Analysis of Chronological Patterns from Archaeological Count Data • kairos

Overview

A convenient and reproducible toolkit for relative and absolute dating and analysis of chronological patterns. This package includes functions for chronological modeling and dating of archaeological assemblages from count data. It provides methods for matrix seriation. It also allows to compute time point estimates and density estimates of the occupation and duration of an archaeological site. kairos provides methods for:

Matrix seriation: seriate_rank() and seriate_average()
Mean ceramic date estimation (South 1977): mcd()
Event and accumulation date estimation (Bellanger and Husi 2012): event()
Aoristic analysis (Ratcliffe 2000): aoristic()
Chronological apportioning (Roberts et al. 2012): apportion()

tabula is a companion package to kairos that provides functions for visualization and analysis of archaeological count data.

Installation

You can install the released version of kairos from CRAN with:

install.packages("kairos")

And the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("tesselle/kairos")

Usage

## Load packages
library(khroma) # Colour schemes
library(tabula) # Plot methods
library(kairos)

kairos only supports dates expressed in CE years (BCE years must be given as negative numbers). All results are rounded to zero decimal places (sub-annual precision does not make sense in most situations). You can change this behavior with options(kairos.precision = x) (for x decimal places).

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.

## Build an incidence matrix with random data
set.seed(12345)
incidence1 <- matrix(sample(0:1, 400, TRUE, c(0.6, 0.4)), nrow = 20)
incidence1 <- incidence1 > 0 # logical

## Get seriation order on rows and columns
(indices <- seriate_rank(incidence1, margin = c(1, 2), stop = 100))
#> <RankPermutationOrder>
#> Permutation order for matrix seriation:
#> - Row order: 1 4 20 3 9 16 19 10 13 2 11 7 17 5 6 18 14 15 8 12...
#> - Column order: 1 16 9 4 8 14 3 20 13 2 6 18 7 17 5 11 19 12 15 10...

## Permute rows and columns
incidence2 <- permute(incidence1, indices)

## Plot matrix
tabula::plot_heatmap(incidence1) +
  khroma::scale_fill_logical()
tabula::plot_heatmap(incidence2) +
  khroma::scale_fill_logical()

## Data from Husi 2022
data("loire", package = "folio")
loire <- subset(loire, area %in% c("Anjou", "Blésois", "Orléanais", 
                                   "Haut-Poitou", "Touraine"))

## Get time range
loire_range <- loire[, c("lower", "upper")]

## Calculate aoristic sum (weights) by group
aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,
                          groups = loire$area)
plot(aorist_groups)


## Rate of change by group
roc_groups <- roc(aorist_groups, n = 30)
plot(roc_groups)

Contributing

Please note that the kairos project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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.

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.

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.

South, S. A. 1977. Method and Theory in Historical Archaeology. Studies in Archeology. New York: Academic Press.