plot_all_trace {BEDASSLE}

Plots all the trace plots for all parameters

Description

This function plots the parameter value estimated in each sampled generation of the MCMC against the index of that sampled generation for each parameter in the model.

Usage

plot_all_trace(MCMC.output, percent.burnin = 0, thinning = 1, population.names = NULL)

Arguments

MCMC.output

The standard MCMC output file generated from a BEDASSLE run.

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 the binomial model is used, population.names will not be used by this function.

Details

A trace plot is a basic visual tool for assessing MCMC mixing. If the chain is mixing well, the trace plot will resemble a "fuzzy caterpillar." If the trace plot has not plateaued, it is an indication that the chain has not converged on the stationary posterior distribution, and must be run longer. If the trace plot of a parameter exhibits high autocorrelation, the user may wish to either increase or decrease the scale of the tuning parameter on that parameter, to decrease or increase acceptance rates, respectively. If the chain appears to be bouncing between areas of "fuzzy caterpillar-dom," it may be an indication of a multi-modal likelihood surface.

Author(s)

Gideon Bradburd