summary.brmsfit

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 both theory-driven and
data-driven non-linear terms, auto-correlation structures, censoring and
truncation, 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 prior knowledge. Models can easily be evaluated and
compared using several methods assessing posterior or prior predictions.
References: Bürkner (2017) <doi:10.18637/jss.v080.i01>;
Bürkner (2018) <doi:10.32614/RJ-2018-017>;
Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017)
<doi:10.18637/jss.v076.i01>.

PaulChristian Buerkner

brms

Bayesian Regression Models using 'Stan'

Paul-Christian Bürkner

Jonah Gabry

Sebastian Weber

Andrew Johnson

Martin Modrak

Hamada S. Badr

Frank Weber

Mattan S. Ben-Shachar

Hayden Rabel

Simon C. Mills

Stephen Wild

summary.brmsfit function

<dl><dt>object</dt>
<dd>An object of class <code>brmsfit</code>.</dd>
<dt>priors</dt>
<dd>Logical; Indicating if priors should be included
in the summary. Default is <code>FALSE</code>.</dd>
<dt>prob</dt>
<dd>A value between 0 and 1 indicating the desired probability
to be covered by the uncertainty intervals. The default is 0.95.</dd>
<dt>robust</dt>
<dd>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.</dd>
<dt>mc_se</dt>
<dd>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>.</dd>
<dt>...</dt>
<dd>Other potential arguments</dd></dl>

Arguments

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

<dl>

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


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


<dt>prob</dt>
<dd>A value between 0 and 1 indicating the desired probability
to be covered by the uncertainty intervals. The default is 0.95.</dd>


<dt>robust</dt>
<dd>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.</dd>


<dt>mc_se</dt>
<dd>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>.</dd>


<dt>...</dt>
<dd>Other potential arguments</dd>

</dl>

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

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

Description

Usage

Arguments

Details

References