posterior_epred.brmsfit {brms}  R Documentation 
Compute posterior draws of the expected value 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 expected value of the posterior predictive distribution is
incorporated in the draws computed by posterior_epred
while the
residual error is ignored there. However, the estimated means of both methods
averaged across draws should be very similar.
## S3 method for class '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 
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.
An array
of draws. 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.
## Not run:
## fit a model
fit < brm(rating ~ treat + period + carry + (1subject),
data = inhaler)
## compute expected predictions
ppe < posterior_epred(fit)
str(ppe)
## End(Not run)