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brms

pp_check.brmsfit

Posterior Predictive Checks for brmsfit Objects

Description

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

Usage

## S3 method for class 'brmsfit'
pp_check(
  object,
  type,
  nsamples,
  group = NULL,
  x = NULL,
  newdata = NULL,
  resp = NULL,
  subset = NULL,
  ...
)

Arguments

object

An object of class brmsfit.
type

Type of the ppc plot as given by a character string. See PPC for an overview of currently supported types. You may also use an invalid type (e.g. type = "xyz") to get a list of supported types in the resulting error message.
nsamples

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

Optional name of a factor variable in the model by which to stratify the ppc plot. This argument is required for ppc *_grouped types and ignored otherwise.
x

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

An optional data.frame for which to evaluate predictions. If NULL (default), the original data of the model is used. NA 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.
resp

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

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

Further arguments passed to predict.brmsfit as well as to the PPC function specified in type.

Details

For a detailed explanation of each of the ppc functions, see the PPC documentation of the bayesplot package.

Value

A ggplot object that can be further customized using the ggplot2 package.

Examples

## Not run: 
fit <-  brm(count ~ zAge + zBase * Trt
            + (1|patient) + (1|obs),
            data = epilepsy, family = poisson())

pp_check(fit)  # shows dens_overlay plot by default
pp_check(fit, type = "error_hist", nsamples = 11)
pp_check(fit, type = "scatter_avg", nsamples = 100)
pp_check(fit, type = "stat_2d")
pp_check(fit, type = "rootogram")
pp_check(fit, type = "loo_pit")

## get an overview of all valid types
pp_check(fit, type = "xyz")

## End(Not run)
brms

Bayesian Regression Models using 'Stan'

v2.15.0
GPL-2
Authors
Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb]
Initial release
2021-03-10

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