00249.x . Poblome, J. & Groenen, P. J. F. (2003). Constrained Correspondence Analysis Seriation Sagalassos Tablewares. Doerr, M. & Apostolis, S. (eds.), Digital Heritage Archaeology. Athens: Hellenic Ministry Culture.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Event and Accumulation Dates — event","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Event and Accumulation Dates — event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Frequency Increment Test — fit","title":"Frequency Increment Test — fit","text":"Frequency Increment Test","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Frequency Increment Test — fit","text":"","code":"fit(object, dates, ...)  # S4 method for data.frame,numeric fit(object, dates, calendar = CE(), level = 0.95, roll = FALSE, window = 3)  # S4 method for matrix,numeric fit(object, dates, calendar = CE(), level = 0.95, roll = FALSE, window = 3)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Frequency Increment Test — fit","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates length-\\(m\\) numeric vector dates. ... Currently used. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era. level length-one numeric vector giving confidence level. roll logical scalar: time series subsetted look episodes selection? window odd integer giving size rolling window. used roll TRUE.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Frequency Increment Test — fit","text":"IncrementTest object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Frequency Increment Test — fit","text":"Frequency Increment Test (FIT) rejects neutrality distribution normalized variant frequency increments exhibits mean deviates significantly zero. roll TRUE, time series subsetted according window see episodes selection can identified among variables might show overall selection.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Frequency Increment Test — fit","text":"Feder, . F., Kryazhimskiy, S. & Plotkin, J. B. (2014). Identifying Signatures Selection Genetic Time Series. Genetics, 196(2): 509-522. doi:10.1534/genetics.113.158220 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Frequency Increment Test — fit","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Frequency Increment Test — fit","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Group by phase ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Frequency Increment Test freq <- fit(counts, dates, calendar = NULL)  ## Plot time vs abundance plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")   ## Plot time vs abundance and highlight selection freq <- fit(counts, dates, calendar = NULL, roll = TRUE, window = 5) plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")"},{"path":"https://packages.tesselle.org/kairos/dev/reference/kairos-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in kairos — kairos-deprecated","title":"Deprecated Functions in kairos — kairos-deprecated","text":"functions still work removed (defunct) next version.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/kairos-package.html","id":null,"dir":"Reference","previous_headings":"","what":"kairos: Analysis of Chronological Patterns from Archaeological Count Data — kairos-package","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data — kairos-package","text":"toolkit absolute relative dating analysis chronological patterns. package includes functions chronological modeling dating archaeological assemblages count data. provides methods matrix seriation. also allows compute time point estimates density estimates occupation duration archaeological site.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/kairos-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data — kairos-package","text":"kairos uses following options() configure behavior: kairos.progress: logical scalar. progress bars displayed? kairos.calendar: aion::TimeScale object (default calendar printing).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/kairos-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data — kairos-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 F-33607 Pessac cedex France","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Ceramic Date — mcd","title":"Mean Ceramic Date — mcd","text":"Estimates Mean Ceramic Date assemblage.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean Ceramic Date — mcd","text":"","code":"mcd(object, dates, ...)  # S4 method for numeric,numeric mcd(object, dates, calendar = CE())  # S4 method for data.frame,numeric mcd(object, dates, calendar = CE())  # S4 method for matrix,numeric mcd(object, dates, calendar = CE())"},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean Ceramic Date — mcd","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates length-\\(p\\) numeric vector dates expressed years. ... Currently used. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean Ceramic Date — mcd","text":"MeanDate object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mean Ceramic Date — mcd","text":"Mean Ceramic Date (MCD) point estimate occupation archaeological site (South 1977). MCD estimated weighted mean date midpoints ceramic types (based absolute dates known production interval) found given assemblage. weights relative frequencies respective types assemblage. bootstrapping procedure used estimate confidence interval given MCD. assemblage, large number new bootstrap replicates created, sample size, resampling original assemblage replacement. MCDs calculated replicates upper lower boundaries confidence interval associated MCD returned.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mean Ceramic Date — mcd","text":"South, S. . (1977). Method Theory Historical Archaeology. New York: Academic Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean Ceramic Date — mcd","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean Ceramic Date — mcd","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Set the start and end dates for each ceramic type dates <- list(   LINO = c(600, 875), KIAT = c(850, 950), RED = c(900, 1050),   GALL = c(1025, 1125), ESC = c(1050, 1150), PUBW = c(1050, 1150),   RES = c(1000, 1200), TULA = c(1175, 1300), PINE = c(1275, 1350),   PUBR = c(1000, 1200), WING = c(1100, 1200), WIPO = c(1125, 1225),   SJ = c(1200, 1300), LSJ = c(1250, 1300), SPR = c(1250, 1300),   PINER = c(1275, 1325), HESH = c(1275, 1450), KWAK = c(1275, 1450) )  ## Calculate date midpoints mid <- vapply(X = dates, FUN = mean, FUN.VALUE = numeric(1))  ## Calculate MCD (mc_dates <- mcd(zuni[100:125, ], dates = mid)) #> 26 x 18 x 1 time series observed between 276230.3 and 459613.1 r.d.  ## Get MCD in years CE time(mc_dates, calendar = CE()) #>  [1]  757.2912  796.6659  797.4991  952.5855  996.2952 1016.0738 1027.5011 #>  [8] 1059.5249 1073.6597 1075.5213 1089.5820 1092.8564 1100.0000 1127.7799 #> [15] 1137.1101 1200.0017 1204.3868 1207.1436 1219.4454 1227.3745 1235.4176 #> [22] 1237.5000 1238.8896 1253.1241 1256.2502 1259.3757  ## Plot plot(mc_dates)   ## Bootstrap resampling boot <- bootstrap(mc_dates, n = 30) head(boot) #>         original      mean      bias     error #> LZ0789  757.2917       NaN       NaN        NA #> LZ0783  796.6667  871.5393 74.872615 131.18539 #> LZ0782  797.5000  853.3771 55.877123  90.10455 #> LZ0778  952.5862  982.2641 29.677845  97.47512 #> LZ0777  996.2963 1001.4051  5.108790  93.74198 #> LZ0776 1016.0714 1010.6228 -5.448629  86.83343  ## Jackknife resampling jack <- jackknife(mc_dates) head(jack) #>         original      mean       bias    error #> LZ0789  757.2917  768.2870 186.921296 207.5535 #> LZ0783  796.6667  806.9974 175.621693 228.0861 #> LZ0782  797.5000  804.1715 113.415558 169.0563 #> LZ0778  952.5862  954.5205  32.882529 138.6064 #> LZ0777  996.2963  996.6640   6.251785 111.0144 #> LZ0776 1016.0714 1017.1652  18.594831  72.6602  ## Simulation sim <- simulate(mc_dates, nsim = 30) plot(sim, interval = \"range\", pch = 16)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/model.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Model Results — model","title":"Extract Model Results — model","text":"coef() extracts model coefficients (see stats::coef()). fitted() extracts model fitted values (see stats::fitted()). residuals() extracts model residuals (see stats::residuals()). sigma() extracts residual standard deviation (see stats::sigma()). terms() extracts model terms (see stats::terms()).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Model Results — model","text":"","code":"# S4 method for EventDate coef(object, calendar = NULL, ...)  # S4 method for EventDate fitted(object, calendar = NULL, ...)  # S4 method for EventDate residuals(object, calendar = NULL, ...)  # S4 method for EventDate sigma(object, calendar = NULL, ...)  # S4 method for EventDate terms(x, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Model Results — model","text":"calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned. ... Currently used. x, object EventDate object.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/model.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Model Results — model","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Model Results — model","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/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 retrieve set parts object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mutators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get or Set Parts of an Object — mutators","text":"","code":"# S4 method for AoristicSum weights(object, ...)  # S4 method for CountApportion weights(object, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/mutators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or Set Parts of an Object — mutators","text":"object object get set element(s). ... Currently used.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/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/kairos/dev/reference/permute.html","id":null,"dir":"Reference","previous_headings":"","what":"Rearranges a Data Matrix — permute","title":"Rearranges a Data Matrix — permute","text":"permute() rearranges data matrix according permutation order. get_order() returns seriation order rows /columns.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/permute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rearranges a Data Matrix — permute","text":"","code":"permute(object, order, ...)  get_order(x, ...)  # S4 method for data.frame,PermutationOrder permute(object, order)  # S4 method for matrix,PermutationOrder permute(object, order)  # S4 method for PermutationOrder get_order(x, margin = c(1, 2))"},{"path":"https://packages.tesselle.org/kairos/dev/reference/permute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rearranges a Data Matrix — permute","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. x, order PermutationOrder object giving permutation order rows columns. margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/permute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rearranges a Data Matrix — permute","text":"permute() returns permuted matrix permuted data.frame (object).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/permute.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rearranges a Data Matrix — permute","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/permute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rearranges a Data Matrix — permute","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #>  #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Aoristic Analysis — plot_aoristic","title":"Plot Aoristic Analysis — plot_aoristic","text":"Plot Aoristic Analysis","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Aoristic Analysis — plot_aoristic","text":"","code":"# S4 method for AoristicSum,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   type = c(\"bar\"),   flip = FALSE,   ncol = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for AoristicSum image(x, calendar = getOption(\"kairos.calendar\"), ...)  # S4 method for RateOfChange,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   level = 0.95,   flip = FALSE,   ncol = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Aoristic Analysis — plot_aoristic","text":"x AoristicSum object. calendar TimeScale object specifying target calendar (see calendar()). type character string specifying whether bar density plotted? must one \"bar\" \"density\". unambiguous substring can given. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... parameters passed panel (e.g. graphical parameters). level length-one numeric vector giving confidence level.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Aoristic Analysis — plot_aoristic","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Aoristic Analysis — plot_aoristic","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Aoristic Analysis — plot_aoristic","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Event Plot — plot_event","title":"Event Plot — plot_event","text":"Produces activity tempo plot.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Event Plot — plot_event","text":"","code":"# S4 method for EventDate,missing plot(   x,   type = c(\"activity\", \"tempo\"),   event = FALSE,   calendar = getOption(\"kairos.calendar\"),   select = 1,   n = 500,   eps = 1e-09,   col.accumulation = \"black\",   col.event = \"red\",   flip = FALSE,   ncol = NULL,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   ... )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Event Plot — plot_event","text":"x EventDate object. type character string indicating type plot. must one \"activity\" (default) \"tempo\" (see details). unambiguous substring can given. event logical scalar: distribution event date displayed? used type \"activity\". calendar TimeScale object specifying target calendar (see calendar()). select numeric character vector giving selection assemblage drawn. n length-one non-negative numeric vector giving desired length vector quantiles density computation. eps length-one numeric value giving cutoff values removed. col.accumulation color specification accumulation density curve. col.event color specification event density curve. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? ... parameters passed panel (e.g. graphical parameters).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Event Plot — plot_event","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"event-and-acccumulation-dates","dir":"Reference","previous_headings":"","what":"Event and Acccumulation Dates","title":"Event Plot — plot_event","text":"plot() displays probability estimate density curves archaeological assemblage dates (event accumulation dates; Bellanger Husi 2012). event date plotted line, accumulation date shown grey filled area. accumulation date can displayed tempo plot (Dye 2016) activity plot (Philippe Vibet 2020): tempo tempo plot estimates cumulative occurrence archaeological events, slope plot directly reflects pace change. activity activity plot displays first derivative tempo plot.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Event Plot — plot_event","text":"Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Dye, T. S. (2016). Long-Term Rhythms Development Hawaiian Social Stratification. Journal Archaeological Science, 71, 1-9. doi:10.1016/j.jas.2016.05.006 . Philippe, . & Vibet, M.