`R/posterior_epred.R`

`posterior_epred.brmsfit.Rd`

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 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

`brmsfit`

.- newdata
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.- re_formula
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.- re.form
Alias of

`re_formula`

.- 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 non-linear parameter. If specified, expected predictions of this parameters are returned.

- ndraws
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`

.- draw_ids
An integer vector specifying the posterior draws to be used. If

`NULL`

(the default), all draws are used.- sort
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 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.

`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.