This repository contains the data and code for the paper:
Hinz, (2020). Sensitivity of Radiocarbon Sum Calibration. Zenodo https://doi.org/10.5281/zenodo.3613674
The pre-print is online here:
Hinz, (2020). Sensitivity of Radiocarbon Sum Calibration. SocArXiv, Accessed 20 Jan 2020. Online at https://osf.io/preprints/socarxiv/bgvk6
Sum calibration has become a standard tool for demographic studies, even though the methodology itself is far from uncontroversial. In addition to fundamental methodological criticism, questions are frequently raised about the sample size and data density required to detect large-scale changes in past populations. This article uses a simulation approach to determine the detection probabilities for events of varying intensity and with varying data density. At the same time, the effectiveness of Monte Carlo-based confidence envelopes as a countermeasure against false-positive results is tested. The results show that the detection of such events is not unlikely and that the Monte Carlo method is well suited to separate signal and noise. However, the nature of the events already observed in this way demands further assessment.
- Simulations used to evaluate the possibility of reconstructing prehistoric demography from 14C data
- Random sampling of 14C data using given probability distributions
- Test the sensitivity of a summed 14C proxy curve to population fluctuations
- Demographic signals can be separated from noise in summed 14C distributions using appropriate techniques
Prehistoric demography; Summed radiocarbon date distributions; Simulation; Calibration; Population proxies
Please cite this compendium as:
Hinz, (2020). Compendium of R code and data for Sensitivity of Radiocarbon Sum Calibration. Accessed 20 Jan 2020. Online at https://doi.org/10.5281/zenodo.3613674
You can download the compendium as a zip from from this URL: https://github.com/MartinHinz/sensitivity.sumcal.article.2020/archive/master.zip
Or you can install this compendium as an R package, sensitivity.sumcal.article.2020, from GitHub with:
# install.packages("devtools")
remotes::install_github("MartinHinz/sensitivity.sumcal.article.2020")This repository contains text, code and data for the paper. The
analysis directory contains paper and data to reproduce the
preparations, calculations and figure renderings. The paper directory
contains the text for the paper in .Rmd format, and rendered versions
as pdf, html and docx. It also contains a directory result_data which
holds the results from the submitted version of the paper.
As the data and code in this repository are complete and self-contained,
it can be reproduced with any R environment (> version 3.5.0). The
necessary package dependencies are documented in the DESCRIPTION file
and can be installed manually or automatically with
devtools::install().
The simulation can then be run using the run_simulation() command. The
total run time was 94480 seconds or 26 hours and 15 minutes (using
parallel computing on 6 cores of an Intel(R) Xeon(R) CPU E3-1240 v5 at
3.50GHz with 16 GB RAM).
Text and figures : CC-BY-4.0
Code : See the DESCRIPTION file
Data : CC-0 attribution requested in reuse
We welcome contributions from everyone. Before you get started, please see our contributor guidelines. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.