MCMC Plots Implemented in bayesplot — mcmc

Convenient way to call MCMC plotting functions implemented in the bayesplot package.

# S3 method for brmsfit
mcmc_plot(
  object,
  pars = NA,
  type = "intervals",
  variable = NULL,
  regex = FALSE,
  fixed = FALSE,
  ...
)

mcmc_plot(object, ...)

Arguments

object: An R object typically of class brmsfit
pars: Deprecated alias of variable. Names of the parameters to plot, as given by a character vector or a regular expression.
type: The type of the plot. Supported types are (as names) hist, dens, hist_by_chain, dens_overlay, violin, intervals, areas, acf, acf_bar,trace, trace_highlight, scatter, rhat, rhat_hist, neff, neff_hist nuts_acceptance, nuts_divergence, nuts_stepsize, nuts_treedepth, and nuts_energy. For an overview on the various plot types see MCMC-overview.
variable: Names of the variables (parameters) to plot, as given by a character vector or a regular expression (if regex = TRUE). By default, a hopefully not too large selection of variables is plotted.
regex: Logical; Indicates whether variable should be treated as regular expressions. Defaults to FALSE.
fixed: (Deprecated) Indicates whether parameter names should be matched exactly (TRUE) or treated as regular expressions (FALSE). Default is FALSE and only works with argument pars.
...: Additional arguments passed to the plotting functions. See MCMC-overview for more details.

Value

A ggplot object that can be further customized using the ggplot2 package.

Details

Also consider using the shinystan package available via method launch_shinystan in brms for flexible and interactive visual analysis.

Examples

if (FALSE) {
model <- brm(count ~ zAge + zBase * Trt + (1|patient),
             data = epilepsy, family = "poisson")

# plot posterior intervals
mcmc_plot(model)

# only show population-level effects in the plots
mcmc_plot(model, variable = "^b_", regex = TRUE)

# show histograms of the posterior distributions
mcmc_plot(model, type = "hist")

# plot some diagnostics of the sampler
mcmc_plot(model, type = "neff")
mcmc_plot(model, type = "rhat")

# plot some diagnostics specific to the NUTS sampler
mcmc_plot(model, type = "nuts_acceptance")
mcmc_plot(model, type = "nuts_divergence")
}