-. (2020). Analysis Archaeological Phases Using R Package ArchaeoPhases. Journal Statistical Software, Code Snippets, 93(1), 1-25. doi:10.18637/jss.v093.c01 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Event Plot — plot_event","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Event Plot — plot_event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection of Selective Processes — plot_fit","title":"Detection of Selective Processes — plot_fit","text":"Produces abundance vs time diagram.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection of Selective Processes — plot_fit","text":"","code":"# S4 method for IncrementTest,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   col.neutral = \"#004488\",   col.selection = \"#BB5566\",   col.roll = \"grey\",   flip = FALSE,   ncol = NULL,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   ... )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection of Selective Processes — plot_fit","text":"x IncrementTest object plotted. calendar TimeScale object specifying target calendar (see calendar()). col.neutral, col.selection, col.roll vector colors. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? ... parameters passed panel (e.g. graphical parameters).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detection of Selective Processes — plot_fit","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Detection of Selective Processes — plot_fit","text":"Results frequency increment test can displayed abundance vs time diagram aid detection quantification selective processes archaeological record. roll TRUE, time series subsetted according window see episodes selection can identified among decoration types might show overall selection. , shading highlights data points fit() identifies selection.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Detection of Selective Processes — plot_fit","text":"Displaying FIT results abundance vs time diagram adapted Ben Marwick's original idea.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection of Selective Processes — plot_fit","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detection of Selective Processes — plot_fit","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Group by phase ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Frequency Increment Test freq <- fit(counts, dates, calendar = NULL)  ## Plot time vs abundance plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")   ## Plot time vs abundance and highlight selection freq <- fit(counts, dates, calendar = NULL, roll = TRUE, window = 5) plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"MCD Plot — plot_mcd","title":"MCD Plot — plot_mcd","text":"MCD Plot","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MCD Plot — plot_mcd","text":"","code":"# S4 method for MeanDate,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   decreasing = TRUE,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for SimulationMeanDate,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   interval = \"student\",   level = 0.8,   decreasing = TRUE,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MCD Plot — plot_mcd","text":"x MeanDate object. calendar TimeScale object specifying target calendar (see calendar()). decreasing logical scalar: sort increasing decreasing? main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters. interval character string giving type confidence interval returned. must one \"student\" (default), \"normal\", \"percentiles\" \"range\" (min-max). unambiguous substring can given. level length-one numeric vector giving confidence level. used interval \"range\".","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MCD Plot — plot_mcd","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"MCD Plot — plot_mcd","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MCD Plot — plot_mcd","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Set the start and end dates for each ceramic type dates <- list(   LINO = c(600, 875), KIAT = c(850, 950), RED = c(900, 1050),   GALL = c(1025, 1125), ESC = c(1050, 1150), PUBW = c(1050, 1150),   RES = c(1000, 1200), TULA = c(1175, 1300), PINE = c(1275, 1350),   PUBR = c(1000, 1200), WING = c(1100, 1200), WIPO = c(1125, 1225),   SJ = c(1200, 1300), LSJ = c(1250, 1300), SPR = c(1250, 1300),   PINER = c(1275, 1325), HESH = c(1275, 1450), KWAK = c(1275, 1450) )  ## Calculate date midpoints mid <- vapply(X = dates, FUN = mean, FUN.VALUE = numeric(1))  ## Calculate MCD (mc_dates <- mcd(zuni[100:125, ], dates = mid)) #> 26 x 18 x 1 time series observed between 276230.3 and 459613.1 r.d.  ## Get MCD in years CE time(mc_dates, calendar = CE()) #>  [1]  757.2912  796.6659  797.4991  952.5855  996.2952 1016.0738 1027.5011 #>  [8] 1059.5249 1073.6597 1075.5213 1089.5820 1092.8564 1100.0000 1127.7799 #> [15] 1137.1101 1200.0017 1204.3868 1207.1436 1219.4454 1227.3745 1235.4176 #> [22] 1237.5000 1238.8896 1253.1241 1256.2502 1259.3757  ## Plot plot(mc_dates)   ## Bootstrap resampling boot <- bootstrap(mc_dates, n = 30) head(boot) #>         original      mean       bias     error #> LZ0789  757.2917       NaN        NaN        NA #> LZ0783  796.6667  858.4295 61.7628395 121.16457 #> LZ0782  797.5000  871.5139 74.0139084  98.12367 #> LZ0778  952.5862  952.3055 -0.2807372 109.08688 #> LZ0777  996.2963 1011.9250 15.6286638  87.38330 #> LZ0776 1016.0714 1012.1865 -3.8849198  75.93731  ## Jackknife resampling jack <- jackknife(mc_dates) head(jack) #>         original      mean       bias    error #> LZ0789  757.2917  768.2870 186.921296 207.5535 #> LZ0783  796.6667  806.9974 175.621693 228.0861 #> LZ0782  797.5000  804.1715 113.415558 169.0563 #> LZ0778  952.5862  954.5205  32.882529 138.6064 #> LZ0777  996.2963  996.6640   6.251785 111.0144 #> LZ0776 1016.0714 1017.1652  18.594831  72.6602  ## Simulation sim <- simulate(mc_dates, nsim = 30) plot(sim, interval = \"range\", pch = 16)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Abundance vs Time Plot — plot_time","title":"Abundance vs Time Plot — plot_time","text":"Produces abundance vs time diagram.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abundance vs Time Plot — plot_time","text":"","code":"plot_time(object, dates, ...)  # S4 method for data.frame,numeric plot_time(object, dates, calendar = getOption(\"kairos.calendar\"), ...)  # S4 method for matrix,numeric plot_time(object, dates, calendar = getOption(\"kairos.calendar\"), ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abundance vs Time Plot — plot_time","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates numeric vector dates. ... parameters passed aion::plot(). calendar TimeScale object specifying target calendar (see calendar()).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Abundance vs Time Plot — plot_time","text":"plot_time() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Abundance vs Time Plot — plot_time","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Abundance vs Time Plot — plot_time","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Coerce the merzbach dataset to a count matrix ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Set dates ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Plot abundance vs time plot_time(counts, dates, calendar = NULL, ncol = 3, xlab = \"Phases\")"},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Event and Accumulation Dates — predict_event","title":"Predict Event and Accumulation Dates — predict_event","text":"Estimates event accumulation dates assemblage.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Event and Accumulation Dates — predict_event","text":"","code":"predict_event(object, data, ...)  predict_accumulation(object, data, ...)  # S4 method for EventDate,missing predict_event(object, margin = 1, level = 0.95, calendar = NULL)  # S4 method for EventDate,matrix predict_event(object, data, margin = 1, level = 0.95, calendar = NULL)  # S4 method for EventDate,missing predict_accumulation(object, calendar = NULL)  # S4 method for EventDate,matrix predict_accumulation(object, data, level = 0.95, calendar = NULL)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Event and Accumulation Dates — predict_event","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). data numeric matrix data.frame count data (absolute frequencies) predict event accumulation dates. ... arguments passed internal methods. margin numeric vector giving subscripts prediction applied : 1 indicates rows, 2 indicates columns. level length-one numeric vector giving confidence level. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Event and Accumulation Dates — predict_event","text":"predict_event() returns data.frame. predict_accumulation() returns data.frame.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Predict Event and Accumulation Dates — predict_event","text":"Bellanger, L. & Husi, P. (2013). Mesurer et modéliser le temps inscrit dans la matière à partir d'une source matérielle : la céramique médiévale. Mesure et Histoire Médiévale. Histoire ancienne et médiévale. Paris: Publication de la Sorbonne, p. 119-134. Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Bellanger, L., Tomassone, R. & Husi, P. (2008). Statistical Approach Dating Archaeological Contexts. Journal Data Science, 6, 135-154. Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes archéologiques. Revue de Statistique Appliquée, 54(2), 65-81. Bellanger, L., Husi, P. & Tomassone, R. (2006). Statistical Aspects Pottery Quantification Dating Archaeological Contexts. Archaeometry, 48(1), 169-183. doi:10.1111/j.1475-4754.2006.00249.x .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict Event and Accumulation Dates — predict_event","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Event and Accumulation Dates — predict_event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/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. aion AD, b2k, BC, BCE, BP, calendar, CE, start, start, time, year_axis arkhe remove_constant, remove_NA, remove_zero, replace_NA, replace_zero","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample Event Dates — resample_event","title":"Resample Event Dates — resample_event","text":"bootstrap() generate bootstrap estimations event. jackknife() generate jackknife estimations event.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample Event Dates — resample_event","text":"","code":"# S4 method for EventDate jackknife(object, level = 0.95, progress = getOption(\"kairos.progress\"), ...)  # S4 method for EventDate bootstrap(   object,   level = 0.95,   probs = c(0.05, 0.95),   n = 1000,   progress = getOption(\"kairos.progress\"),   ... )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample Event Dates — resample_event","text":"object EventDate object (typically returned event()). level length-one numeric vector giving confidence level. progress logical scalar: progress bar displayed? ... arguments passed internal methods. probs numeric vector probabilities values \\([0,1]\\). n non-negative integer specifying number bootstrap replications.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample Event Dates — resample_event","text":"data.frame.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Resample Event Dates — resample_event","text":"jackknife() used, one type/fabric removed time statistics recalculated. way, one can assess whether certain type/fabric substantial influence date estimate. three columns data.frame returned, giving results resampling procedure (jackknifing fabrics) assemblage (rows) following columns: mean jackknife mean (event date). bias jackknife estimate bias. error standard error predicted means. bootstrap() used, large number new bootstrap assemblages created, sample size, resampling original assemblage replacement. , examination bootstrap statistics makes possible pinpoint assemblages require investigation. five columns data.frame returned, giving bootstrap distribution statistics replicated assemblage (rows) following columns: min Minimum value. mean Mean value (event date). max Maximum value. Q5 Sample quantile 0.05 probability. Q95 Sample quantile 0.95 probability.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample Event Dates — resample_event","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample Mean Ceramic Dates — resample_mcd","title":"Resample Mean Ceramic Dates — resample_mcd","text":"bootstrap() generate bootstrap estimations MCD. jackknife() generate jackknife estimations MCD.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample Mean Ceramic Dates — resample_mcd","text":"","code":"# S4 method for MeanDate bootstrap(object, n = 1000, f = NULL, calendar = getOption(\"kairos.calendar\"))  # S4 method for MeanDate jackknife(object, f = NULL, calendar = getOption(\"kairos.calendar\"))  # S4 method for MeanDate simulate(object, nsim = 1000)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample Mean Ceramic Dates — resample_mcd","text":"object MeanDate object (typically returned mcd()). n non-negative integer specifying number bootstrap replications. f function takes single numeric vector (result resampling procedure) argument. calendar TimeScale object specifying target calendar (see calendar()). nsim non-negative integer specifying number simulations.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample Mean Ceramic Dates — resample_mcd","text":"f NULL, bootstrap() jackknife() return data.frame following elements (else, returns result f applied n resampled values) : original observed value. mean bootstrap/jackknife estimate mean. bias bootstrap/jackknife estimate bias. error boostrap/jackknife estimate standard erro.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample Mean Ceramic Dates — resample_mcd","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate of Change — roc","title":"Rate of Change — roc","text":"Computes rate change aoristic analysis.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate of Change — roc","text":"","code":"roc(object, ...)  # S4 method for AoristicSum roc(object, n = 100)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate of Change — roc","text":"object AoristicSum object. ... Currently used. n non-negative integer giving number replications (see details).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate of Change — roc","text":"RateOfChange object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rate of Change — roc","text":"Baxter, M. J. & Cool, H. E. M. (2016). Reinventing Wheel? Modelling Temporal Uncertainty Applications Brooch Distributions Roman Britain. Journal Archaeological Science, 66: 120-27. doi:10.1016/j.jas.2015.12.007 . Crema, E. R. (2012). Modelling Temporal Uncertainty Archaeological Analysis. Journal Archaeological Method Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rate of Change — roc","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rate of Change — roc","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Correspondence Analysis-Based Seriation — seriate_average","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"Correspondence Analysis-Based Seriation","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"","code":"seriate_average(object, ...)  # S4 method for data.frame seriate_average(object, margin = c(1, 2), axes = 1, ...)  # S4 method for matrix seriate_average(object, margin = c(1, 2), axes = 1, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns, c(2, 1) indicates columns rows. axes integer vector giving subscripts CA axes used.