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

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,
  ...
)

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 in Estimate 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