pp_check

pp_check.brmsfit

object

Type of the ppc plot as given by a character string.
Currently, the following plots (as names) are implemented
in bayesplot (v1.1.0):
<code>boxplot</code>, <code>dens</code> <code>dens_overlay</code> (default), 
<code>ecdf_overlay</code>, <code>error_binned</code>, 
<code>error_hist</code>, <code>error_scatter</code>, 
<code>error_scatter_avg</code>, <code>error_scatter_avg_vs_x</code>,
<code>freqpoly</code>, <code>freqpoly_grouped</code>, <code>hist</code>,
<code>intervals</code>, <code>intervals_grouped</code>, 
<code>ribbon</code>, <code>ribbon_grouped</code>, <code>scatter</code>,
<code>scatter_avg</code>, <code>scatter_avg_grouped</code>, 
<code>stat</code>, <code>stat_2d</code>, <code>stat_freqpoly_grouped</code>,
<code>stat_grouped</code>, and <code>violin_grouped</code>.

type

Positive integer indicating how many
posterior samples should be used.
If <code>NULL</code> all samples are used. If not specified,
the number of posterior samples is chosen automatically.
Ignored if <code>subset</code> is not <code>NULL</code>.

nsamples

Optional name of a grouping factor in the model
by which to stratify the ppc plot. This argument is required for
ppc <code>*_grouped</code> types and ignored otherwise.

group

Optional name of a variable in the model. 
Only used for ppc types having an <code>x</code> argument 
and ignored otherwise.

An optional data.frame for which to evaluate predictions.
If <code>NULL</code> (default), the orginal data of the model is used.

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 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

A flag indicating if autocorrelation
parameters should be included in the predictions. 
Defaults to <code>TRUE</code>.

incl_autocor

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

subset

Parameter used in rejection sampling
for truncated discrete models only
(defaults to <code>5</code>). For more details see
<code><a rd-options="brms:predict.brmsfit" href="/link/predict.brmsfit?package=brms&version=1.4.0&to=brms%3Apredict.brmsfit" data-mini-rdoc="brms:predict.brmsfit::predict.brmsfit">predict.brmsfit</a></code>.

ntrys

Further arguments passed to the ppc functions
of the <a rd-options="bayesplot:bayesplot" href="/link/bayesplot?package=brms&version=1.4.0&to=bayesplot%3Abayesplot" data-mini-rdoc="bayesplot:bayesplot::bayesplot">bayesplot</a> package.

Perform posterior predictive checks with the help
of the bayesplot package.

Fit Bayesian generalized (non-)linear 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, binomial, Poisson, survival, response times, ordinal,
zero-inflated, hurdle, and even non-linear models all in a
multilevel context. Further modeling options include auto-correlation
and smoothing terms, user defined dependence 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.
In addition, model fit can easily be assessed and compared with
posterior predictive checks and leave-one-out cross-validation.

PaulChristian Buerkner

brms

Bayesian Regression Models using Stan

pp_check.brmsfit function

Type of the ppc plot as given by a character string.
Currently, the following plots (as names) are implemented
in bayesplot (v1.1.0):
<code>boxplot</code>, <code>dens</code> <code>dens_overlay</code> (default), 
<code>ecdf_overlay</code>, <code>error_binned</code>, 
<code>error_hist</code>, <code>error_scatter</code>, 
<code>error_scatter_avg</code>, <code>error_scatter_avg_vs_x</code>,
<code>freqpoly</code>, <code>freqpoly_grouped</code>, <code>hist</code>,
<code>intervals</code>, <code>intervals_grouped</code>, 
<code>ribbon</code>, <code>ribbon_grouped</code>, <code>scatter</code>,
<code>scatter_avg</code>, <code>scatter_avg_grouped</code>, 
<code>stat</code>, <code>stat_2d</code>, <code>stat_freqpoly_grouped</code>,
<code>stat_grouped</code>, and <code>violin_grouped</code>.

Parameter used in rejection sampling
for truncated discrete models only
(defaults to <code>5</code>). For more details see
<code><a rd-options='brms:predict.brmsfit' href='predict.brmsfit'>predict.brmsfit</a></code>.

Further arguments passed to the ppc functions
of the <a rd-options='bayesplot:bayesplot' href='bayesplot'>bayesplot</a> package.

Perform posterior predictive checks with the help
of the bayesplot package.

pp_check.brmsfit: Posterior Predictive Checks for `brmsfit` Objects

Description

Usage

Arguments

Value

Details

Examples