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"AveragePermutationOrder object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"Correspondence analysis (CA) effective method seriation archaeological assemblages. order rows columns given coordinates along one dimension CA space, assumed account temporal variation. direction temporal change within correspondence analysis space arbitrary: additional information needed determine actual order time.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"Ihm, P. (2005). Contribution History Seriation Archaeology. C. Weihs & W. Gaul (Eds.), Classification: Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. doi:10.1007/3-540-28084-7_34 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #>  #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Reciprocal Ranking Seriation — seriate_rank","title":"Reciprocal Ranking Seriation — seriate_rank","text":"Reciprocal Ranking Seriation","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reciprocal Ranking Seriation — seriate_rank","text":"","code":"seriate_rank(object, ...)  # S4 method for data.frame seriate_rank(object, EPPM = FALSE, margin = c(1, 2), stop = 100)  # S4 method for matrix seriate_rank(object, EPPM = FALSE, margin = c(1, 2), stop = 100)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reciprocal Ranking Seriation — seriate_rank","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. EPPM logical scalar: seriation computed EPPM instead raw data? margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns, c(2, 1) indicates columns rows. stop integer giving stopping rule (.e. maximum number iterations) avoid infinite loop.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reciprocal Ranking Seriation — seriate_rank","text":"RankPermutationOrder object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reciprocal Ranking Seriation — seriate_rank","text":"procedure iteratively rearrange rows /columns according weighted rank data matrix convergence. Note procedure enter infinite loop. convergence reached maximum number iterations, stops warning.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Reciprocal Ranking Seriation — seriate_rank","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 . Dunnell, R. C. (1970). Seriation Method Evaluation. American Antiquity, 35(03), 305-319. doi:10.2307/278341 . Ihm, P. (2005). Contribution History Seriation Archaeology. C. Weihs & W. Gaul (Eds.), Classification: Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. doi:10.1007/3-540-28084-7_34 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Reciprocal Ranking Seriation — seriate_rank","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reciprocal Ranking Seriation — seriate_rank","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #>  #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":null,"dir":"Reference","previous_headings":"","what":"Refine CA-based Seriation — seriate_refine","title":"Refine CA-based Seriation — seriate_refine","text":"Refine CA-based Seriation","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refine CA-based Seriation — seriate_refine","text":"","code":"seriate_refine(object, ...)  # S4 method for AveragePermutationOrder seriate_refine(object, cutoff, margin = 1, axes = 1, n = 30, ...)  # S4 method for BootstrapCA seriate_refine(object, cutoff, margin = 1, axes = 1, ...)  # S4 method for RefinePermutationOrder hist(x, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refine CA-based Seriation — seriate_refine","text":"object PermutationOrder object (typically returned seriate_average()). ... arguments passed internal methods. cutoff function takes numeric vector argument returns single numeric value (see ). margin length-one numeric vector giving subscripts refinement applied : 1 indicates rows, 2 indicates columns. axes integer vector giving subscripts CA axes used. n non-negative integer giving number bootstrap replications. x RefinePermutationOrder object","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Refine CA-based Seriation — seriate_refine","text":"seriate_refine() returns RefinePermutationOrder object. hist() called side-effects: results histogram displayed (invisibly returns x).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Refine CA-based Seriation — seriate_refine","text":"seriate_refine() allows identify samples subject sampling error samples underlying structural relationships might influencing ordering along CA space. relies partial bootstrap approach CA-based seriation sample replicated n times. maximum dimension length convex hull around sample point cloud allows remove samples given cutoff value. According Peebles Schachner (2012), \"[] point removal procedure [results ] reduced dataset position individuals within CA highly stable produces ordering consistent assumptions frequency seriation.\"","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Refine CA-based Seriation — seriate_refine","text":"hist(RefinePermutationOrder): Compute plot histogram convex hull maximum dimension length.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Refine CA-based Seriation — seriate_refine","text":"Peeples, M. ., & Schachner, G. (2012). Refining correspondence analysis-based ceramic seriation regional data sets. Journal Archaeological Science, 39(8), 2818-2827. doi:10.1016/j.jas.2012.04.040 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Refine CA-based Seriation — seriate_refine","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/series.html","id":null,"dir":"Reference","previous_headings":"","what":"Sampling Times — series","title":"Sampling Times — series","text":"Get times time series sampled.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sampling Times — series","text":"","code":"# S4 method for EventDate time(x, calendar = NULL)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sampling Times — series","text":"x R object. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sampling Times — series","text":"numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/series.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sampling Times — series","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object — subset","title":"Extract or Replace Parts of an Object — subset","text":"Operators acting objects extract replace parts.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object — subset","text":"","code":"# S4 method for MeanDate [(x, i, j, k, drop = FALSE)  # S4 method for IncrementTest [(x, i, j, k, drop = FALSE)  # S4 method for PermutationOrder,ANY,missing [[(x, i)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object — subset","text":"x object extract element(s) replace element(s). , j, k Indices specifying elements extract replace. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object — subset","text":"subsetted object.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object — subset","text":"N. Frerebeau","code":""}]
+[{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"definition","dir":"Articles","previous_headings":"","what":"Definition","title":"Event Date Model","text":"Event accumulation dates density estimates occupation duration archaeological site (Bellanger, Husi Tomassone 2006; Bellanger, Tomassone Husi 2008; Bellanger Husi 2012). event date estimation terminus post-quem archaeological assemblage. accumulation date represents “chronological profile” assemblage. According Bellanger Husi (2012), accumulation date can interpreted “best […] formation process reflecting duration succession events scale archaeological time, worst, imprecise dating due contamination context residual intrusive material.” words, accumulation dates estimate occurrence archaeological events rhythms long term.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"event-date","dir":"Articles","previous_headings":"Definition","what":"Event Date","title":"Event Date Model","text":"Event dates estimated fitting Gaussian multiple linear regression model factors resulting correspondence analysis - somewhat similar idea introduced Poblome Groenen (2003). model results known dates selection reliable contexts allows predict event dates remaining assemblages. First, correspondence analysis (CA) carried summarize information count matrix \\(X\\). correspondence analysis \\(X\\) provides coordinates \\(m\\) rows along \\(q\\) factorial components, denoted \\(f_{ik} ~\\forall \\\\left[ 1,m \\right], k \\\\left[ 1,q \\right]\\). , assuming \\(n\\) assemblages reliably dated another source, Gaussian multiple linear regression model fitted factorial components \\(n\\) dated assemblages: \\[ t^E_i = \\beta_{0} + \\sum_{k = 1}^{q} \\beta_{k} f_{ik} + \\epsilon_i ~\\forall \\[1,n] \\] \\(t^E_i\\) known date point estimate \\(\\)th assemblage, \\(\\beta_k\\) regression coefficients \\(\\epsilon_i\\) normally, identically independently distributed random variables, \\(\\epsilon_i \\sim \\mathcal{N}(0,\\sigma^2)\\). \\(n\\) equations stacked together written matrix notation \\[ t^E = F \\beta + \\epsilon \\] \\(\\epsilon \\sim \\mathcal{N}_{n}(0,\\sigma^2 I_{n})\\), \\(\\beta = \\left[ \\beta_0 \\cdots \\beta_q \\right]' \\\\mathbb{R}^{q+1}\\) \\[ F = \\begin{bmatrix} 1 & f_{11} & \\cdots & f_{1q} \\\\ 1 & f_{21} & \\cdots & f_{2q} \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ 1 & f_{n1} & \\cdots & f_{nq} \\end{bmatrix} \\] Assuming \\(F'F\\) nonsingular, ordinary least squares estimator unknown parameter vector \\(\\beta\\) : \\[ \\widehat{\\beta} = \\left( F'F \\right)^{-1} F' t^E \\] Finally, given vector CA coordinates \\(f_i\\), predicted event date assemblage \\(t^E_i\\) : \\[ \\widehat{t^E_i} = f_i \\hat{\\beta} \\] endpoints \\(100(1 − \\alpha)\\)% associated prediction confidence interval given : \\[ \\widehat{t^E_i} \\pm t_{\\alpha/2,n-q-1} \\sqrt{\\widehat{V}} \\] \\(\\widehat{V_i}\\) estimator variance prediction error:  \\[ \\widehat{V_i} = \\widehat{\\sigma}^2 \\left( f_i^T \\left( F'F \\right)^{-1} f_i + 1 \\right) \\] \\(\\widehat{\\sigma} = \\frac{\\sum_{=1}^{n} \\left( t_i - \\widehat{t^E_i} \\right)^2}{n - q - 1}\\). probability density event date \\(t^E_i\\) can described normal distribution: \\[ t^E_i \\sim \\mathcal{N}(\\widehat{t^E_i},\\widehat{V_i}) \\]","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"accumulation-date","dir":"Articles","previous_headings":"Definition","what":"Accumulation Date","title":"Event Date Model","text":"row (assemblages) columns (types) CA coordinates linked together -called transition formulae, event dates type \\(t^E_j\\) can predicted following procedure . , accumulation date \\(t^A_i\\) defined weighted mean event date ceramic types found given assemblage. weights conditional frequencies respective types assemblage (akin MCD). accumulation date estimated : \\[ \\widehat{t^A_i} = \\sum_{j = 1}^{p} \\widehat{t^E_j} \\times \\frac{x_{ij}}{x_{\\cdot}} \\] probability density accumulation date \\(t^A_i\\) can described Gaussian mixture: \\[ t^A_i \\sim \\frac{x_{ij}}{x_{\\cdot}} \\mathcal{N}(\\widehat{t^E_j},\\widehat{V_j}^2) \\] Interestingly, integral accumulation date offers estimates cumulative occurrence archaeological events, close enough definition tempo plot introduced Dye (2016).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"limitation","dir":"Articles","previous_headings":"","what":"Limitation","title":"Event Date Model","text":"Event accumulation dates estimation relies conditions assumptions matrix seriation problem. Dunnell (1970) summarizes conditions assumptions follows. homogeneity conditions state groups included seriation must: comparable duration. Belong cultural tradition. Come local area. mathematical assumptions state distribution historical temporal class: continuous time. Exhibits form unimodal curve. Theses assumptions create distributional model ordering accomplished arranging matrix class distributions approximate required pattern. resulting order inferred chronological. Predicted dates interpreted care: dates highly dependent range known dates fit regression.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"usage","dir":"Articles","previous_headings":"","what":"Usage","title":"Event Date Model","text":"package provides implementation chronological modeling method developed Bellanger Husi (2012). method slightly modified allows construction different probability density curves archaeological assemblage dates (event, activity tempo).    Resampling methods can used check stability resulting model. jackknife() used, one type/fabric removed time statistics recalculated. way, one can assess whether certain type/fabric substantial influence date estimate. bootstrap() used, large number new bootstrap assemblages created, sample size, resampling original assemblage replacement. , examination bootstrap statistics makes possible pinpoint assemblages require investigation.","code":"## Bellanger et al. did not publish the data supporting their demonstration:  ## no replication of their results is possible.  ## Here is an example using the Zuni dataset from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) ## The names of the vector entries must match the names of the assemblages zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, dates = zuni_dates, rank = 10)  ## Model summary ## (results are expressed in rata die) summary(model) #>  #> Call: #> stats::lm(formula = date ~ ., data = contexts) #>  #> Residuals: #>   LZ0852   LZ0610   LZ0578   LZ0569   LZ0563   LZ0329   LZ0322   LZ0279  #>  -479.32   351.48  1283.51   163.57 -1626.71  -290.90   950.04 -1427.33  #>   LZ0227   LZ0067   LZ0066  LZ0005Q     CS16    CS144   LZ1209  #>  -280.59   -50.24   266.02    45.83  -105.47  1016.24   183.86  #> attr(,\"class\") #> [1] \"RataDie\" #> attr(,\"class\")attr(,\"package\") #> [1] \"aion\" #>  #> Coefficients: #>             Estimate Std. Error t value Pr(>|t|)     #> (Intercept) 425015.4     1064.9 399.095 2.36e-10 *** #> F1          -57813.4      602.6 -95.938 7.08e-08 *** #> F2            9387.7      615.2  15.260 0.000108 *** #> F3           -2047.2      789.1  -2.594 0.060411 .   #> F4            3928.9     2127.1   1.847 0.138452     #> F5           -1146.2     1442.3  -0.795 0.471276     #> F6             995.1      485.4   2.050 0.109663     #> F7            1667.0     1906.7   0.874 0.431304     #> F8            4126.0     1264.9   3.262 0.031027 *   #> F9           -1889.1     1079.6  -1.750 0.155039     #> F10           -144.8      967.9  -0.150 0.888300     #> --- #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>  #> Residual standard error: 1494 on 4 degrees of freedom #> Multiple R-squared:  0.9997, Adjusted R-squared:  0.999  #> F-statistic:  1433 on 10 and 4 DF,  p-value: 1.168e-06  ## Extract model coefficients ## (convert results to Gregorian years) coef(model, calendar = CE()) #>  (Intercept)           F1           F2           F3           F4           F5  #> 1163.6512430 -158.2887134   25.7005062   -5.6059619   10.7559523   -3.1402928  #>           F6           F7           F8           F9          F10  #>    2.7236281    4.5617156   11.2951729   -5.1728421   -0.3984238  ## Extract residual standard deviation ## (convert results to Gregorian years) sigma(model, calendar = CE()) #> [1] 4.088072  ## Extract model residuals ## (convert results to Gregorian years) resid(model, calendar = CE()) #>  [1] -1.3132066  0.9602214  3.5123344  0.4453877 -4.4554781 -0.7975492 #>  [7]  2.6001038 -3.9104984 -0.7693659 -0.1399926  0.7260925  0.1228117 #> [13] -0.2908912  2.7814906  0.5009809  ## Extract model fitted values ## (convert results to Gregorian years) fitted(model, calendar = CE()) #>  [1] 1217.3105 1073.0370 1176.4849 1096.5531 1210.4540 1076.7948 1106.3999 #>  [8] 1122.9078 1104.7666  863.1376 1110.2712  858.8744 1328.2882 1259.2185 #> [15] 1250.4963 ## Estimate event dates ## (results are expressed in rata die) eve <- predict_event(model, margin = 1, level = 0.95) head(eve) #>            date    lower    upper    error #> LZ1105 426454.0 420445.5 432462.4 2164.087 #> LZ1103 417145.8 414689.9 419601.7  884.558 #> LZ1100 421968.1 416394.9 427541.4 2007.317 #> LZ1099 400974.7 396642.1 405307.4 1560.503 #> LZ1097 397176.5 393404.6 400948.3 1358.530 #> LZ1096 306371.0 300801.2 311940.7 2006.057 ## Activity plot plot(model, type = \"activity\", event = TRUE, select = 1:6) plot(model, type = \"activity\", event = TRUE, select = \"LZ1105\") ## Tempo plot plot(model, type = \"tempo\", select = \"LZ1105\") ## Check model variability ## (results are expressed in rata die) ## Warning: this may take a few seconds  ## Jackknife fabrics jack <- jackknife(model) head(jack) #>             date     lower     upper    error       bias #> LZ1105 155634807 155628798 155640815 2164.087 2638541997 #> LZ1103 152396320 152393864 152398776  884.558 2583645955 #> LZ1100 154171872 154166299 154177445 2007.317 2613748366 #> LZ1099 146238877 146234544 146243210 1560.503 2479244339 #> LZ1097 145631868 145628096 145635640 1358.530 2468989755 #> LZ1096 112226452 112220882 112232021 2006.057 1902641373  ## Bootstrap of assemblages boot <- bootstrap(model, n = 30) head(boot) #>             min     mean      max       Q5      Q95 #> LZ1105 410334.3 426428.6 439120.1 417496.8 434740.8 #> LZ1103 399338.3 419530.2 439358.0 404374.2 434841.2 #> LZ1100 402595.0 424568.7 444349.5 406580.8 439192.0 #> LZ1099 396859.2 401138.7 405976.8 398557.6 405506.1 #> LZ1097 351380.8 396090.8 422539.6 360559.3 420506.6 #> LZ1096 264824.9 317752.5 410236.2 285727.7 358410.1"},{"path":"https://packages.tesselle.org/kairos/dev/articles/event.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Event Date Model","text":"Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes archéologiques. Revue de statistique appliquée, 54(2), 65-81. URL: http://www.numdam.org/item/RSA_2006__54_2_65_0/. Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. DOI: 10.1016/j.jas.2011.06.031. Bellanger, L. & Tomassone, R. & Husi, P. (2008). Statistical Approach Dating Archaeological Contexts. Journal Data Science, 6, 135-154. Dunnell, R. C. (1970). Seriation Method Evaluation. American Antiquity, 35(3), 305-319. DOI: 10.2307/278341. Dye, T. S. (2016). Long-Term Rhythms Development Hawaiian Social Stratification. Journal Archaeological Science, 71, 1-9. DOI: 10.1016/j.jas.2016.05.006. Poblome, J. & Groenen, P. J. F. (2003). Constrained Correspondence Analysis Seriation Sagalassos Tablewares. M. Doerr & . Sarris (Eds.), Digital Heritage Archaeology Athens: Hellenic Ministry Culture.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/seriation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Seriation","text":"matrix seriation problem archaeology based three conditions two assumptions, Dunnell (1970) summarizes follows. homogeneity conditions state groups included seriation must: comparable duration, Belong cultural tradition, Come local area. mathematical assumptions state distribution historical temporal class: continuous time, Exhibits form unimodal curve. Theses assumptions create distributional model ordering accomplished arranging matrix class distributions approximate required pattern. resulting order inferred chronological.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/articles/seriation.html","id":"reciprocal-ranking","dir":"Articles","previous_headings":"","what":"Reciprocal ranking","title":"Seriation","text":"Reciprocal ranking iteratively rearrange rows /columns according weighted rank data matrix convergence (Ihm 2005). given incidence matrix \\(C\\): rows \\(C\\) rearranged increasing order : \\[ x_{} = \\sum_{j = 1}^{p} j \\frac{c_{ij}}{c_{\\cdot}} \\] columns \\(C\\) rearranged similar way: \\[ y_{j} = \\sum_{= 1}^{m} \\frac{c_{ij}}{c_{\\cdot j}} \\] two steps repeated convergence. Note procedure enter infinite loop.  positive difference column mean percentage (french “écart positif au pourcentage moyen”, EPPM) represents deviation situation statistical independence (Desachy 2004). independence can interpreted absence relationships types chronological order assemblages, EPPM useful graphical tool explore significance relationship rows columns related seriation (Desachy 2004).","code":"## Build an incidence matrix with random data set.seed(12345) bin <- sample(c(TRUE, FALSE), 400, TRUE, c(0.6, 0.4)) incidence1 <- matrix(bin, nrow = 20)  ## Get seriation order on rows and columns ## If no convergence is reached before the maximum number of iterations (100),  ## it stops with a warning. (indices <- seriate_rank(incidence1, margin = c(1, 2), stop = 100)) #>  #> Permutation order for matrix seriation: #> - Row order: 6 15 12 14 2 5 10 8 13 20 16 18 19 7 9 1 3 11 17 4... #> - Column order: 9 4 2 8 3 1 6 5 16 20 15 14 12 17 13 10 19 7 11 18...  ## Permute matrix rows and columns incidence2 <- permute(incidence1, indices)  ## Plot matrix tabula::plot_heatmap(incidence1, col = c(\"white\", \"black\")) tabula::plot_heatmap(incidence2, col = c(\"white\", \"black\")) ## Replicates Desachy 2004 data(\"compiegne\", package = \"folio\")  ## Plot frequencies and EPPM values tabula::seriograph(compiegne) ## Get seriation order for columns on EPPM using the reciprocal ranking method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #>  #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Permute columns compiegne_permuted <- permute(compiegne, indices)  ## Plot frequencies and EPPM values tabula::seriograph(compiegne_permuted)"},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/articles/seriation.html","id":"seriation","dir":"Articles","previous_headings":"Correspondence analysis","what":"Seriation","title":"Seriation","text":"Correspondence Analysis (CA) effective method seriation archaeological assemblages. order rows columns given coordinates along one dimension CA space, assumed account temporal variation. direction temporal change within correspondence analysis space arbitrary: additional information needed determine actual order time.","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni) ## Get row permutations from CA coordinates (zun_indices <- seriate_average(zuni, margin = c(1, 2))) #>  #> Permutation order for matrix seriation: #> - Row order: 372 387 350 367 110 417 364 407 357 160 344 348 35... #> - Column order: 18 14 17 16 13 15 9 8 12 11 6 7 5 10 4 2 3 1...  ## Plot CA results biplot(zun_indices) ## Permute data matrix zuni_permuted <- permute(zuni, zun_indices)  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni_permuted)"},{"path":"https://packages.tesselle.org/kairos/dev/articles/seriation.html","id":"refining","dir":"Articles","previous_headings":"Correspondence analysis","what":"Refining","title":"Seriation","text":"Peeples Schachner (2012) propose procedure identify samples subject sampling error samples underlying structural relationships might influencing ordering along CA space. relies partial bootstrap approach CA-based seriation sample replicated n times. maximum dimension length convex hull around sample point cloud allows remove samples given cutoff value. According Peeples Schachner (2012), “[] point removal procedure [results ] reduced dataset position individuals within CA highly stable produces ordering consistent assumptions frequency seriation.”","code":"## Partial bootstrap CA ## Warning: this may take a few seconds! zuni_boot <- bootstrap(zun_indices, n = 30)  ## Bootstrap CA results for the rows ## (add convex hull) zuni_boot |>    viz_rows(col = \"lightgrey\", pch = 16, legend = NULL) |>    viz_hull(col = adjustcolor(\"#004488\", alpha = 0.2)) #> Warning: Consider specifying symbols manually: more than 6 becomes difficult to #> discriminate.  ## Bootstrap CA results for the columns zuni_boot |>    viz_columns(pch = 16, legend = list(x = \"topright\")) #> Warning: Consider specifying symbols manually: more than 6 becomes difficult to #> discriminate. ## Replicates Peeples and Schachner 2012 results ## Samples with convex hull maximum dimension length greater than the cutoff ## value will be marked for removal. ## Define cutoff as one standard deviation above the mean fun <- function(x) { mean(x) + sd(x) } (zuni_refine <- seriate_refine(zun_indices, cutoff = fun, margin = 1)) #>  #> Permutation order for matrix seriation: #> - Row order: 372 350 387 367 110 417 364 357 407 160 344 348 35... #> - Column order: 17 18 14 16 13 15 9 8 12 11 6 10 7 5 4 2 3 1... #> Partial bootstrap refinement: #> - Cutoff value: 1.77 #> - Rows to keep: 360 of 420 (86%)  ## Plot CA results for the rows viz_rows(zuni_refine, highlight = \"observation\", pch = c(16, 15)) ## Histogram of convex hull maximum dimension length hist(zuni_refine) ## Permute data matrix zuni_permuted2 <- permute(zuni, zuni_refine)  ## Ford diagram par(cex.axis = 0.7) tabula::plot_ford(zuni_permuted2)"},{"path":"https://packages.tesselle.org/kairos/dev/articles/seriation.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Seriation","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. Dunnell, R. C. (1970). Seriation Method Evaluation. American Antiquity, 35(03), 305-319. DOI: 10.2307/278341. Ihm, P. (2005). Contribution History Seriation Archaeology. C. Weihs & W. Gaul (Eds.), Classification: Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. DOI: 10.1007/3-540-28084-7_34. Peeples, M. . & Schachner, G. (2012). Refining correspondence analysis-based ceramic seriation regional data sets. Journal Archaeological Science, 39(8), 2818-2827. DOI: 10.1016/j.jas.2012.04.040.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"convenient reproducible toolkit relative absolute dating analysis chronological patterns. package includes functions chronological modeling dating archaeological assemblages count data. provides methods matrix seriation. also allows compute time point estimates density estimates occupation duration archaeological site. kairos provides methods : Matrix seriation: seriate_rank() seriate_average() Mean ceramic date estimation (South 1977): mcd() Event accumulation date estimation (Bellanger Husi 2012): event() Aoristic analysis (Ratcliffe 2000): aoristic() Chronological apportioning (Roberts et al. 2012): apportion() tabula companion package kairos provides functions visualization analysis archaeological count data.","code":"To cite kairos in publications use:    Frerebeau N (2024). _kairos: Analysis of Chronological Patterns from   Archaeological Count Data_. Université Bordeaux Montaigne, Pessac,   France. doi:10.5281/zenodo.5653896   , R package version 2.0.2,   .  A BibTeX entry for LaTeX users is    @Manual{R-kairos,     author = {Nicolas Frerebeau},     title = {{kairos: Analysis of Chronological Patterns from Archaeological Count Data}},     year = {2024},     organization = {Université Bordeaux Montaigne},     address = {Pessac, France},     note = {R package version 2.0.2},     url = {https://packages.tesselle.org/kairos/},     doi = {10.5281/zenodo.5653896},   }  This package is a part of the tesselle project ."},{"path":"https://packages.tesselle.org/kairos/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"can install released version kairos CRAN : development version GitHub :","code":"install.packages(\"kairos\") # install.packages(\"remotes\") remotes::install_github(\"tesselle/kairos\")"},{"path":"https://packages.tesselle.org/kairos/dev/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"kairos v2.0 uses aion internal date representation. Look vignette(\"aion\") start. assumes keep data tidy: variable (type/taxa) must saved column observation (sample/case) must saved row.","code":"## Load packages library(kairos) #> Loading required package: dimensio ## Data from Husi 2022 data(\"loire\", package = \"folio\") keep <- c(\"Anjou\", \"Blésois\", \"Orléanais\", \"Haut-Poitou\", \"Touraine\") loire <- subset(loire, area %in% keep)  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (weights) by group ao <- aoristic(loire_range, step = 50, weight = TRUE, groups = loire$area)  ## Plot plot(ao, col = \"grey\") ## Rate of change by group ro <- roc(ao, n = 30) plot(ro)"},{"path":"https://packages.tesselle.org/kairos/dev/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Analysis of Chronological Patterns from Archaeological Count Data","text":"Please note kairos project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/paper.html","id":"functionality","dir":"","previous_headings":"","what":"Functionality","title":"Summary","text":"toolkit absolute relative dating analysis chronological patterns. package includes functions chronological modeling dating archaeological assemblages count data. provides methods matrix seriation. also allows compute time point estimates density estimates occupation duration archaeological site.","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/AoristicSum-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Aoristic Sum — AoristicSum-class","title":"Aoristic Sum — AoristicSum-class","text":"S4 class represent aoristic analysis results.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/AoristicSum-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Aoristic Sum — AoristicSum-class","text":"breaks RataDie vector giving date break time-blocks. weights numeric vector. groups character vector store group names (). p numeric array giving aorisitic probabilities.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/AoristicSum-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Aoristic Sum — AoristicSum-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/AoristicSum-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Aoristic Sum — AoristicSum-class","text":"code snippets , x AoristicSum object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/AoristicSum-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Aoristic Sum — AoristicSum-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/CountApportion-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Apportioning — CountApportion-class","title":"Count Apportioning — CountApportion-class","text":"S4 class represent artifact apportioning results. Gives apportioning artifact types (columns) per site (rows) per period (dim. 3).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/CountApportion-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Count Apportioning — CountApportion-class","text":".Data \\(m \\times p \\times k\\) array giving proportion artifact type (\\(p\\)) given period (\\(k\\)). p \\(m \\times p \\times k\\) array giving probability apportioning artifact type (\\(p\\)) given period (\\(k\\)). method character string specifying distribution used apportioning (type popularity curve). length-one numeric vector giving beginning period interest (years AD). length-one numeric vector giving end period interest (years AD). step length-one numeric vector giving step size, .e. width time step apportioning (years AD).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/CountApportion-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count Apportioning — CountApportion-class","text":"class inherits base array.