Posterior Draws of Predictive Errors — predictive

Compute posterior draws 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.

# S3 method for brmsfit
predictive_error(
  object,
  newdata = NULL,
  re_formula = NULL,
  re.form = NULL,
  method = "posterior_predict",
  resp = NULL,
  ndraws = NULL,
  draw_ids = 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.
method: Method used to obtain predictions. Can be set to "posterior_predict" (the default), "posterior_epred", or "posterior_linpred". For more details, see the respective function documentations.
resp: Optional names of response variables. If specified, predictions are performed only for the specified response variables.
ndraws: Positive integer indicating how many posterior draws should be used. If NULL (the default) all draws are used. Ignored if draw_ids is not NULL.
draw_ids: An integer vector specifying the posterior draws to be used. If NULL (the default), all draws 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 draws, where S is the number of posterior draws and N is the number of observations.

Examples

if (FALSE) {
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
           data = inhaler, cores = 2)

## extract predictive errors
pe <- predictive_error(fit)
str(pe)
}