make_conditions

This is a helper function to prepare fully crossed conditions primarily
for use with the <code>conditions</code> argument of <code>conditional_effects</code>.
Automatically creates labels for each row in the <code>cond__</code> column.

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

Aki Vehtari

Mattan S. Ben-Shachar

Hayden Rabel

Simon C. Mills

Stephen Wild

Ven Popov

make_conditions function

<dl><dt>x</dt>
<dd>An R object from which to extract the variables
that should be part of the conditions.</dd>
<dt>vars</dt>
<dd>Names of the variables that should be part of the conditions.</dd>
<dt>...</dt>
<dd>Arguments passed to <code>rows2labels</code>.</dd></dl>

Arguments

Prepare Fully Crossed Conditions — make_conditions

<dl>

<dt>x</dt>
<dd>An R object from which to extract the variables
that should be part of the conditions.</dd>


<dt>vars</dt>
<dd>Names of the variables that should be part of the conditions.</dd>


<dt>...</dt>
<dd>Arguments passed to <code>rows2labels</code>.</dd>

</dl>

Prepare Fully Crossed Conditions

make_conditions: Prepare Fully Crossed Conditions

Description

Usage

Value

Arguments

Details

See Also

Examples