In andburch/bayesianfingerprintr: Inferring the Age and Sex of Ancient Potters from Fingerprint Ridge Densities

knitr::opts_chunk$set(
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  fig.path = "man/figures/README-",
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bayesianfingerprintr

The goal of bayesianfingerprintr is to help archaeologists infer demographic information about ancient potters who leave fingerprint impressions on ceramic artifacts. It uses a Bayesian mixture modelling approach, coupled with a data-driven understanding of how epidermal ridge densities, sex, and age covary.

Installation

You can install the development version of bayesianfingerprintr from GitHub with:

# install.packages("devtools")
devtools::install_github("andburch/fingerprint-mixture-model")

Example

This is a basic example, where we first make up some fake archaeological mean ridge breadth (MRB) data. Each data point has an identification number, a MRB measurement, and (we're making this up here) maybe some other information like where it was recovered from.

library(bayesianfingerprintr)

fake.data <- 
  data.frame(
  id.number = 1:100,
  mrb = c(rgamma(50,64,160), rgamma(50,2500,5000)),
  location = sample(c("palace", "domestic"), 100, replace = TRUE)
  )

Then we want to make sure the MRB values are scaled to match the modern reference data collected by Kralik and Novotny (2003):

fake.data$mrb <- fake.data$mrb*scaling_factor(fake.data$mrb)

Finally we can put these scaled MRB values into the model;

output <- 
  run_model(fake.data, #the data frame with the MRB values in a column named `mrb`
            "2sex.2age", #which variant of the model we want to run
            "2sex.2age.model.txt", #where to save the model specifications
            150, #number of MCMC iterations per chain (should be over 150,000 normally)
            5, #thinning factor
            F, F, 3, F, 100) #other options

andburch/bayesianfingerprintr documentation built on March 11, 2023, 8:53 p.m.