brms (version 2.6.0)

stanplot.brmsfit: MCMC Plots Implemented in bayesplot

Description

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

Usage

# S3 method for brmsfit
stanplot(object, pars = NA, type = "intervals",
  exact_match = FALSE, ...)
stanplot(object, ...)

Arguments

object

An R object typically of class brmsfit

pars

Names of parameters to be plotted, as given by a character vector or regular expressions. By default, all parameters except for group-level and smooth effects are plotted. May be ignored for some plots.

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.

exact_match

Indicates whether parameter names should be matched exactly or treated as regular expression. Default is FALSE.

...

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

# NOT RUN {
model <- brm(count ~ log_Age_c + log_Base4_c * Trt 
             + (1|patient) + (1|visit),
             data = epilepsy, family = "poisson")
             
# plot posterior intervals
stanplot(model)

# only show population-level effects in the plots
stanplot(model, pars = "^b_")

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

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

# plot some diagnostics specific to the NUTS sampler
stanplot(model, type = "nuts_acceptance")
stanplot(model, type = "nuts_divergence")
# }
# NOT RUN {
# }