run.mcmc: Perform a Bayesian Analysis of a Sample of Genes in a...

In infeR: A pedagogical tool to teach the basics of inference in population genetics

Description

Perform a Bayesian analysis of a sample of genes in a subdivided population.

Usage

  run.mcmc(sample,chain.length,burnin,range.M,delta.M,delta.pi,alpha,graphics,true.M,true.pi)

Arguments

sample	an object generated by the sim.inference.model or by the sim.coalescent command
chain.length	total length of the chain
burnin	number of initial steps to discard from the chain
range.M	the support for M
delta.M	width of the uniform proposal for the parameter M
delta.pi	width of the uniform proposals for the parameters pi
alpha	the alpha-level, which takes the default value alpha = 0.05
graphics	a logical variable, which is TRUE if the user wants graphics to be plotted
true.M	true (simulated) value of M
true.pi	true (simulated) value of pi

Details

Once the sim.inference.model or by the sim.coalescent command lines have been executed, run.mcmc can be used to compute a Bayesian analysis of the sample of genes.

Value

A MCMC chain.

Examples

## This is to simulate a sample of genes (at a single locus), using the inference model, with
## 50 genes collected in each of 10 sampled demes. In this example, the product of
## twice the effective population size and migration rate is 2, 
## and the frequency of allele A in the migrant pool is 0.5

sample <- sim.inference.model(number.of.sampled.demes = 10,sample.sizes = 50,M = 2,pi = 0.5)

## This is to run a MCMC for that sample

run.mcmc(sample)

infeR documentation built on May 2, 2019, 4:45 p.m.