Compute posterior samples 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 samples computed by
posterior_epred while any residual error is ignored. However, the
estimated means of both methods averaged across samples 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,
nsamples = NULL,
subset = NULL,
sort = FALSE,
...
)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 samples should
be used. If NULL (the default) all samples are used. Ignored if
subset is not NULL.
A numeric vector specifying the posterior samples to be used.
If NULL (the default), all samples 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.
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 samples, 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.
# NOT RUN {
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler)
## compute expected predictions
ppe <- posterior_epred(fit)
str(ppe)
# }
# NOT RUN {
# }
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