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/CountApportion-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Count Apportioning — CountApportion-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/EventDate-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Date Model — EventDate-class","title":"Date Model — EventDate-class","text":"S4 class store event accumulation times archaeological assemblages.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/EventDate-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Date Model — EventDate-class","text":"dates length-\\(m\\) numeric vector dates expressed rata die. model multiple linear model: Gaussian multiple linear regression model fitted event date estimation prediction. keep integer vector giving subscripts CA components keep.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/EventDate-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Date Model — EventDate-class","text":"Dates internally stored rata die. class inherits dimensio::CA.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/EventDate-class.html","id":"extract","dir":"Reference","previous_headings":"","what":"Extract","title":"Date Model — EventDate-class","text":"code snippets , x EventDate object. time(x) Extract dates assemblages. coef(x) Extract model coefficients. fitted(x) Extract model fitted values. residuals(x) Extract model residuals. sigma(x) Extract residual standard deviation. terms(x) Extract model terms.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/EventDate-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Date Model — EventDate-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/IncrementTest-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Frequency Increment Test — IncrementTest-class","title":"Frequency Increment Test — IncrementTest-class","text":"S4 class represent Frequency Increment Test results.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/IncrementTest-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Frequency Increment Test — IncrementTest-class","text":"statistic numeric vector giving values t-statistic. parameter integer giving degrees freedom t-statistic. p_value numeric vector giving p-value test.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/IncrementTest-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Frequency Increment Test — IncrementTest-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/IncrementTest-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Frequency Increment Test — IncrementTest-class","text":"code snippets , x IncrementTest object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/IncrementTest-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Frequency Increment Test — IncrementTest-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/MeanDate-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Date — MeanDate-class","title":"Mean Date — MeanDate-class","text":"S4 class store weighted mean date (e.g. Mean Ceramic Date) archaeological assemblages.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/MeanDate-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Mean Date — MeanDate-class","text":"dates length-\\(p\\) numeric vector giving dates (ceramic) types expressed rata die. replications numeric matrix giving replications.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/MeanDate-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Mean Date — MeanDate-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/MeanDate-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Mean Date — MeanDate-class","text":"code snippets , x MeanDate object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/MeanDate-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean Date — MeanDate-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/PermutationOrder-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutation Order — PermutationOrder-class","title":"Permutation Order — PermutationOrder-class","text":"S4 classes represent permutation order.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/PermutationOrder-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Permutation Order — PermutationOrder-class","text":"rows_order integer vector giving rows permutation. columns_order integer vector giving columns permutation.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/PermutationOrder-class.html","id":"subset","dir":"Reference","previous_headings":"","what":"Subset","title":"Permutation Order — PermutationOrder-class","text":"code snippets , x PermutationOrder object. x[[]] Extract information slot selected subscript . length-one character vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/PermutationOrder-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Permutation Order — PermutationOrder-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RateOfChange-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate of Change — RateOfChange-class","title":"Rate of Change — RateOfChange-class","text":"S4 class represent rates change aoristic analysis.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RateOfChange-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Rate of Change — RateOfChange-class","text":"replicates non-negative integer giving number replications. groups character vector store group names ().","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RateOfChange-class.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Rate of Change — RateOfChange-class","text":"class inherits aion::TimeSeries: dates internally stored rata die.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RateOfChange-class.html","id":"coerce","dir":"Reference","previous_headings":"","what":"Coerce","title":"Rate of Change — RateOfChange-class","text":"code snippets , x AoristicSum object. .data.frame(x) Coerces data.frame.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/RateOfChange-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rate of Change — RateOfChange-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RefinePermutationOrder-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial Bootstrap CA — RefinePermutationOrder-class","title":"Partial Bootstrap CA — RefinePermutationOrder-class","text":"S4 class store partial bootstrap correspondence analysis results.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/RefinePermutationOrder-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Partial Bootstrap CA — RefinePermutationOrder-class","text":"length numeric vector giving convex hull maximum dimension length. cutoff length-one numeric vector giving cutoff value samples selection. keep integer vector giving subscript variables kept.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/RefinePermutationOrder-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Partial Bootstrap CA — RefinePermutationOrder-class","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":null,"dir":"Reference","previous_headings":"","what":"Aoristic Analysis — aoristic","title":"Aoristic Analysis — aoristic","text":"Computes aoristic sum.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aoristic Analysis — aoristic","text":"","code":"aoristic(x, y, ...)  # S4 method for numeric,numeric aoristic(   x,   y,   step = 1,   start = min(x),   end = max(y),   calendar = CE(),   weight = TRUE,   groups = NULL )  # S4 method for ANY,missing aoristic(   x,   step = 1,   start = NULL,   end = NULL,   calendar = CE(),   weight = TRUE,   groups = NULL )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aoristic Analysis — aoristic","text":"x, y numeric vector giving lower upper boundaries time intervals, respectively. y missing, attempt made interpret x suitable way (see grDevices::xy.coords()). ... Currently used. step length-one integer vector giving step size, .e. width time step time series (defaults \\(1\\), .e. annual level). start length-one numeric vector giving beginning time window. end length-one numeric vector giving end time window. calendar TimeScale object specifying calendar x y (see calendar()). Defaults Gregorian Common Era. weight logical scalar: aoristic sum weighted length periods (default). FALSE aoristic sum number elements within time block. groups factor vector sense .factor(groups) defines grouping. x list (data.frame), groups can length-one vector giving index grouping component (column) x.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aoristic Analysis — aoristic","text":"AoristicSum object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aoristic Analysis — aoristic","text":"Aoristic analysis used determine probability contemporaneity archaeological sites assemblages. aoristic analysis distributes probability event uniformly temporal fraction period considered. aoristic sum distribution total number events assumed within period. Muller Hinz (2018) pointed overlapping temporal intervals related period categorization dating accuracy likely bias analysis. proposed weighting method overcome problem. method implemented (moment), see aoristAAR package.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Aoristic Analysis — aoristic","text":"Crema, E. R. (2012). Modelling Temporal Uncertainty Archaeological Analysis. Journal Archaeological Method Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3 . Johnson, . (2004). Aoristic Analysis: Seeds New Approach Mapping Archaeological Distributions Time. Ausserer, K. F., Börner, W., Goriany, M. & Karlhuber-Vöckl, L. (ed.), Enter Past - E-Way Four Dimensions Cultural Heritage, Oxford: Archaeopress, p. 448-52. BAR International Series 1227. doi:10.15496/publikation-2085 Müller-Scheeßel, N. & Hinz, M. (2018). Aoristic Research R: Correcting Temporal Categorizations Archaeology. Presented Human History Digital Future (CAA 2018), Tubingen, March 21. https://www.youtube.com/watch?v=bUBukex30QI. Palmisano, ., Bevan, . & Shennan, S. (2017). Comparing Archaeological Proxies Long-Term Population Patterns: Example Central Italy. Journal Archaeological Science, 87: 59-72. doi:10.1016/j.jas.2017.10.001 . Ratcliffe, J. H. (2000). Aoristic Analysis: Spatial Interpretation Unspecific Temporal Events. International Journal Geographical Information Science, 14(7): 669-79. doi:10.1080/136588100424963 . Ratcliffe, J. H. (2002). Aoristic Signatures Spatio-Temporal Analysis High Volume Crime Patterns. Journal Quantitative Criminology, 18(1): 23-43. doi:10.1023/:1013240828824 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Aoristic Analysis — aoristic","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/aoristic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aoristic Analysis — aoristic","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Chronological Apportioning — apportion","title":"Chronological Apportioning — apportion","text":"Chronological Apportioning","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chronological Apportioning — apportion","text":"","code":"apportion(object, ...)  # S4 method for data.frame apportion(   object,   s0,   s1,   t0,   t1,   from = min(s0),   to = max(s1),   step = 25,   method = c(\"uniform\", \"truncated\"),   z = 2,   progress = getOption(\"kairos.progress\") )  # S4 method for matrix apportion(   object,   s0,   s1,   t0,   t1,   from = min(s0),   to = max(s1),   step = 25,   method = c(\"uniform\", \"truncated\"),   z = 2,   progress = getOption(\"kairos.progress\") )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chronological Apportioning — apportion","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. s0 length-\\(m\\) numeric vector giving site beginning dates expressed CE years (BCE years must given negative numbers). s1 length-\\(m\\) numeric vector giving site end dates expressed CE years (BCE years must given negative numbers). t0 length-\\(p\\) numeric vector giving type beginning dates expressed CE years (BCE years must given negative numbers). t1 length-\\(p\\) numeric vector giving type end dates expressed CE years (BCE years must given negative numbers). length-one numeric vector giving beginning period interest (years CE). length-one numeric vector giving end period interest (years CE). step length-one integer vector giving step size, .e. width time step apportioning (years CE; defaults \\(25\\)). method character string specifying distribution used (type popularity curve). must one \"uniform\" (uniform distribution) \"truncated\" (truncated standard normal distribution). unambiguous substring can given. z integer value giving lower upper truncation points (defaults \\(2\\)). used method \"truncated\". progress logical scalar: progress bar displayed?","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Chronological Apportioning — apportion","text":"CountApportion object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Chronological Apportioning — apportion","text":"Roberts, J. M., Mills, B. J., Clark, J. J., Haas, W. R., Huntley, D. L. & Trowbridge, M. . (2012). Method Chronological Apportioning Ceramic Assemblages. Journal Archaeological Science, 39(5): 1513-20. doi:10.1016/j.jas.2011.12.022 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/apportion.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Chronological Apportioning — apportion","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/data.frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce to a Data Frame — data.frame","title":"Coerce to a Data Frame — data.frame","text":"Coerce Data Frame","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/data.frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce to a Data Frame — data.frame","text":"","code":"# S4 method for MeanDate as.data.frame(x, ..., calendar = getOption(\"kairos.calendar\"))  # S4 method for AoristicSum as.data.frame(x, ..., calendar = getOption(\"kairos.calendar\"))  # S4 method for IncrementTest as.data.frame(x, row.names = NULL, optional = FALSE, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/data.frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce to a Data Frame — data.frame","text":"x object. ... parameters passed data.frame(). calendar TimeScale object specifying target calendar (see calendar()). NULL, rata die returned. row.names, optional Currently used.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/data.frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce to a Data Frame — data.frame","text":"data.frame extra time column giving (decimal) years time series sampled.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/data.frame.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Coerce to a Data Frame — data.frame","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":null,"dir":"Reference","previous_headings":"","what":"Event and Accumulation Dates — event","title":"Event and Accumulation Dates — event","text":"Fits date event model.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Event and Accumulation Dates — event","text":"","code":"event(object, dates, ...)  # S4 method for data.frame,numeric event(object, dates, rank = NULL, sup_row = NULL, calendar = CE(), ...)  # S4 method for matrix,numeric event(object, dates, rank = NULL, sup_row = NULL, calendar = CE(), ...)  # S4 method for EventDate summary(object, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Event and Accumulation Dates — event","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates numeric vector dates. named, names must match row names object. ... arguments passed internal methods. rank integer specifying number CA factorial components use linear model fitting (see details). NULL (default), axes corresponding least 60% inertia used. sup_row numeric logical vector specifying indices supplementary rows. