A multi-proxy inference of Jōmon population dynamics using Bayesian phase models, residential data, and summed probability distribution of 14C dates: source code, data, and scripts
This repository contains an updated version of the data and scripts used in the following paper.
Crema, E.R., Kobayashi, K., 2020. A multi-proxy inference of Jōmon population dynamics using bayesian phase models, residential data, and summed probability distribution of 14C dates. Journal of Archaeological Science 117, 105136. DOI: https://doi.org/10.1016/j.jas.2020.105136
The original repository contained scripts based on the IntCal13 and Marine13 calibration curves and can be accessed here. Analyses and results contained in this repository are based on the IntCal20 and Marine20 curves and contains. This version contains also an updated version of the function mcsim() as well as a new rmarkdown file with a short Rmarkdown tutorial on how to use the function with different datasets.
The main workflow is recorded in the log.R file and outputs are stored as R image files located in the R_images directory.
All raw data used in the paper can be found in the data directory. The file c14dates.csv contains the radiocarbon dates used for the ceramic phase modelling, the folder rekihaku14C contains CSV files of radiocarbon dates downloaded from the National Museum of Japanese History's Database of radiocarbon dates published in Japanese archaeological research reports. The R script file bindC14csv.R in the same directory contains details of the query used to download the data, as well as a script for aggregating the files into an R image file (spdC14.RData) contained in the R_images directory. The sub-directory ./data/suzuki contains CSV files of Jomon pithouse counts obtained from tables on pages 88 to 93 of 「縄文時代集落の研究」("Research on Jomon Period Settlements"), by 鈴木 保彦 (Suzuki, Yasuhiko), published by 雄山閣 (Yuzankaku), Tokyo, in 2006. The data has been digitised into separate CSV files and aggregated into a long format data.frame containing a pithouse per row. This process is recorded in the R script pithouseBinder.R and the resulting data.frame is stored in the R image file (pithouseData.RData).
Bayesian modelling have been carried out using OxCal v4.4, with the preparation of OxCal scripts done in R. The analyses was conducted in three stages. Firstly, potential outliers were detected within sets of dates associated with the same event (e.g. different organic residues from the same vessel). This was achievied by using the outlierExcluder() function (stored here) which internally calls OxCal from R using the oxcAAR package. The function eliminates potential outliers from the initial set of radiocarbon dates and result of this routine was stored in the R image file c14data.RData.
The second step of analysis consisted in creating OxCal scripts for different probability distributions emulating putative within-phase uncertainty. This was achieved by using the oxcalScriptGen() function (located in this file. The output scripts (stored in the directory ./oxcal/oxcalscripts) were then loaded into OxCal for analyses. The results of the Bayesian analyses are stored in the directory ./oxcal/results, and include the .csv storing the posterior samples and a JavaScript file (.js) containing key statistics such as individual and overall agreement indices, read in R using the oxcalReadjs() function (see source code here).
The third and final step consisted of removing samples with low agreement index and generate a new set of OxCal scripts (with suffix "R" to distinguish this second submission to OxCal, e.g. gaussian.oxcal and gaussianR.oxcal). The result of the OxCal analysis was then processed and the posterior samples organised into a series of objects stored in the R image posteriorSamples.RData.
Dates of individual pithouse were simulated 5,000 times taking account: 1) the uncertatinty within the phase ( within phase uncertainty ); 2) the uncertainty in defining the membership of the pithouse to a particular phase ( phase assignement uncertainty ); and the uncertainty associated with the parameters of the probability distribution describing the within phase uncertainty (i.e. the phase boundary uncertainty). The function mcsim() (see source code here) was used for this purpose. The R image fle simdatesPithouses.RData contains the 5,000 set of simulated dates, along with counts organised in 100 years bins (between 8,000 and 3,000 cal BP), and the outcome of a composite kernel density estimate analyses (see details in the log.R file).
Summed probability distribution (SPD) of radiocarbon dates have been generated using rcarbon . To enable correlation analyses with the residential data, a matrix of 5000 sets of randomly sampled calendar dates was created and aggregated by the same 100 years intervals between 8,000 and 3,000 cal BP. All SPD analysis related R objects are stored in the R image ./R_images/spdRes.RData.
The time-series of residential and radiocarbon density have been compared via correlation analyses and the modelTest() function in rcarbon. The former was carried out by generating 5,000 correlation values by iteratively comparing the the time-series of simulated pithouse dates and randomly sampled calendar dates from the calibrated radiocarbon dates. The latter compared the observed annual growth rate in the SPD against an expectation derived from the average trend obtained from the composite kernel density estimate of pithouse frequencies over time. The results of these analyses are stored in the R image ./R_images/comp.RData.
