`brmsfit`

object`R/summary.R`

`summary.brmsfit.Rd`

Create a summary of a fitted model represented by a `brmsfit`

object

```
# S3 method for brmsfit
summary(
object,
priors = FALSE,
prob = 0.95,
robust = FALSE,
mc_se = FALSE,
...
)
```

- object
An object of class

`brmsfit`

.- priors
Logical; Indicating if priors should be included in the summary. Default is

`FALSE`

.- prob
A value between 0 and 1 indicating the desired probability to be covered by the uncertainty intervals. The default is 0.95.

- robust
If

`FALSE`

(the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If`TRUE`

, the median and the median absolute deviation (MAD) are applied instead.- mc_se
Logical; Indicating if the uncertainty in

`Estimate`

caused by the MCMC sampling should be shown in the summary. Defaults to`FALSE`

.- ...
Other potential arguments

The convergence diagnostics `Rhat`

, `Bulk_ESS`

, and
`Tail_ESS`

are described in detail in Vehtari et al. (2020).

Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner (2020). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. *Bayesian Analysis*. 1–28. dpi:10.1214/20-BA1221