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, ...)
```

- 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.

A `ggplot`

object
that can be further customized using the ggplot2 package.

Also consider using the shinystan package available via
method `launch_shinystan`

in brms for flexible
and interactive visual analysis.

```
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")
}
```