brms (version 2.1.0)

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, nsamples, group = NULL, x = NULL,
  resp = NULL, newdata = NULL, re_formula = NULL,
  allow_new_levels = FALSE, sample_new_levels = "uncertainty",
  new_objects = list(), incl_autocor = TRUE, subset = NULL, nug = NULL,
  ntrys = 5, loo_args = list(), ...)

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.

resp

Optional names of response variables. If specified, fitted values of these response variables are returned.

newdata

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

re_formula

formula containing group-level effects to be considered in the prediction. If NULL (default), include all group-level effects; if NA, include no group-level effects.

allow_new_levels

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

sample_new_levels

Indicates how to sample new levels for grouping factors specified in re_formula. This argument is only relevant if newdata is provided and allow_new_levels is set to TRUE. If "uncertainty" (default), include group-level uncertainty in the predictions based on the variation of the existing levels. If "gaussian", sample new levels from the (multivariate) normal distribution implied by the group-level standard deviations and correlations. This options may be useful for conducting Bayesian power analysis. If "old_levels", directly sample new levels from the existing levels.

new_objects

A named list of objects containing new data, which cannot be passed via argument newdata. Currently, only required for objects passed to cor_sar and cor_fixed.

incl_autocor

A flag indicating if ARMA autocorrelation parameters should be included in the predictions. Defaults to TRUE. Setting it to FALSE will not affect other correlation structures such as cor_bsts, or cor_fixed.

subset

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

nug

Small positive number for Gaussian process terms only. For numerical reasons, the covariance matrix of a Gaussian process might not be positive definite. Adding a very small number to the matrix's diagonal often solves this problem. If NULL (the default), nug is chosen internally.

ntrys

Parameter used in rejection sampling for truncated discrete models only (defaults to 5). For more details see predict.brmsfit.

loo_args

An optional list of additional arguments passed to psislw. Ignored for non loo_* ppc types.

...

Further arguments passed to the ppc functions of bayesplot.

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 ~ log_Age_c + log_Base4_c * Trt_c
            + (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")
# }
# NOT RUN {
# }