brms (version 2.16.1)

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 brmsfit
pp_check(
  object,
  type,
  ndraws = NULL,
  nsamples = NULL,
  group = NULL,
  x = NULL,
  newdata = NULL,
  resp = NULL,
  draw_ids = 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.

ndraws

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

nsamples

Deprecated alias of ndraws.

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.

draw_ids

An integer vector specifying the posterior draws to be used. If NULL (the default), all draws are used.

subset

Deprecated alias of draw_ids.

...

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

Value

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

Details

For a detailed explanation of each of the ppc functions, see the PPC documentation of the bayesplot 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", ndraws = 11)
pp_check(fit, type = "scatter_avg", ndraws = 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")
# }
# NOT RUN {
# }