mvbrmsformula

mvbf

Set up a multivariate model formula for use in the brms package
allowing to define (potentially non-linear) additive multilevel
models for all parameters of the assumed response distributions.

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

mvbrmsformula function

<dl><dt>...</dt>
<dd>Objects of class <code>formula</code> or <code>brmsformula</code>,
each specifying a univariate model. See <code>brmsformula</code>
for details on how to specify univariate models.</dd>
<dt>flist</dt>
<dd>Optional list of formulas, which are treated in the
same way as formulas passed via the <code>...</code> argument.</dd>
<dt>rescor</dt>
<dd>Logical; Indicates if residual correlation between
the response variables should be modeled. Currently, this is only
possible in multivariate <code>gaussian</code> and <code>student</code> models.
If <code>NULL</code> (the default), <code>rescor</code> is internally set to
<code>TRUE</code> when possible.</dd></dl>

Arguments

Set up a multivariate model formula for use in brms — mvbrmsformula

<dl>

<dt>...</dt>
<dd>Objects of class <code>formula</code> or <code>brmsformula</code>,
each specifying a univariate model. See <code>brmsformula</code>
for details on how to specify univariate models.</dd>


<dt>flist</dt>
<dd>Optional list of formulas, which are treated in the
same way as formulas passed via the <code>...</code> argument.</dd>


<dt>rescor</dt>
<dd>Logical; Indicates if residual correlation between
the response variables should be modeled. Currently, this is only
possible in multivariate <code>gaussian</code> and <code>student</code> models.
If <code>NULL</code> (the default), <code>rescor</code> is internally set to
<code>TRUE</code> when possible.</dd>

</dl>

Set up a multivariate model formula for use in brms

mvbrmsformula: Set up a multivariate model formula for use in brms

Description

Usage

Value

Arguments

Details

See Also

Examples