plot_marginal: Plots the marginal density of a parameter
In BEDASSLE: Quantifies Effects of Geo/Eco Distance on Genetic Differentiation

plot_marginal

R Documentation

Plots the marginal density of a parameter

Description

Plots the posterior marginal density of a parameter. Users may specify whether they want a histogram, a density, or both.

Usage

plot_marginal(parameter, percent.burnin = 0, thinning = 1, histogram = TRUE, 
density = TRUE, population.names = NULL, param.name = deparse(substitute(parameter)))

Arguments

`parameter`	The parameter for which the marginal plot is being generated.
`percent.burnin`	The percent of the sampled MCMC generations to be discarded as "burn-in." If the MCMC is run for 1,000,000 generations, and sampled every 1,000 generations, there will be 1,000 sampled generations. A `percent.burnin` of `20` will discard the first 200 sampled parameter values from that sample.
`thinning`	The multiple by which the sampled MCMC generations are thinned. A `thinning` of `5` will sample every 5th MCMC generation.
`histogram`	A switch that controls whether or not the plot contains a histogram of the values estimated for the parameter over the course of the MCMC. Default is `TRUE`.
`density`	A switch that controls whether or not the plot shows the density of the values estimated for the parameter over the course of the MCMC. Default is `TRUE`.
`population.names`	A vector of length `k`, where `k` is the number of populations/individuals (i.e. `k = nrow(counts)`), giving the name or identifier of each population/individual included in the analysis. These will be used to title the `k` marginal plots of the phi parameters estimated for each population/individual in the beta-binomial model. If the binomial model is used, `population.names` will not be used by this function.
`param.name`	The name of the parameter for which the trace plot is being displayed.

Details

The marginal plot is another basic visual tool for MCMC diagnosis. Users should look for marginal plots that are "smooth as eggs" (indicating that the chain has been run long enough) and unimodal (indicating a single peak in the likelihood surface).