R/posterior_epred.R
posterior_epred.brmsfit.Rd
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, ... )
object  An object of class 

newdata  An optional data.frame for which to evaluate predictions. If

re_formula  formula containing grouplevel effects to be considered in
the prediction. If 
re.form  Alias of 
resp  Optional names of response variables. If specified, predictions are performed only for the specified response variables. 
dpar  Optional name of a predicted distributional parameter. If specified, expected predictions of this parameters are returned. 
nlpar  Optional name of a predicted nonlinear parameter. If specified, expected predictions of this parameters are returned. 
ndraws  Positive integer indicating how many posterior draws should
be used. If 
draw_ids  An integer vector specifying the posterior draws to be used.
If 
sort  Logical. Only relevant for time series models.
Indicating whether to return predicted values in the original
order ( 
...  Further arguments passed to 
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.
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.