This method is an alias of `predictive_error.brmsfit`

with additional arguments for obtaining summaries of the computed draws.

- 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.- method
Method used to obtain predictions. Can be set to

`"posterior_predict"`

(the default),`"posterior_epred"`

, or`"posterior_linpred"`

. For more details, see the respective function documentations.- type
The type of the residuals, either

`"ordinary"`

or`"pearson"`

. More information is provided under 'Details'.- resp
Optional names of response variables. If specified, predictions are performed only for the specified response variables.

- 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`

).- summary
Should summary statistics be returned instead of the raw values? Default is

`TRUE`

..- robust
If

`FALSE`

(the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If`TRUE`

, the median and the median absolute deviation (MAD) are applied instead. Only used if`summary`

is`TRUE`

.- probs
The percentiles to be computed by the

`quantile`

function. Only used if`summary`

is`TRUE`

.- ...
Further arguments passed to

`prepare_predictions`

that control several aspects of data validation and prediction.

An `array`

of predictive error/residual draws. If

`summary = FALSE`

the output resembles those of

`predictive_error.brmsfit`

. If `summary = TRUE`

the output
is an N x E matrix, where N is the number of observations and E denotes
the summary statistics computed from the draws.

Residuals of type `'ordinary'`

are of the form \(R = Y -
Yrep\), where \(Y\) is the observed and \(Yrep\) is the predicted response.
Residuals of type `pearson`

are of the form \(R = (Y - Yrep) /
SD(Yrep)\), where \(SD(Yrep)\) is an estimate of the standard deviation of
\(Yrep\).