plot_all_phi_trace: Plots all the trace plots for the phi parameters for all...
In BEDASSLE: Quantifies Effects of Geo/Eco Distance on Genetic Differentiation

plot_all_phi_trace

R Documentation

Plots all the trace plots for the phi parameters for all populations

Description

Plots all trace plots for the phi parameters in all populations. 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

plot_all_phi_trace(phi_mat, percent.burnin = 0, thinning = 1, population.names = NULL)

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.

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.