posterior_linpred.brmsfit

posterior_linpred

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

object

(Deprecated) Logical; if <code>FALSE</code>
(the default), samples of the linear predictor are returned.
If <code>TRUE</code>, samples of transformed linear predictor,
that is, the mean of the posterior predictive distribution
are returned instead (see <code><a rd-options="" href="/link/posterior_epred?package=brms&version=2.15.0" data-mini-rdoc="brms::posterior_epred">posterior_epred</a></code> for details).
Only implemented for compatibility with the 
<code><a rd-options="rstantools:posterior_linpred" href="/link/posterior_linpred?package=brms&version=2.15.0&to=rstantools%3Aposterior_linpred" data-mini-rdoc="rstantools:posterior_linpred::posterior_linpred">posterior_linpred</a></code>
generic.

transform

An optional data.frame for which to evaluate predictions. If
<code>NULL</code> (default), the original data of the model is used.
<code>NA</code> values within factors are interpreted as if all dummy
variables of this factor are zero. This allows, for instance, to make
predictions of the grand mean when using sum coding.

newdata

formula containing group-level effects to be considered in
the prediction. If <code>NULL</code> (default), include all group-level effects;
if <code>NA</code>, include no group-level effects.

re_formula

re.form

Optional names of response variables. If specified, predictions
are performed only for the specified response variables.

resp

Optional name of a predicted distributional parameter.
If specified, expected predictions of this parameters are returned.

dpar

Optional name of a predicted non-linear parameter.
If specified, expected predictions of this parameters are returned.

nlpar

Positive integer indicating how many posterior samples should
be used. If <code>NULL</code> (the default) all samples are used. Ignored if
<code>subset</code> is not <code>NULL</code>.

nsamples

A numeric vector specifying the posterior samples to be used.
If <code>NULL</code> (the default), all samples are used.

subset

Logical. Only relevant for time series models. 
Indicating whether to return predicted values in the original 
order (<code>FALSE</code>; default) or in the order of the 
time series (<code>TRUE</code>).

sort

Further arguments passed to <code><a rd-options="" href="/link/prepare_predictions?package=brms&version=2.15.0" data-mini-rdoc="brms::prepare_predictions">prepare_predictions</a></code>
that control several aspects of data validation and prediction.

Compute posterior samples of the linear predictor, that is samples before
applying any link functions or other transformations. Can be performed for
the data used to fit the model (posterior predictive checks) or for new data.

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 non-linear and
smooth terms, auto-correlation structures, censored data, 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 beliefs.
Model fit can easily be assessed and compared with posterior predictive
checks and leave-one-out cross-validation. References: B<c3><bc>rkner (2017)
<doi:10.18637/jss.v080.i01>; B<c3><bc>rkner (2018) <doi:10.32614/RJ-2018-017>;
Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

PaulChristian Buerkner

brms

Bayesian Regression Models using 'Stan'

Paul-Christian B<c3><bc>rkner

Jonah Gabry

Sebastian Weber

Andrew Johnson

Martin Modrak

posterior_linpred.brmsfit function

(Deprecated) Logical; if <code>FALSE</code>
(the default), samples of the linear predictor are returned.
If <code>TRUE</code>, samples of transformed linear predictor,
that is, the mean of the posterior predictive distribution
are returned instead (see <code><a rd-options='' href='posterior_epred'>posterior_epred</a></code> for details).
Only implemented for compatibility with the 
<code><a rd-options='rstantools:posterior_linpred' href='posterior_linpred'>posterior_linpred</a></code>
generic.

Further arguments passed to <code><a rd-options='' href='prepare_predictions'>prepare_predictions</a></code>
that control several aspects of data validation and prediction.

posterior_linpred.brmsfit: Posterior Samples of the Linear Predictor

Description

Usage

Arguments

See Also

Examples