R: MCMC Plots Implemented in 'bayesplot'

mcmc_plot.brmsfit {brms} | R Documentation

## MCMC Plots Implemented in bayesplot

### Description

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

### Usage

    ## S3 method for class '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.

### Details

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

### Value

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

### Examples

    ## 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, 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")

    ## End(Not run)

[Package brms version 2.17.0 Index]