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Event and Accumulation Dates — event","text":"EventDate object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Event and Accumulation Dates — event","text":"implementation chronological modeling method proposed Bellanger Husi (2012, 2013). Event accumulation dates density estimates occupation duration archaeological site (Bellanger Husi 2012, 2013). event date estimation terminus post-quem archaeological assemblage. accumulation date represents \"chronological profile\" assemblage. According Bellanger Husi (2012), accumulation date can interpreted \"best [...] formation process reflecting duration succession events scale archaeological time, worst, imprecise dating due contamination context residual intrusive material.\" words, accumulation dates estimate occurrence archaeological events rhythms long term. Dates converted rata die computation. method relies strong archaeological statistical assumptions (see vignette(\"event\")).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Event and Accumulation Dates — event","text":"Bellanger, L. & Husi, P. (2013). Mesurer et modéliser le temps inscrit dans la matière à partir d'une source matérielle : la céramique médiévale. Mesure et Histoire Médiévale. Histoire ancienne et médiévale. Paris: Publication de la Sorbonne, p. 119-134. Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Bellanger, L., Tomassone, R. & Husi, P. (2008). Statistical Approach Dating Archaeological Contexts. Journal Data Science, 6, 135-154. Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes archéologiques. Revue de Statistique Appliquée, 54(2), 65-81. Bellanger, L., Husi, P. & Tomassone, R. (2006). Statistical Aspects Pottery Quantification Dating Archaeological Contexts. Archaeometry, 48(1), 169-183. doi:10.1111/j.1475-4754.2006.00249.x . Poblome, J. & Groenen, P. J. F. (2003). Constrained Correspondence Analysis Seriation Sagalassos Tablewares. Doerr, M. & Apostolis, S. (eds.), Digital Heritage Archaeology. Athens: Hellenic Ministry Culture.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Event and Accumulation Dates — event","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Event and Accumulation Dates — event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Frequency Increment Test — fit","title":"Frequency Increment Test — fit","text":"Frequency Increment Test","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Frequency Increment Test — fit","text":"","code":"fit(object, dates, ...)  # S4 method for data.frame,numeric fit(object, dates, calendar = CE(), level = 0.95, roll = FALSE, window = 3)  # S4 method for matrix,numeric fit(object, dates, calendar = CE(), level = 0.95, roll = FALSE, window = 3)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Frequency Increment Test — fit","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates length-\\(m\\) numeric vector dates. ... Currently used. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era. level length-one numeric vector giving confidence level. roll logical scalar: time series subsetted look episodes selection? window odd integer giving size rolling window. used roll TRUE.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Frequency Increment Test — fit","text":"IncrementTest object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Frequency Increment Test — fit","text":"Frequency Increment Test (FIT) rejects neutrality distribution normalized variant frequency increments exhibits mean deviates significantly zero. roll TRUE, time series subsetted according window see episodes selection can identified among variables might show overall selection.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Frequency Increment Test — fit","text":"Feder, . F., Kryazhimskiy, S. & Plotkin, J. B. (2014). Identifying Signatures Selection Genetic Time Series. Genetics, 196(2): 509-522. doi:10.1534/genetics.113.158220 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Frequency Increment Test — fit","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Frequency Increment Test — fit","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Group by phase ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Frequency Increment Test freq <- fit(counts, dates, calendar = NULL)  ## Plot time vs abundance plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")   ## Plot time vs abundance and highlight selection freq <- fit(counts, dates, calendar = NULL, roll = TRUE, window = 5) plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")"},{"path":"https://packages.tesselle.org/kairos/dev/reference/kairos-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions in kairos — kairos-deprecated","title":"Deprecated Functions in kairos — kairos-deprecated","text":"functions still work removed (defunct) next version.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/kairos-package.html","id":null,"dir":"Reference","previous_headings":"","what":"kairos: Analysis of Chronological Patterns from Archaeological Count Data — kairos-package","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data — kairos-package","text":"toolkit absolute relative dating analysis chronological patterns. package includes functions chronological modeling dating archaeological assemblages count data. provides methods matrix seriation. also allows compute time point estimates density estimates occupation duration archaeological site.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/kairos-package.html","id":"package-options","dir":"Reference","previous_headings":"","what":"Package options","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data — kairos-package","text":"kairos uses following options() configure behavior: kairos.progress: logical scalar. progress bars displayed? kairos.calendar: aion::TimeScale object (default calendar printing).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/kairos-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"kairos: Analysis of Chronological Patterns from Archaeological Count Data — kairos-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 F-33607 Pessac cedex France","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean Ceramic Date — mcd","title":"Mean Ceramic Date — mcd","text":"Estimates Mean Ceramic Date assemblage.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean Ceramic Date — mcd","text":"","code":"mcd(object, dates, ...)  # S4 method for numeric,numeric mcd(object, dates, calendar = CE())  # S4 method for data.frame,numeric mcd(object, dates, calendar = CE())  # S4 method for matrix,numeric mcd(object, dates, calendar = CE())"},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean Ceramic Date — mcd","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates length-\\(p\\) numeric vector dates expressed years. ... Currently used. calendar TimeScale object specifying calendar dates (see calendar()). Defaults Gregorian Common Era.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean Ceramic Date — mcd","text":"MeanDate object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mean Ceramic Date — mcd","text":"Mean Ceramic Date (MCD) point estimate occupation archaeological site (South 1977). MCD estimated weighted mean date midpoints ceramic types (based absolute dates known production interval) found given assemblage. weights relative frequencies respective types assemblage. bootstrapping procedure used estimate confidence interval given MCD. assemblage, large number new bootstrap replicates created, sample size, resampling original assemblage replacement. MCDs calculated replicates upper lower boundaries confidence interval associated MCD returned.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mean Ceramic Date — mcd","text":"South, S. . (1977). Method Theory Historical Archaeology. New York: Academic Press.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Mean Ceramic Date — mcd","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean Ceramic Date — mcd","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Set the start and end dates for each ceramic type dates <- list(   LINO = c(600, 875), KIAT = c(850, 950), RED = c(900, 1050),   GALL = c(1025, 1125), ESC = c(1050, 1150), PUBW = c(1050, 1150),   RES = c(1000, 1200), TULA = c(1175, 1300), PINE = c(1275, 1350),   PUBR = c(1000, 1200), WING = c(1100, 1200), WIPO = c(1125, 1225),   SJ = c(1200, 1300), LSJ = c(1250, 1300), SPR = c(1250, 1300),   PINER = c(1275, 1325), HESH = c(1275, 1450), KWAK = c(1275, 1450) )  ## Calculate date midpoints mid <- vapply(X = dates, FUN = mean, FUN.VALUE = numeric(1))  ## Calculate MCD (mc_dates <- mcd(zuni[100:125, ], dates = mid)) #> 26 x 18 x 1 time series observed between 276230.3 and 459613.1 r.d.  ## Get MCD in years CE time(mc_dates, calendar = CE()) #>  [1]  757.2912  796.6659  797.4991  952.5855  996.2952 1016.0738 1027.5011 #>  [8] 1059.5249 1073.6597 1075.5213 1089.5820 1092.8564 1100.0000 1127.7799 #> [15] 1137.1101 1200.0017 1204.3868 1207.1436 1219.4454 1227.3745 1235.4176 #> [22] 1237.5000 1238.8896 1253.1241 1256.2502 1259.3757  ## Plot plot(mc_dates)   ## Bootstrap resampling boot <- bootstrap(mc_dates, n = 30) head(boot) #>         original      mean      bias     error #> LZ0789  757.2917       NaN       NaN        NA #> LZ0783  796.6667  871.5393 74.872615 131.18539 #> LZ0782  797.5000  853.3771 55.877123  90.10455 #> LZ0778  952.5862  982.2641 29.677845  97.47512 #> LZ0777  996.2963 1001.4051  5.108790  93.74198 #> LZ0776 1016.0714 1010.6228 -5.448629  86.83343  ## Jackknife resampling jack <- jackknife(mc_dates) head(jack) #>         original      mean       bias    error #> LZ0789  757.2917  768.2870 186.921296 207.5535 #> LZ0783  796.6667  806.9974 175.621693 228.0861 #> LZ0782  797.5000  804.1715 113.415558 169.0563 #> LZ0778  952.5862  954.5205  32.882529 138.6064 #> LZ0777  996.2963  996.6640   6.251785 111.0144 #> LZ0776 1016.0714 1017.1652  18.594831  72.6602  ## Simulation sim <- simulate(mc_dates, nsim = 30) plot(sim, interval = \"range\", pch = 16)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/model.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Model Results — model","title":"Extract Model Results — model","text":"coef() extracts model coefficients (see stats::coef()). fitted() extracts model fitted values (see stats::fitted()). residuals() extracts model residuals (see stats::residuals()). sigma() extracts residual standard deviation (see stats::sigma()). terms() extracts model terms (see stats::terms()).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Model Results — model","text":"","code":"# S4 method for EventDate coef(object, calendar = NULL, ...)  # S4 method for EventDate fitted(object, calendar = NULL, ...)  # S4 method for EventDate residuals(object, calendar = NULL, ...)  # S4 method for EventDate sigma(object, calendar = NULL, ...)  # S4 method for EventDate terms(x, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Model Results — model","text":"calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned. ... Currently used. x, object EventDate object.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/model.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract Model Results — model","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract Model Results — model","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/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 retrieve set parts object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/mutators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get or Set Parts of an Object — mutators","text":"","code":"# S4 method for AoristicSum weights(object, ...)  # S4 method for CountApportion weights(object, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/mutators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or Set Parts of an Object — mutators","text":"object object get set element(s). ... Currently used.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/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/kairos/dev/reference/permute.html","id":null,"dir":"Reference","previous_headings":"","what":"Rearranges a Data Matrix — permute","title":"Rearranges a Data Matrix — permute","text":"permute() rearranges data matrix according permutation order. get_order() returns seriation order rows /columns.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/permute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rearranges a Data Matrix — permute","text":"","code":"permute(object, order, ...)  get_order(x, ...)  # S4 method for data.frame,PermutationOrder permute(object, order)  # S4 method for matrix,PermutationOrder permute(object, order)  # S4 method for PermutationOrder get_order(x, margin = c(1, 2))"},{"path":"https://packages.tesselle.org/kairos/dev/reference/permute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rearranges a Data Matrix — permute","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. x, order PermutationOrder object giving permutation order rows columns. margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/permute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rearranges a Data Matrix — permute","text":"permute() returns permuted matrix permuted data.frame (object).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/permute.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rearranges a Data Matrix — permute","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/permute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rearranges a Data Matrix — permute","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #>  #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Aoristic Analysis — plot_aoristic","title":"Plot Aoristic Analysis — plot_aoristic","text":"Plot Aoristic Analysis","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Aoristic Analysis — plot_aoristic","text":"","code":"# S4 method for AoristicSum,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   type = c(\"bar\"),   flip = FALSE,   ncol = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for AoristicSum image(x, calendar = getOption(\"kairos.calendar\"), ...)  # S4 method for RateOfChange,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   level = 0.95,   flip = FALSE,   ncol = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Aoristic Analysis — plot_aoristic","text":"x AoristicSum object. calendar TimeScale object specifying target calendar (see calendar()). type character string specifying whether bar density plotted? must one \"bar\" \"density\". unambiguous substring can given. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... parameters passed panel (e.g. graphical parameters). level length-one numeric vector giving confidence level.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Aoristic Analysis — plot_aoristic","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Aoristic Analysis — plot_aoristic","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_aoristic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Aoristic Analysis — plot_aoristic","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Event Plot — plot_event","title":"Event Plot — plot_event","text":"Produces activity tempo plot.