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.
plot_all_trace(MCMC.output, percent.burnin = 0, thinning = 1, population.names = NULL)
The standard MCMC output file generated from a BEDASSLE run.
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.
The multiple by which the sampled MCMC generations are thinned. A thinning of
5 will sample every 5th MCMC generation.
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.
Gideon Bradburd
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.