ThetaMater.M3: ThetaMater.M3: (Bayesian Model 2) MCMC function that uses the...

In radamsRHA/ThetaMater: ThetaMater: Bayesian estimation of the population size parameter theta from genomic data

ThetaMater.M3: (Bayesian Model 2) MCMC function that uses the MCMCmetrop1R function from MCMCpack with a discrete gamma rate variation model

Description

This function returns a list of each step in the MCMC sampling chain

Usage

ThetaMater.M3(k.vec, l.vec, n.vec, c.vec, K, ngens, burnin, thin, theta.shape,
  theta.scale, alpha.shape, alpha.scale)

Arguments

k.vec	Vector of mutation counts
l.vec	Vector of locus lengths
n.vec	Vector of sample numbers
c.vec	Vector of data pattern counts
ngens	Number of generations to run the MCMC
burnin	Number of generations to discard from MCMC chain
thin	Number of generations to thin in the MCMC chain
theta.shape	Shape parameter of the gamma distribution for setting the prior on theta
theta.scale	Scale parameter of the gamma distribution for setting the prior on theta
alpha.shape	Shape parameter of the gamma distribution for setting the prior on alpha
alpha.shape	Scale parameter of the gamma distribution for setting the prior on alpha

Examples

library(Rcpp)
library(ThetaMater)
library(MCMCpack)
sim.results <- Coal.Theta.Sim.File.G(theta = 0.001, n.vec = c(10,10), l.vec = c(2000,2000), num.loci = 1000, out.file = '~/Desktop/example.alleles', alpha.param = 0.5)
example.data <- Read.AllelesFile.NoThreshold(alleles.file = '~/Desktop/example.alleles', log.file = '~/Desktop/log.txt')
Theta.Posterior.G <- MCMC.Theta.M3(k.vec = example.data$k.vec, l.vec = example.data$l.vec, n.vec = example.data$n.vec, c.vec = example.data$c.vec, 
ngens = 10000, thin = 10, burnin = 10000, K = 4, alpha.param = 0.5)

radamsRHA/ThetaMater documentation built on April 25, 2024, 10:11 a.m.