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Event Plot — plot_event","text":"","code":"# S4 method for EventDate,missing plot(   x,   type = c(\"activity\", \"tempo\"),   event = FALSE,   calendar = getOption(\"kairos.calendar\"),   select = 1,   n = 500,   eps = 1e-09,   col.accumulation = \"black\",   col.event = \"red\",   flip = FALSE,   ncol = NULL,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   ... )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Event Plot — plot_event","text":"x EventDate object. type character string indicating type plot. must one \"activity\" (default) \"tempo\" (see details). unambiguous substring can given. event logical scalar: distribution event date displayed? used type \"activity\". calendar TimeScale object specifying target calendar (see calendar()). select numeric character vector giving selection assemblage drawn. n length-one non-negative numeric vector giving desired length vector quantiles density computation. eps length-one numeric value giving cutoff values removed. col.accumulation color specification accumulation density curve. col.event color specification event density curve. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? ... parameters passed panel (e.g. graphical parameters).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Event Plot — plot_event","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"event-and-acccumulation-dates","dir":"Reference","previous_headings":"","what":"Event and Acccumulation Dates","title":"Event Plot — plot_event","text":"plot() displays probability estimate density curves archaeological assemblage dates (event accumulation dates; Bellanger Husi 2012). event date plotted line, accumulation date shown grey filled area. accumulation date can displayed tempo plot (Dye 2016) activity plot (Philippe Vibet 2020): tempo tempo plot estimates cumulative occurrence archaeological events, slope plot directly reflects pace change. activity activity plot displays first derivative tempo plot.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Event Plot — plot_event","text":"Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Dye, T. S. (2016). Long-Term Rhythms Development Hawaiian Social Stratification. Journal Archaeological Science, 71, 1-9. doi:10.1016/j.jas.2016.05.006 . Philippe, . & Vibet, M.-. (2020). Analysis Archaeological Phases Using R Package ArchaeoPhases. Journal Statistical Software, Code Snippets, 93(1), 1-25. doi:10.18637/jss.v093.c01 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Event Plot — plot_event","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Event Plot — plot_event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":null,"dir":"Reference","previous_headings":"","what":"Detection of Selective Processes — plot_fit","title":"Detection of Selective Processes — plot_fit","text":"Produces abundance vs time diagram.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detection of Selective Processes — plot_fit","text":"","code":"# S4 method for IncrementTest,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   col.neutral = \"#004488\",   col.selection = \"#BB5566\",   col.roll = \"grey\",   flip = FALSE,   ncol = NULL,   xlab = NULL,   ylab = NULL,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   ... )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detection of Selective Processes — plot_fit","text":"x IncrementTest object plotted. calendar TimeScale object specifying target calendar (see calendar()). col.neutral, col.selection, col.roll vector colors. flip logical scalar: y-axis (ticks numbering) flipped side 2 (left) 4 (right) series series facet \"multiple\"? ncol integer specifying number columns use facet \"multiple\". Defaults 1 4 series, otherwise 2. xlab, ylab character vector giving x y axis labels. main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x y axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? ... parameters passed panel (e.g. graphical parameters).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detection of Selective Processes — plot_fit","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Detection of Selective Processes — plot_fit","text":"Results frequency increment test can displayed abundance vs time diagram aid detection quantification selective processes archaeological record. roll TRUE, time series subsetted according window see episodes selection can identified among decoration types might show overall selection. , shading highlights data points fit() identifies selection.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Detection of Selective Processes — plot_fit","text":"Displaying FIT results abundance vs time diagram adapted Ben Marwick's original idea.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Detection of Selective Processes — plot_fit","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_fit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detection of Selective Processes — plot_fit","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Group by phase ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Frequency Increment Test freq <- fit(counts, dates, calendar = NULL)  ## Plot time vs abundance plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")   ## Plot time vs abundance and highlight selection freq <- fit(counts, dates, calendar = NULL, roll = TRUE, window = 5) plot(freq, calendar = NULL, ncol = 3, xlab = \"Phases\")"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"MCD Plot — plot_mcd","title":"MCD Plot — plot_mcd","text":"MCD Plot","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MCD Plot — plot_mcd","text":"","code":"# S4 method for MeanDate,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   decreasing = TRUE,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )  # S4 method for SimulationMeanDate,missing plot(   x,   calendar = getOption(\"kairos.calendar\"),   interval = \"student\",   level = 0.8,   decreasing = TRUE,   main = NULL,   sub = NULL,   ann = graphics::par(\"ann\"),   axes = TRUE,   frame.plot = axes,   panel.first = NULL,   panel.last = NULL,   ... )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MCD Plot — plot_mcd","text":"x MeanDate object. calendar TimeScale object specifying target calendar (see calendar()). decreasing logical scalar: sort increasing decreasing? main character string giving main title plot. sub character string giving subtitle plot. ann logical scalar: default annotation (title x, y z axis labels) appear plot? axes logical scalar: axes drawn plot? frame.plot logical scalar: box drawn around plot? panel.first expression evaluated plot axes set plotting takes place. can useful drawing background grids. panel.last expression evaluated plotting taken place axes, title box added. ... graphical parameters. interval character string giving type confidence interval returned. must one \"student\" (default), \"normal\", \"percentiles\" \"range\" (min-max). unambiguous substring can given. level length-one numeric vector giving confidence level. used interval \"range\".","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MCD Plot — plot_mcd","text":"plot() called side-effects: results graphic displayed (invisibly returns x).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"MCD Plot — plot_mcd","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_mcd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MCD Plot — plot_mcd","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Set the start and end dates for each ceramic type dates <- list(   LINO = c(600, 875), KIAT = c(850, 950), RED = c(900, 1050),   GALL = c(1025, 1125), ESC = c(1050, 1150), PUBW = c(1050, 1150),   RES = c(1000, 1200), TULA = c(1175, 1300), PINE = c(1275, 1350),   PUBR = c(1000, 1200), WING = c(1100, 1200), WIPO = c(1125, 1225),   SJ = c(1200, 1300), LSJ = c(1250, 1300), SPR = c(1250, 1300),   PINER = c(1275, 1325), HESH = c(1275, 1450), KWAK = c(1275, 1450) )  ## Calculate date midpoints mid <- vapply(X = dates, FUN = mean, FUN.VALUE = numeric(1))  ## Calculate MCD (mc_dates <- mcd(zuni[100:125, ], dates = mid)) #> 26 x 18 x 1 time series observed between 276230.3 and 459613.1 r.d.  ## Get MCD in years CE time(mc_dates, calendar = CE()) #>  [1]  757.2912  796.6659  797.4991  952.5855  996.2952 1016.0738 1027.5011 #>  [8] 1059.5249 1073.6597 1075.5213 1089.5820 1092.8564 1100.0000 1127.7799 #> [15] 1137.1101 1200.0017 1204.3868 1207.1436 1219.4454 1227.3745 1235.4176 #> [22] 1237.5000 1238.8896 1253.1241 1256.2502 1259.3757  ## Plot plot(mc_dates)   ## Bootstrap resampling boot <- bootstrap(mc_dates, n = 30) head(boot) #>         original      mean       bias     error #> LZ0789  757.2917       NaN        NaN        NA #> LZ0783  796.6667  858.4295 61.7628395 121.16457 #> LZ0782  797.5000  871.5139 74.0139084  98.12367 #> LZ0778  952.5862  952.3055 -0.2807372 109.08688 #> LZ0777  996.2963 1011.9250 15.6286638  87.38330 #> LZ0776 1016.0714 1012.1865 -3.8849198  75.93731  ## Jackknife resampling jack <- jackknife(mc_dates) head(jack) #>         original      mean       bias    error #> LZ0789  757.2917  768.2870 186.921296 207.5535 #> LZ0783  796.6667  806.9974 175.621693 228.0861 #> LZ0782  797.5000  804.1715 113.415558 169.0563 #> LZ0778  952.5862  954.5205  32.882529 138.6064 #> LZ0777  996.2963  996.6640   6.251785 111.0144 #> LZ0776 1016.0714 1017.1652  18.594831  72.6602  ## Simulation sim <- simulate(mc_dates, nsim = 30) plot(sim, interval = \"range\", pch = 16)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Abundance vs Time Plot — plot_time","title":"Abundance vs Time Plot — plot_time","text":"Produces abundance vs time diagram.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Abundance vs Time Plot — plot_time","text":"","code":"plot_time(object, dates, ...)  # S4 method for data.frame,numeric plot_time(object, dates, calendar = getOption(\"kairos.calendar\"), ...)  # S4 method for matrix,numeric plot_time(object, dates, calendar = getOption(\"kairos.calendar\"), ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Abundance vs Time Plot — plot_time","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). dates numeric vector dates. ... parameters passed aion::plot(). calendar TimeScale object specifying target calendar (see calendar()).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Abundance vs Time Plot — plot_time","text":"plot_time() called side-effects: results graphic displayed (invisibly returns object).","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Abundance vs Time Plot — plot_time","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/plot_time.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Abundance vs Time Plot — plot_time","text":"","code":"## Data from Crema et al. 2016 data(\"merzbach\", package = \"folio\")  ## Coerce the merzbach dataset to a count matrix ## Keep only decoration types that have a maximum frequency of at least 50 keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50) counts <- merzbach[, keep]  ## Set dates ## We use the row names as time coordinates (roman numerals) dates <- as.numeric(utils::as.roman(rownames(counts)))  ## Plot abundance vs time plot_time(counts, dates, calendar = NULL, ncol = 3, xlab = \"Phases\")"},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Event and Accumulation Dates — predict_event","title":"Predict Event and Accumulation Dates — predict_event","text":"Estimates event accumulation dates assemblage.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Event and Accumulation Dates — predict_event","text":"","code":"predict_event(object, data, ...)  predict_accumulation(object, data, ...)  # S4 method for EventDate,missing predict_event(object, margin = 1, level = 0.95, calendar = NULL)  # S4 method for EventDate,matrix predict_event(object, data, margin = 1, level = 0.95, calendar = NULL)  # S4 method for EventDate,missing predict_accumulation(object, calendar = NULL)  # S4 method for EventDate,matrix predict_accumulation(object, data, level = 0.95, calendar = NULL)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Event and Accumulation Dates — predict_event","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). data numeric matrix data.frame count data (absolute frequencies) predict event accumulation dates. ... arguments passed internal methods. margin numeric vector giving subscripts prediction applied : 1 indicates rows, 2 indicates columns. level length-one numeric vector giving confidence level. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Event and Accumulation Dates — predict_event","text":"predict_event() returns data.frame. predict_accumulation() returns data.frame.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Predict Event and Accumulation Dates — predict_event","text":"Bellanger, L. & Husi, P. (2013). Mesurer et modéliser le temps inscrit dans la matière à partir d'une source matérielle : la céramique médiévale. Mesure et Histoire Médiévale. Histoire ancienne et médiévale. Paris: Publication de la Sorbonne, p. 119-134. Bellanger, L. & Husi, P. (2012). Statistical Tool Dating Interpreting Archaeological Contexts Using Pottery. Journal Archaeological Science, 39(4), 777-790. doi:10.1016/j.jas.2011.06.031 . Bellanger, L., Tomassone, R. & Husi, P. (2008). Statistical Approach Dating Archaeological Contexts. Journal Data Science, 6, 135-154. Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes archéologiques. Revue de Statistique Appliquée, 54(2), 65-81. Bellanger, L., Husi, P. & Tomassone, R. (2006). Statistical Aspects Pottery Quantification Dating Archaeological Contexts. Archaeometry, 48(1), 169-183. doi:10.1111/j.1475-4754.2006.00249.x .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Predict Event and Accumulation Dates — predict_event","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/predict_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Event and Accumulation Dates — predict_event","text":"","code":"## Data from Peeples and Schachner 2012 data(\"zuni\", package = \"folio\")  ## Assume that some assemblages are reliably dated (this is NOT a real example) zuni_dates <- c(   LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,   LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,   LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,   LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 )  ## Model the event and accumulation date for each assemblage model <- event(zuni, zuni_dates, rank = 10) plot(model, select = 1:10, event = TRUE, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/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. aion AD, b2k, BC, BCE, BP, calendar, CE, start, start, time, year_axis arkhe remove_constant, remove_NA, remove_zero, replace_NA, replace_zero","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample Event Dates — resample_event","title":"Resample Event Dates — resample_event","text":"bootstrap() generate bootstrap estimations event. jackknife() generate jackknife estimations event.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample Event Dates — resample_event","text":"","code":"# S4 method for EventDate jackknife(object, level = 0.95, progress = getOption(\"kairos.progress\"), ...)  # S4 method for EventDate bootstrap(   object,   level = 0.95,   probs = c(0.05, 0.95),   n = 1000,   progress = getOption(\"kairos.progress\"),   ... )"},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample Event Dates — resample_event","text":"object EventDate object (typically returned event()). level length-one numeric vector giving confidence level. progress logical scalar: progress bar displayed? ... arguments passed internal methods. probs numeric vector probabilities values \\([0,1]\\). n non-negative integer specifying number bootstrap replications.