brms (version 2.14.0)

mcmc_plot.brmsfit: MCMC Plots Implemented in bayesplot

Description

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

Usage

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

mcmc_plot(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.

fixed

Indicates whether parameter names should be matched exactly (TRUE) or treated as regular expressions (FALSE). Default is FALSE.

exact_match

Deprecated alias of argument fixed.

...

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

Run this code
# NOT RUN {
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, pars = "^b_")

# 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")
# }
# NOT RUN {
# }

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