predictive_error.brmsfit: Posterior Draws of Predictive Errors

In brms: Bayesian Regression Models using 'Stan'

Posterior Draws of Predictive Errors

Description

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.

Usage

## S3 method for class '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 (excluding grouping variables) 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. NA values within grouping variables are treated as a new level.
re_formula	formula containing group-level effects to be considered in the prediction. If NULL (default), include all group-level effects; if NA or ~0, 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

## 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)

## End(Not run)

brms documentation built on Sept. 23, 2024, 5:08 p.m.