as.mcmc.brmsfit

as.mcmc

Extract posterior samples for use with the coda package

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

as.mcmc.brmsfit function

<dl><dt>x</dt>
<dd>An <code>R</code> object typically of class <code>brmsfit</code></dd>
<dt>pars</dt>
<dd>Names of parameters for which posterior samples
should be returned, as given by a character vector or regular expressions.
By default, all posterior samples of all parameters are extracted.</dd>
<dt>fixed</dt>
<dd>Indicates whether parameter names
should be matched exactly (<code>TRUE</code>) or treated as
regular expressions (<code>FALSE</code>). Default is <code>FALSE</code>.</dd>
<dt>combine_chains</dt>
<dd>Indicates whether chains should be combined.</dd>
<dt>inc_warmup</dt>
<dd>Indicates if the warmup samples should be included.
Default is <code>FALSE</code>. Warmup samples are used to tune the
parameters of the sampling algorithm and should not be analyzed.</dd>
<dt>...</dt>
<dd>currently unused</dd></dl>

Arguments

Extract posterior samples for use with the coda package — as.mcmc.brmsfit

<dl>

<dt>x</dt>
<dd>An <code>R</code> object typically of class <code>brmsfit</code></dd>


<dt>pars</dt>
<dd>Names of parameters for which posterior samples
should be returned, as given by a character vector or regular expressions.
By default, all posterior samples of all parameters are extracted.</dd>


<dt>fixed</dt>
<dd>Indicates whether parameter names
should be matched exactly (<code>TRUE</code>) or treated as
regular expressions (<code>FALSE</code>). Default is <code>FALSE</code>.</dd>


<dt>combine_chains</dt>
<dd>Indicates whether chains should be combined.</dd>


<dt>inc_warmup</dt>
<dd>Indicates if the warmup samples should be included.
Default is <code>FALSE</code>. Warmup samples are used to tune the
parameters of the sampling algorithm and should not be analyzed.</dd>


<dt>...</dt>
<dd>currently unused</dd>

</dl>

Extract posterior samples for use with the coda package

as.mcmc.brmsfit: Extract posterior samples for use with the coda package

Description

Usage

Value

Arguments