loo_subsample.brmsfit

loo_subsample

Efficient approximate leave-one-out cross-validation (LOO) using subsampling

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

loo_subsample.brmsfit function

<dl><dt>x</dt>
<dd>A <code>brmsfit</code> object.</dd>
<dt>...</dt>
<dd>More <code>brmsfit</code> objects or further arguments
passed to the underlying post-processing functions.
In particular, see <code>prepare_predictions</code> for further
supported arguments.</dd>
<dt>compare</dt>
<dd>A flag indicating if the information criteria
of the models should be compared to each other
via <code>loo_compare</code>.</dd>
<dt>resp</dt>
<dd>Optional names of response variables. If specified, predictions
are performed only for the specified response variables.</dd>
<dt>model_names</dt>
<dd>If <code>NULL</code> (the default) will use model names
derived from deparsing the call. Otherwise will use the passed
values as model names.</dd></dl>

Arguments

Efficient approximate leave-one-out cross-validation (LOO) using subsampling — loo_subsample.brmsfit

<dl>

<dt>x</dt>
<dd>A <code>brmsfit</code> object.</dd>


<dt>...</dt>
<dd>More <code>brmsfit</code> objects or further arguments
passed to the underlying post-processing functions.
In particular, see <code>prepare_predictions</code> for further
supported arguments.</dd>


<dt>compare</dt>
<dd>A flag indicating if the information criteria
of the models should be compared to each other
via <code>loo_compare</code>.</dd>


<dt>resp</dt>
<dd>Optional names of response variables. If specified, predictions
are performed only for the specified response variables.</dd>


<dt>model_names</dt>
<dd>If <code>NULL</code> (the default) will use model names
derived from deparsing the call. Otherwise will use the passed
values as model names.</dd>

</dl>

Efficient approximate leave-one-out cross-validation (LOO) using subsampling

loo_subsample.brmsfit: Efficient approximate leave-one-out cross-validation (LOO) using subsampling

Description

Usage

Arguments

Details

Examples