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
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
# 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, ... )
An object of class
An optional data.frame for which to evaluate predictions. If
formula containing group-level effects to be considered in
the prediction. If
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
An integer vector specifying the posterior draws to be used.
Logical. Only relevant for time series models.
Indicating whether to return predicted values in the original
Further arguments passed to
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.
NA values within factors in
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
New levels can be sampled in multiple ways, which can be controlled
sample_new_levels. Both of these arguments are
prepare_predictions along with several
other useful arguments to control specific aspects of the predictions.