This repo contains the data and R code used for the study presented in the following paper:
THE METHOD FOR ESTIMATING REC MODEL PARAMETERS HAS BEEN SUPERSEDED Please see the the pre-release R package chronup for the new approach and the related Open Science Foundation project and pre-print for up-to-date information.
Chronological uncertainty complicates attempts to use radiocarbon dates as proxies for processes like human population growth/decline, forest fires, and marine ingression. Established approaches involve turning databases of radiocarbon-date densities into single summary proxies that cannot fully account for chronological uncertainty. Here, I use simulated data to explore an alternate Bayesian approach that instead models the data as what they are, namely radiocarbon-dated event-counts. The approach involves assessing possible event-count sequences by sampling radiocarbon date densities and then applying MCMC to estimate the parameters of an appropriate count-based regression model. The regressions based on individual sampled sequences were placed in a multilevel framework, which allowed for the estimation of hyperparameters that account for chronological uncertainty in individual event times. Two processes were used to produce simulated data. One represented a simple monotonic change in event-counts and the other was based on a real palaeoclimate proxy record. In both cases, the method produced estimates that had the correct sign and were consistently biased toward zero. These results indicate that the approach is widely applicable and could form the basis of a new class of quantitative models for use in exploring long-term human-environment interaction.
The R scripts contained in this repository are intended for replication efforts and to improve the transparency of my research. They are, of course, provided without warranty or technical support. That said, questions about the code can be directed to me, Chris Carleton, at [email protected].
This analysis described in the associated manuscript was performed in R. Thus, you may need to download the latest version of R in order to make use of the scripts described below.
This project made use of a Bayesian Analysis package called Nimble. See the Nimble website for documentation and a tutorial. Then, refer to the R scripts in this repo.
- Generate simulated data
- See
./R_scripts/Analysis/simdates_exp.Rand./R_scripts/Analysis/simdates_palaeo.R - For the latter, you will need to have the palaeoclimate dataset described in Kennett et al. Development and disintegration of maya political systems in response to climate change, Science 338 (2012) 788–791. doi:10.1126/science.1226299. I have included it in a .csv file in this repo at
/Data/Climate/ - These data generating scripts make use of the R package 'clam' to calibrate the dates.
- See
- Estimate model parameters with scripts that correspond to the data. There are several scripts with analytical functions in them. The ones used to estimate full HiREC models are labelled "negbin_hier..." while the core Negative Binomial regression can be run with just the "negbin.R" script. In the HiREC scripts there are lines that assign data to variables used in the Nimble code. Comment out the lines that don't apply before running the script---i.e., some lines are used to draw a single element from the radiocarbon-dated event count ensemble (see main paper) and others assign the chronological-error-free data to variables.
When using these scripts, pay close attention, of course, to path names, which may differ on your system.
For a more detailed replication demonstration, see the Wiki for this repo.