validate_newdata

A <code>data.frame</code> containing new data to be validated.

newdata

object

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 flag indicating if new levels of group-level
effects are allowed (defaults to <code>FALSE</code>). Only relevant if
<code>newdata</code> is provided.

allow_new_levels

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

resp

Logical; Indicates if response variables should
be checked as well. Defaults to <code>TRUE</code>.

check_response

A flag indicating if ARMA autocorrelation parameters
should be included in the predictions. Defaults to <code>TRUE</code>. Setting it
to <code>FALSE</code> will not affect other correlation structures such as
<code><a rd-options="brms:cor_bsts" href="/link/cor_bsts?package=brms&version=2.6.0&to=brms%3Acor_bsts" data-mini-rdoc="brms:cor_bsts::cor_bsts">cor_bsts</a></code>, or
<code><a rd-options="brms:cor_fixed" href="/link/cor_fixed?package=brms&version=2.6.0&to=brms%3Acor_fixed" data-mini-rdoc="brms:cor_fixed::cor_fixed">cor_fixed</a></code>.

incl_autocor

Optional names of grouping variables to be validated.
Defaults to all grouping variables in the model.

all_group_vars

Validate new data passed to post-processing methods of brms. Unless you
are a package developer, you will rarely need to call <code>validate_newdata</code>
directly.

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>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

PaulChristian Buerkner

brms

Bayesian Regression Models using 'Stan'

Paul-Christian B<c3><bc>rkner

validate_newdata function

A flag indicating if ARMA autocorrelation parameters
should be included in the predictions. Defaults to <code>TRUE</code>. Setting it
to <code>FALSE</code> will not affect other correlation structures such as
<code><a rd-options='brms:cor_bsts' href='cor_bsts'>cor_bsts</a></code>, or
<code><a rd-options='brms:cor_fixed' href='cor_fixed'>cor_fixed</a></code>.

validate_newdata: Validate New Data

Description

Usage

Arguments

Value