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brms

summary.brmsfit

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

Description

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

Usage

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

Arguments

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

Other potential arguments

Details

The convergence diagnostics Rhat, Bulk_ESS, and Tail_ESS are described in detail in Vehtari et al. (2020).

References

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

brms

Bayesian Regression Models using 'Stan'

v2.15.0
GPL-2
Authors
Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb]
Initial release
2021-03-10

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