standata.brmsfit

standata

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

object

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

A named <code>list</code> of objects containing new data, which
cannot be passed via argument <code>newdata</code>. Required for some objects 
used in autocorrelation structures, or <code><a rd-options="" href="/link/stanvars?package=brms&version=2.15.0" data-mini-rdoc="brms::stanvars">stanvars</a></code>.

newdata2

Deprecated alias of <code>newdata2</code>.

new_objects

A flag indicating if correlation structures originally
specified via <code>autocor</code> should be included in the predictions.
Defaults to <code>TRUE</code>.

incl_autocor

More arguments passed to <code><a rd-options="" href="/link/make_standata?package=brms&version=2.15.0" data-mini-rdoc="brms::make_standata">make_standata</a></code>
and <code><a rd-options="" href="/link/validate_newdata?package=brms&version=2.15.0" data-mini-rdoc="brms::validate_newdata">validate_newdata</a></code>.

Extract all data that was used by Stan to fit the model.

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

standata.brmsfit function

A named <code>list</code> of objects containing new data, which
cannot be passed via argument <code>newdata</code>. Required for some objects 
used in autocorrelation structures, or <code><a rd-options='' href='stanvars'>stanvars</a></code>.

More arguments passed to <code><a rd-options='' href='make_standata'>make_standata</a></code>
and <code><a rd-options='' href='validate_newdata'>validate_newdata</a></code>.

standata.brmsfit: Extract data passed to Stan

Description

Usage

Arguments

Value