Compute posterior draws of the expected value/mean of the posterior predictive distribution. Can be performed for the data used to fit the model (posterior predictive checks) or for new data. By definition, these predictions have smaller variance than the posterior predictions performed by the posterior_predict.brmsfit method. This is because only the uncertainty in the mean is incorporated in the draws computed by posterior_epred while any residual error is ignored. However, the estimated means of both methods averaged across draws should be very similar.

# S3 method for brmsfit
posterior_epred(
object,
newdata = NULL,
re_formula = NULL,
re.form = NULL,
resp = NULL,
dpar = NULL,
nlpar = NULL,
ndraws = NULL,
draw_ids = NULL,
sort = FALSE,
...
)

## Arguments

object An object of class brmsfit. 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. 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. Alias of re_formula. Optional names of response variables. If specified, predictions are performed only for the specified response variables. Optional name of a predicted distributional parameter. If specified, expected predictions of this parameters are returned. Optional name of a predicted non-linear parameter. If specified, expected predictions of this parameters are returned. 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. An integer vector specifying the posterior draws to be used. If NULL (the default), all draws are used. 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 array of predicted mean response values. For categorical and ordinal models, the output is an S x N x C array. Otherwise, the output is an S x N matrix, where S is the number of posterior draws, N is the number of observations, and C is the number of categories. In multivariate models, an additional dimension is added to the output which indexes along the different response variables.

## Details

NA values within factors in newdata, 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.

In multilevel models, it is possible to allow new levels of grouping factors to be used in the predictions. This can be controlled via argument allow_new_levels. New levels can be sampled in multiple ways, which can be controlled via argument sample_new_levels. Both of these arguments are documented in prepare_predictions along with several other useful arguments to control specific aspects of the predictions.

## Examples

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

## compute expected predictions
ppe <- posterior_epred(fit)
str(ppe)
}