loadPosterior: loadPosterior

In hferg/bayestraitr: A set of tools to help with the interpretation and analysis of the output of BayesTraits MCMC analyses.

Description

Returns the full mcmc object from a BayesTraits log file. This is used inside plot functions and so on, but might be useful for other MCMC manipulations and so on. Extracts the MCMC samples from a BayesTraits logfile (i.e. discards the header information and coerces samples into a matrix.)

Usage

loadPosterior(logfile, thinning = 1, burnin = 0)

Arguments

logfile	The name of the logfile of the BayesTraits analysis.
thinning	Thinning parameter for the posterior - defaults to 1 (all samples). 2 uses every second sample, 3 every third and so on.
burnin	The number of generations to remove from the start of the chain as burnin. Use if the chain has not reached convergence before sampling began. Useful if the burnin parameter for the analysis itself was not long enough.

Value

A tibble (see tibble) with the class "bt_post" containing the samples from the BayesTraits MCMC chain. Headers vary on model type.

hferg/bayestraitr documentation built on May 28, 2019, 8:55 p.m.