BEDASSLE-package {BEDASSLE}

Disentangling the contributions of geographic and ecological isolation to genetic differentiation

Description

This method models the covariance in allele frequencies between populations on a landscape as a decreasing function of their pairwise geographic and ecological distance. Allele frequencies are modeled as a spatial Gaussian process with a parametric covariance function. The parameters of this covariance function, as well as the spatially smoothed allele frequencies, are estimated in a custom Markov chain Monte Carlo.

The two inference functions are MCMC and MCMC_BB, which call the Markov chain Monte Carlo algorithms on the standard and overdispersion (Beta-Binomial) models, respectively. To evaluate MCMC performance, there are a number of MCMC diagnosis and visualization functions, which variously show the trace, plots, marginal and joint marginal densities, and parameter acceptance rates. To evaluate model adequacy, there is a posterior predictive sample function (posterior.predictive.sample), and an accompanying function to plot its output and visually assess the model's ability to describe the user's data.

Author(s)

Gideon Bradburd

Maintainer: Gideon Bradburd <[e-mail]>

References

Bradburd, G.S., Ralph, P.L., and Coop, G.M. Disentangling the effects of geographic and ecological isolation on genetic differentiation. Evolution 2013.