BEDASSLE-package: Disentangling the contributions of geographic and ecological...
In BEDASSLE: Quantifies Effects of Geo/Eco Distance on Genetic Differentiation
BEDASSLE-package R Documentation
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 <gbradburd@ucdavis.edu>
References
Bradburd, G.S., Ralph, P.L., and Coop, G.M. Disentangling the effects of geographic and
ecological isolation on genetic differentiation. Evolution 2013.
BEDASSLE documentation built on May 29, 2024, 9:31 a.m.
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| BEDASSLE-package | R Documentation |
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 <gbradburd@ucdavis.edu>
References
Bradburd, G.S., Ralph, P.L., and Coop, G.M. Disentangling the effects of geographic and ecological isolation on genetic differentiation. Evolution 2013.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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