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├── data
│ ├── c14dates.csv
│ ├── rekihaku14C
│ │ ├── bindC14csv.R
│ │ ├── kanagawa_M_B_11_3_2020.csv
│ │ ├── kanagawa_T_B_5_11_2019.csv
│ │ ├── nagano_T_B_5_11_2019.csv
│ │ ├── saitama_M_B_11_3_2020.csv
│ │ ├── saitama_T_B_5_11_2019.csv
│ │ ├── tokyo_M_B_11_3_2020.csv
│ │ ├── tokyo_M_B_11_3_2020.csv.csv
│ │ ├── tokyo_T_B_5_11_2019.csv
│ │ └── yamanashi_T_B_5_11_2019.csv
│ └── suzuki
│ ├── kanagawa.csv
│ ├── nagano.csv
│ ├── pithouseBinder.R
│ ├── saitama.csv
│ ├── tokyo.csv
│ └── yamanashi.csv
├── esm.pdf
├── esm.Rmd
├── log.R
├── manuscript
│ ├── figures
│ │ ├── figure1.pdf
│ │ ├── figure2.pdf
│ │ ├── figure3.pdf
│ │ ├── figure4.pdf
│ │ └── figurelog.R
│ └── tables
│ └── table1_base.csv
├── oxcal
│ ├── oxcalscripts
│ │ ├── gaussian.oxcal
│ │ ├── gaussianR.oxcal
│ │ ├── trapezoid.oxcal
│ │ ├── trapezoidR.oxcal
│ │ ├── uniform.oxcal
│ │ └── uniformR.oxcal
│ └── results
│ ├── gaussian.js
│ ├── gaussian.log
│ ├── gaussian.oxcal
│ ├── gaussianR.js
│ ├── gaussianR.log
│ ├── gaussianR.oxcal
│ ├── gaussianR.txt
│ ├── gaussian.txt
│ ├── mcmcGaussian.csv
│ ├── mcmcGaussianR.csv
│ ├── mcmcTrapezoid.csv
│ ├── mcmcTrapezoidR.csv
│ ├── mcmcUniform.csv
│ ├── mcmcUniformR.csv
│ ├── trapezoid.js
│ ├── trapezoid.log
│ ├── trapezoid.oxcal
│ ├── trapezoidR.js
│ ├── trapezoidR.log
│ ├── trapezoidR.oxcal
│ ├── trapezoidR.txt
│ ├── trapezoid.txt
│ ├── uniform.js
│ ├── uniform.log
│ ├── uniform.oxcal
│ ├── uniformR.js
│ ├── uniformR.log
│ ├── uniformR.oxcal
│ ├── uniformR.txt
│ └── uniform.txt
├── R
│ ├── mcsim.R
│ ├── outlierAnalysis.R
│ ├── oxcalReadjs.R
│ ├── oxcalScriptCreator.R
│ └── utilities.R
├── README.md
└── R_images
├── c14data.RData
├── comp.RData
├── pithouseData.RData
├── posteriorSamples.RData
├── simdatesPithouses.RData
├── spdC14.RData
└── spdRes.RData
attached base packages:
[1] stats graphics grDevices utils methods base
other attached packages:
[1] readr_1.3.1 trapezoid_2.0-0 TTR_0.23-5 rcarbon_1.4.1
[5] oxcAAR_1.0.0 dplyr_0.8.3 magrittr_1.5 nvimcom_0.9-82
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 pillar_1.4.2
[3] compiler_3.6.1 xts_0.11-2
[5] iterators_1.0.12 tools_3.6.1
[7] rpart_4.1-15 goftest_1.1-1
[9] jsonlite_1.6 tibble_2.1.3
[11] nlme_3.1-140 lattice_0.20-38
[13] mgcv_1.8-28 pkgconfig_2.0.3
[15] rlang_0.4.0 Matrix_1.2-17
[17] foreach_1.4.7 curl_4.2
[19] parallel_3.6.1 spatstat.data_1.4-0
[21] stringr_1.4.0 hms_0.4.2
[23] spatstat.utils_1.13-0 grid_3.6.1
[25] tidyselect_0.2.5 glue_1.3.1
[27] R6_2.4.0 sp_1.3-2
[29] polyclip_1.10-0 purrr_0.3.2
[31] deldir_0.1-23 tensor_1.5
[33] splines_3.6.1 codetools_0.2-16
[35] assertthat_0.2.1 abind_1.4-5
[37] spatstat_1.61-0 stringi_1.4.3
[39] doParallel_1.0.15 crayon_1.3.4
[41] zoo_1.8-6
This research was funded by the ERC grant Demography, Cultural Change, and the Diffusion of Rice and Millets during the Jomon-Yayoi transition in prehistoric Japan (ENCOUNTER) (Project N. 801953, PI: Enrico Crema).
CC-BY 3.0