Posterior Draws of Predictive Errors

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

# \dontrun{
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
           data = inhaler, cores = 2)
#> Compiling Stan program...
#> Start sampling
#> 1200 / 2000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 1.691 seconds (Warm-up)
#> Chain 2:                0.762 seconds (Sampling)
#> Chain 2:                2.453 seconds (Total)
#> Chain 2: 
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 1.893 seconds (Warm-up)
#> Chain 1:                0.775 seconds (Sampling)
#> Chain 1:                2.668 seconds (Total)
#> Chain 1: 

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