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.
Gideon Bradburd
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.