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 |

`pars` |
Deprecated alias of |

`type` |
The type of the plot.
Supported types are (as names) |

`variable` |
Names of the variables (parameters) to plot, as given by a
character vector or a regular expression (if |

`regex` |
Logical; Indicates whether |

`fixed` |
(Deprecated) Indicates whether parameter names
should be matched exactly ( |

`...` |
Additional arguments passed to the plotting functions.
See |

### 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)
```

*brms*version 2.21.0 Index]