posterior.predictive.sample: Generates posterior predictive samples

View source: R/posterior.predictive.sample.R

posterior.predictive.sampleR Documentation

Generates posterior predictive samples


This function simulates data using the inference model parameterized from the joint posterior of the MCMC and the observed independent variables (D_{ij} and E_{ij}). These posterior predictive samples can be compared to the observed data to see how well the model is able to describe the observed data.


posterior.predictive.sample(MCMC.output, posterior.predictive.sample.size, output.file, 
prefix = "")



The standard MCMC output file generated from a BEDASSLE run.


The number of posterior predictive datasets the user wishes to simulate.


The name that will be assigned to the R object containing the posterior predictive datasets. The suffix ".Robj" will be appended to the user-specified name.


If specified, this prefix will be added to the output file name.


This function simulates datasets like those the user analyzed with BEDASSLE, using the same independent variables (sample.sizes, D_{ij} and E_{ij}) as in the user's dataset and plugging them into the inference model, which is parameterized by randomly drawing parameter values from the joint posterior output of the MCMC analysis. These posterior predictive simulated allelic count data are summarized as unbiased pairwise F_{ST} (using calculate.all.pairwise.Fst), which may then be compared to the observed unbiased pairwise F_{ST} to determine how well the model is able to describe the user's data. The output of posterior.predictive.sample can be visualized using plot.posterior.predictive.sample.


Gideon Bradburd

BEDASSLE documentation built on April 11, 2022, 1:07 a.m.