summary.brmsfit

An object of class <code>brmsfit</code>.

object

Logical; Indicating if priors should be included 
in the summary. Default is <code>FALSE</code>.

priors

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

prob

If <code>FALSE</code> (the default) the mean is used as 
the measure of central tendency and the standard deviation as 
the measure of variability. If <code>TRUE</code>, the median and the 
median absolute deviation (MAD) are applied instead.

robust

Logical; Indicating if the uncertainty in <code>Estimate</code>
caused by the MCMC sampling should be shown in the summary. Defaults to
<code>FALSE</code>.

mc_se

Create a summary of a fitted model represented by a <code>brmsfit</code> object

Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others --
linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include non-linear and
smooth terms, auto-correlation structures, censored data, meta-analytic
standard errors, and quite a few more. In addition, all parameters of the
response distribution can be predicted in order to perform distributional
regression. Prior specifications are flexible and explicitly encourage
users to apply prior distributions that actually reflect their beliefs.
Model fit can easily be assessed and compared with posterior predictive
checks and leave-one-out cross-validation. References: B<c3><bc>rkner (2017)
<doi:10.18637/jss.v080.i01>; B<c3><bc>rkner (2018) <doi:10.32614/RJ-2018-017>;
Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

PaulChristian Buerkner

summary.brmsfit: Create a summary of a fitted model represented by a `brmsfit` object

Description

Usage

Arguments

Details

References