plot_all_phi_marginals: Plot all the marginals for the phi parameters for all...

Description Usage Arguments Details Author(s)

Description

Plots the posterior marginal densities of all phi parameters. Users may specify whether they want a histogram, a density, or both. For convenience, the F_{k} statistic is presented in place of the phi parameter, as this is the statistic users care about. F_{k} is defined as \frac{1}{1+phi_{k}}.

Usage

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plot_all_phi_marginals(phi_mat, percent.burnin = 0, thinning = 1, 
population.names = NULL, pop.index= NULL, histogram = TRUE, density = TRUE)

Arguments

phi_mat

The k by ngen matrix of phi values estimated for all k populations/individuals included in the analysis in each of ngen MCMC generations.

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.

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 trace plots of the phi parameters estimated for each population/individual in the beta- binomial model. If population.names is not provided (i.e. population.names = NULL), a population index number will be used to title the plot.

pop.index

A population index number generated to title a marginal plot if no population.names is specified.

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.

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).

Author(s)

Gideon Bradburd


BEDASSLE documentation built on May 2, 2019, 6:10 a.m.