DiagMCMC: Diagnose MCMC

In bfw: Bayesian Framework for Computational Modeling

Diagnose MCMC

Description

MCMC convergence diagnostics

Usage

DiagMCMC(
  data.MCMC,
  par.name,
  job.names,
  job.group,
  credible.region = 0.95,
  monochrome = TRUE,
  plot.colors = c("#495054", "#e3e8ea")
)

Arguments

data.MCMC	MCMC chains to diagnose
par.name	parameter to analyze
job.names	names of all parameters in analysis, Default: NULL
job.group	for some hierarchical models with several layers of parameter names (e.g., latent and observed parameters), Default: NULL
credible.region	summarize uncertainty by defining a region of most credible values (e.g., 95 percent of the distribution), Default: 0.95
monochrome	logical, indicating whether or not to use monochrome colors, else use DistinctColors, Default: TRUE
plot.colors	range of color to use, Default: c("#495054", "#e3e8ea")

Value

list of diagnostic plots

See Also

dev.new,colorRampPalette,recordPlot,graphics.off,dev.list,dev.off par,layout,plot.new,matplot,abline,text,points,mtext traceplot,gelman.plot,effectiveSize sd,acf,density

bfw documentation built on March 18, 2022, 6:19 p.m.