Data and R scripts for the paper 'A Bayesian approach for fitting and comparing demographic growth models of radiocarbon dates: a case study on the Jomon-Yayoi transition in Kyushu, Japan'
This repository contains data and scripts used in the following paper:
Crema, E. R., & Shoda, S. (2021). A Bayesian approach for fitting and comparing demographic growth models of radiocarbon dates: a case study on the Jomon-Yayoi transition in Kyushu (Japan). PLOS ONE, 16(5), e0251695.
The repository is organised into four main directories: data, runscripts, R_images, and manuscript. The data directory contains the the raw data used for the case study, the runscripts contains all script for generating the simulated data as well as running the core Bayesian analyses, the R_images directory includes all R Image files of all outputs, and finally R scripts for generating figures and tables for the manuscript are included in manuscript.
All raw data used in the paper can be found in the data directory. This contains the original CSV files of radiocarbon dates downloaded from the National Museum of Japanese History's Database of radiocarbon dates published in Japanese archaeological research reports, and R script file bindC14csv.R used pre-process the data with the output stored in the R image file c14data.RData. The script file data_prep.R provides an additional set of routine for calibrating and sub-setting dates for the main Bayesian analysis. Results are stored in the R image file cleaned_data.RData.
Scripts for the Bayesian Analyses of three growth models are contained in the files mcmc_m1.R, mcmc_m2.R, and mcmc_m3.R. Resulting MCMC posterior samples are stored in the R image files mmcmc.m1.samples.RData, mcmc.m2.samples.RData, and mcmc.m3.samples.RData. Notice that given the large number of MCMC samples the processing time of each script is considerably long. R scripts for the MCMC diagnostics and posterior predictive check are included in the file MCMC_diagnostic_and_ppcheck.R.
Scripts for generating simulated data for each experiment as well as the associated Bayesian analyses are contained in the files: experiment1.R, experiment2.R, experiment3a.R, experiment3b.R, and experiment4.R. Results of each experiment are stored in the R image files: experiment1_results.RData, experiment2_results.RData, experiment3a_results.RData, experiment3b_results.RData, and experiment4_results.RData.
Figures and raw data required for relevant tables, as well as R scripts required for generating them are contained in relevant sub-directory inside manuscript. All figures and tables require the analyses output stored in the R_images directory.
.
├── data
│ ├── bindC14csv.R
│ ├── c14data.RData
│ ├── fukuoka_T_7000_to_0_14122020.csv
│ ├── kagoshima_T_7000_to_0_14122020.csv
│ ├── kumamoto_T_7000_to_0_14122020.csv
│ ├── miyazaki_T_7000_to_0_14122020.csv
│ ├── nagasaki_T_7000_to_0_14122020.csv
│ ├── ooita_T_7000_to_0_14122020.csv
│ └── saga_T_7000_to_0_14122020.csv
├── manuscript
│ ├── figures
│ │ ├── figure1.tiff
│ │ ├── figure2.tiff
│ │ ├── figure3.tiff
│ │ ├── figure4.tiff
│ │ ├── figure5.tiff
│ │ ├── figure6.tiff
│ │ ├── figure7.tiff
│ │ ├── figure8.tiff
│ │ ├── figure9.tiff
│ │ └── main_figures.R
│ ├── supplementary_figures
│ │ ├── figureS1.pdf
│ │ ├── figureS2.pdf
│ │ ├── figureS3.pdf
│ │ ├── figureS4.pdf
│ │ ├── figureS5.pdf
│ │ ├── figureS6.pdf
│ │ ├── figureS7.pdf
│ │ └── supplementary_figures.R
│ └── tables
│ ├── main_tables.R
│ ├── table2.csv
│ └── table3.csv
├── README.md
├── R_images
│ ├── cleaned_data.RData
│ ├── experiment1_results.RData
│ ├── experiment2_results.RData
│ ├── experiment3a_results.RData
│ ├── experiment3b_results.RData
│ ├── experiment4_results.RData
│ ├── mcmc_diagnostics_and_ppcheck.RData
│ ├── mcmc.m1.samples.RData
│ ├── mcmc.m2.samples.RData
│ └── mcmc.m3.samples.RData
└── runscripts
├── data_prep.R
├── experiment1.R
├── experiment2.R
├── experiment3a.R
├── experiment3b.R
├── experiment4.R
├── MCMC_diagnostic_and_ppcheck.R
├── mcmc_m1.R
├── mcmc_m2.R
└── mcmc_m3.R
attached base packages:
[1] stats graphics grDevices utils methods base
other attached packages:
[1] oxcAAR_1.0.0 dplyr_1.0.2 latex2exp_0.4.0 coda_0.19-4
[5] sp_1.4-5 rnaturalearth_0.2.0 truncnorm_1.0-8 nimbleCarbon_0.1.0
[9] nimble_0.10.1 rcarbon_1.4.2 here_0.1
loaded via a namespace (and not attached):
[1] spatstat.linnet_2.0-0 tidyselect_1.1.0 xfun_0.22
[4] purrr_0.3.4 sf_0.9-6 splines_4.0.3
[7] lattice_0.20-41 spatstat.utils_2.1-0 vctrs_0.3.6
[10] generics_0.1.0 doSNOW_1.0.19 snow_0.4-3
[13] yaml_2.2.1 mgcv_1.8-31 rlang_0.4.10
[16] pillar_1.4.7 startup_0.14.1 spatstat.data_2.1-0
[19] e1071_1.7-4 spatstat_2.0-1 glue_1.4.2
[22] DBI_1.1.0 foreach_1.5.1 lifecycle_1.0.0
[25] stringr_1.4.0 spatstat.core_2.0-0 codetools_0.2-16
[28] knitr_1.31 parallel_4.0.3 class_7.3-17
[31] Rcpp_1.0.5 KernSmooth_2.23-17 tensor_1.5
[34] classInt_0.4-3 jsonlite_1.7.2 abind_1.4-5
[37] deldir_0.2-10 stringi_1.5.3 spatstat.sparse_2.0-0
[40] polyclip_1.10-0 grid_4.0.3 rprojroot_2.0.2
[43] tools_4.0.3 magrittr_2.0.1 goftest_1.2-2
[46] tibble_3.0.6 crayon_1.4.1 pkgconfig_2.0.3
[49] ellipsis_0.3.1 Matrix_1.2-18 httr_1.4.2
[52] iterators_1.0.13 R6_2.5.0 rpart_4.1-15
[55] units_0.6-7 spatstat.geom_2.0-1 igraph_1.2.6
[58] nlme_3.1-148 compiler_4.0.3
The nimbleCarbon package includes the key function for all Bayesian analysis carried out in the manuscript. The package is currently on a github repo and the following command is required for installing the specific version used to generate results and figures:
library(devtools)
install_github('ercrema/nimbleCarbon@4706e8f')
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) and by a Philip Leverhulme Prize (PLP-2019-304) in archaeology awarded to Enrico Crema.
CC-BY 3.0