brms (version 2.14.0)

predictive_error.brmsfit: Posterior Samples of Predictive Errors

Description

Compute posterior samples of predictive errors, that is, observed minus predicted responses. Can be performed for the data used to fit the model (posterior predictive checks) or for new data.

Usage

# S3 method for brmsfit
predictive_error(
  object,
  newdata = NULL,
  re_formula = NULL,
  re.form = NULL,
  resp = NULL,
  nsamples = NULL,
  subset = NULL,
  sort = FALSE,
  ...
)

Arguments

object

An object of class brmsfit.

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.

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.

re.form

Alias of re_formula.

resp

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

nsamples

Positive integer indicating how many posterior samples should be used. If NULL (the default) all samples are used. Ignored if subset is not NULL.

subset

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

sort

Logical. Only relevant for time series models. Indicating whether to return predicted values in the original order (FALSE; default) or in the order of the time series (TRUE).

...

Further arguments passed to prepare_predictions that control several aspects of data validation and prediction.

Value

An S x N array of predictive error samples, where S is the number of posterior samples and N is the number of observations.

Examples

# NOT RUN {
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject), 
           data = inhaler, cores = 2)

## extract predictive errors
pe <- predictive_error(fit)
str(pe)
# }
# NOT RUN {
# }