MCMC Plots Implemented in bayesplot
Convenient way to call MCMC plotting functions implemented in the bayesplot package.
## S3 method for class 'brmsfit' mcmc_plot( object, pars = NA, type = "intervals", fixed = FALSE, exact_match = FALSE, ... ) mcmc_plot(object, ...)
object |
An R object typically of class |
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) |
fixed |
Indicates whether parameter names
should be matched exactly ( |
exact_match |
Deprecated alias of argument |
... |
Additional arguments passed to the plotting functions.
See |
Also consider using the shinystan package available via
method launch_shinystan in brms for flexible
and interactive visual analysis.
A ggplot object
that can be further customized using the ggplot2 package.
## 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")
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.