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample Event Dates — resample_event","text":"data.frame.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Resample Event Dates — resample_event","text":"jackknife() used, one type/fabric removed time statistics recalculated. way, one can assess whether certain type/fabric substantial influence date estimate. three columns data.frame returned, giving results resampling procedure (jackknifing fabrics) assemblage (rows) following columns: mean jackknife mean (event date). bias jackknife estimate bias. error standard error predicted means. bootstrap() used, large number new bootstrap assemblages created, sample size, resampling original assemblage replacement. , examination bootstrap statistics makes possible pinpoint assemblages require investigation. five columns data.frame returned, giving bootstrap distribution statistics replicated assemblage (rows) following columns: min Minimum value. mean Mean value (event date). max Maximum value. Q5 Sample quantile 0.05 probability. Q95 Sample quantile 0.95 probability.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_event.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample Event Dates — resample_event","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_mcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample Mean Ceramic Dates — resample_mcd","title":"Resample Mean Ceramic Dates — resample_mcd","text":"bootstrap() generate bootstrap estimations MCD. jackknife() generate jackknife estimations MCD.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_mcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample Mean Ceramic Dates — resample_mcd","text":"","code":"# S4 method for MeanDate bootstrap(object, n = 1000, f = NULL, calendar = getOption(\"kairos.calendar\"))  # S4 method for MeanDate jackknife(object, f = NULL, calendar = getOption(\"kairos.calendar\"))  # S4 method for MeanDate simulate(object, nsim = 1000)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_mcd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample Mean Ceramic Dates — resample_mcd","text":"object MeanDate object (typically returned mcd()). n non-negative integer specifying number bootstrap replications. f function takes single numeric vector (result resampling procedure) argument. calendar TimeScale object specifying target calendar (see calendar()). nsim non-negative integer specifying number simulations.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_mcd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample Mean Ceramic Dates — resample_mcd","text":"f NULL, bootstrap() jackknife() return data.frame following elements (else, returns result f applied n resampled values) : original observed value. mean bootstrap/jackknife estimate mean. bias bootstrap/jackknife estimate bias. error boostrap/jackknife estimate standard erro.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/resample_mcd.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Resample Mean Ceramic Dates — resample_mcd","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":null,"dir":"Reference","previous_headings":"","what":"Rate of Change — roc","title":"Rate of Change — roc","text":"Computes rate change aoristic analysis.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rate of Change — roc","text":"","code":"roc(object, ...)  # S4 method for AoristicSum roc(object, n = 100)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rate of Change — roc","text":"object AoristicSum object. ... Currently used. n non-negative integer giving number replications (see details).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rate of Change — roc","text":"RateOfChange object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Rate of Change — roc","text":"Baxter, M. J. & Cool, H. E. M. (2016). Reinventing Wheel? Modelling Temporal Uncertainty Applications Brooch Distributions Roman Britain. Journal Archaeological Science, 66: 120-27. doi:10.1016/j.jas.2015.12.007 . Crema, E. R. (2012). Modelling Temporal Uncertainty Archaeological Analysis. Journal Archaeological Method Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rate of Change — roc","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/roc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rate of Change — roc","text":"","code":"## Data from Husi 2022 data(\"loire\", package = \"folio\")  ## Get time range loire_range <- loire[, c(\"lower\", \"upper\")]  ## Calculate aoristic sum (normal) aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE) plot(aorist_raw, col = \"grey\")   ## Calculate aoristic sum (weights) aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE) plot(aorist_weighted, col = \"grey\")   ## Calculate aoristic sum (weights) by group aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,                           groups = loire$area) plot(aorist_groups, flip = TRUE, col = \"grey\")  image(aorist_groups)   ## Rate of change roc_weighted <- roc(aorist_weighted, n = 30) plot(roc_weighted)   ## Rate of change by group roc_groups <- roc(aorist_groups, n = 30) plot(roc_groups, flip = TRUE)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":null,"dir":"Reference","previous_headings":"","what":"Correspondence Analysis-Based Seriation — seriate_average","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"Correspondence Analysis-Based Seriation","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"","code":"seriate_average(object, ...)  # S4 method for data.frame seriate_average(object, margin = c(1, 2), axes = 1, ...)  # S4 method for matrix seriate_average(object, margin = c(1, 2), axes = 1, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... arguments passed internal methods. margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns, c(2, 1) indicates columns rows. axes integer vector giving subscripts CA axes used.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"AveragePermutationOrder object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"Correspondence analysis (CA) effective method seriation archaeological assemblages. order rows columns given coordinates along one dimension CA space, assumed account temporal variation. direction temporal change within correspondence analysis space arbitrary: additional information needed determine actual order time.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"Ihm, P. (2005). Contribution History Seriation Archaeology. C. Weihs & W. Gaul (Eds.), Classification: Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. doi:10.1007/3-540-28084-7_34 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_average.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correspondence Analysis-Based Seriation — seriate_average","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #>  #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Reciprocal Ranking Seriation — seriate_rank","title":"Reciprocal Ranking Seriation — seriate_rank","text":"Reciprocal Ranking Seriation","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reciprocal Ranking Seriation — seriate_rank","text":"","code":"seriate_rank(object, ...)  # S4 method for data.frame seriate_rank(object, EPPM = FALSE, margin = c(1, 2), stop = 100)  # S4 method for matrix seriate_rank(object, EPPM = FALSE, margin = c(1, 2), stop = 100)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reciprocal Ranking Seriation — seriate_rank","text":"object \\(m \\times p\\) numeric matrix data.frame count data (absolute frequencies giving number individuals category, .e. contingency table). data.frame coerced numeric matrix via data.matrix(). ... Currently used. EPPM logical scalar: seriation computed EPPM instead raw data? margin numeric vector giving subscripts rearrangement applied : 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows columns, c(2, 1) indicates columns rows. stop integer giving stopping rule (.e. maximum number iterations) avoid infinite loop.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reciprocal Ranking Seriation — seriate_rank","text":"RankPermutationOrder object.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reciprocal Ranking Seriation — seriate_rank","text":"procedure iteratively rearrange rows /columns according weighted rank data matrix convergence. Note procedure enter infinite loop. convergence reached maximum number iterations, stops warning.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Reciprocal Ranking Seriation — seriate_rank","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 . Dunnell, R. C. (1970). Seriation Method Evaluation. American Antiquity, 35(03), 305-319. doi:10.2307/278341 . Ihm, P. (2005). Contribution History Seriation Archaeology. C. Weihs & W. Gaul (Eds.), Classification: Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. doi:10.1007/3-540-28084-7_34 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Reciprocal Ranking Seriation — seriate_rank","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reciprocal Ranking Seriation — seriate_rank","text":"","code":"## Replicates Desachy 2004 results data(\"compiegne\", package = \"folio\")  ## Get seriation order for columns on EPPM using the reciprocal averaging method ## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H (indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2)) #>  #> Permutation order for matrix seriation: #> - Row order: 1 2 3 4 5... #> - Column order: 14 1 3 11 16 12 2 5 9 13 4 7 15 10 6 8...  ## Get permutation order get_order(indices, 1) # rows #> [1] 1 2 3 4 5 get_order(indices, 2) # columns #>  [1] 14  1  3 11 16 12  2  5  9 13  4  7 15 10  6  8  ## Permute columns (new <- permute(compiegne, indices)) #>      N     A     C    K   P    L    B    E   I   M    D    G   O    J    F    H #> 5 1510 13740  8270 1740   0  460  375   20   0   0  250   40 350    5   10   80 #> 4  565 13540 10110 7210 450 1785 1520 1230   0 410  740  265 310  105  635  400 #> 3  160 12490  4220 6750 275 5930 5255 1395  30 350  980  440  10  580 1415  680 #> 2  410  6940  5800 2130 410 2410 2880 1510 620 910 3400 1080 310 2075 2280 2840 #> 1  190  6490  6900 1080  50  570 2350  670 340 740 2745  950 985 2660 3020 6700  ## See the vignette if (FALSE) { utils::vignette(\"seriation\") }"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":null,"dir":"Reference","previous_headings":"","what":"Refine CA-based Seriation — seriate_refine","title":"Refine CA-based Seriation — seriate_refine","text":"Refine CA-based Seriation","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refine CA-based Seriation — seriate_refine","text":"","code":"seriate_refine(object, ...)  # S4 method for AveragePermutationOrder seriate_refine(object, cutoff, margin = 1, axes = 1, n = 30, ...)  # S4 method for BootstrapCA seriate_refine(object, cutoff, margin = 1, axes = 1, ...)  # S4 method for RefinePermutationOrder hist(x, ...)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refine CA-based Seriation — seriate_refine","text":"object PermutationOrder object (typically returned seriate_average()). ... arguments passed internal methods. cutoff function takes numeric vector argument returns single numeric value (see ). margin length-one numeric vector giving subscripts refinement applied : 1 indicates rows, 2 indicates columns. axes integer vector giving subscripts CA axes used. n non-negative integer giving number bootstrap replications. x RefinePermutationOrder object","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Refine CA-based Seriation — seriate_refine","text":"seriate_refine() returns RefinePermutationOrder object. hist() called side-effects: results histogram displayed (invisibly returns x).","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Refine CA-based Seriation — seriate_refine","text":"seriate_refine() allows identify samples subject sampling error samples underlying structural relationships might influencing ordering along CA space. relies partial bootstrap approach CA-based seriation sample replicated n times. maximum dimension length convex hull around sample point cloud allows remove samples given cutoff value. According Peebles Schachner (2012), \"[] point removal procedure [results ] reduced dataset position individuals within CA highly stable produces ordering consistent assumptions frequency seriation.\"","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Refine CA-based Seriation — seriate_refine","text":"hist(RefinePermutationOrder): Compute plot histogram convex hull maximum dimension length.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Refine CA-based Seriation — seriate_refine","text":"Peeples, M. ., & Schachner, G. (2012). Refining correspondence analysis-based ceramic seriation regional data sets. Journal Archaeological Science, 39(8), 2818-2827. doi:10.1016/j.jas.2012.04.040 .","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/seriate_refine.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Refine CA-based Seriation — seriate_refine","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/series.html","id":null,"dir":"Reference","previous_headings":"","what":"Sampling Times — series","title":"Sampling Times — series","text":"Get times time series sampled.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/series.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sampling Times — series","text":"","code":"# S4 method for EventDate time(x, calendar = NULL)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/series.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sampling Times — series","text":"x R object. calendar TimeScale object specifying target calendar (see calendar()). NULL (default), rata die returned.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/series.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sampling Times — series","text":"numeric vector.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/series.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Sampling Times — series","text":"N. Frerebeau","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or Replace Parts of an Object — subset","title":"Extract or Replace Parts of an Object — subset","text":"Operators acting objects extract replace parts.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or Replace Parts of an Object — subset","text":"","code":"# S4 method for MeanDate [(x, i, j, k, drop = FALSE)  # S4 method for IncrementTest [(x, i, j, k, drop = FALSE)  # S4 method for PermutationOrder,ANY,missing [[(x, i)"},{"path":"https://packages.tesselle.org/kairos/dev/reference/subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or Replace Parts of an Object — subset","text":"x object extract element(s) replace element(s). , j, k Indices specifying elements extract replace. drop logical scalar: result coerced lowest possible dimension? works extracting elements, replacement.","code":""},{"path":"https://packages.tesselle.org/kairos/dev/reference/subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or Replace Parts of an Object — subset","text":"subsetted object.","code":""},{"path":[]},{"path":"https://packages.tesselle.org/kairos/dev/reference/subset.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Extract or Replace Parts of an Object — subset","text":"N. Frerebeau","code":""}]
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