This method is an alias of `posterior_predict.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.- transform
(Deprecated) A function or a character string naming a function to be applied on the predicted responses before summary statistics are computed.

- resp
Optional names of response variables. If specified, predictions are performed only for the specified response variables.

- negative_rt
Only relevant for Wiener diffusion models. A flag indicating whether response times of responses on the lower boundary should be returned as negative values. This allows to distinguish responses on the upper and lower boundary. Defaults to

`FALSE`

.- 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`

).- ntrys
Parameter used in rejection sampling for truncated discrete models only (defaults to

`5`

). See Details for more information.- cores
Number of cores (defaults to

`1`

). On non-Windows systems, this argument can be set globally via the`mc.cores`

option.- 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 predicted response values.
If `summary = FALSE`

the output resembles those of

If `summary = TRUE`

the output depends on the family: For categorical
and ordinal families, the output is an N x C matrix, where N is the number
of observations, C is the number of categories, and the values are
predicted category probabilities. For all other families, the output is a N
x E matrix where E = `2 + length(probs)`

is the number of summary
statistics: The `Estimate`

column contains point estimates (either
mean or median depending on argument `robust`

), while the

`Est.Error`

column contains uncertainty estimates (either standard
deviation or median absolute deviation depending on argument

`robust`

). The remaining columns starting with `Q`

contain
quantile estimates as specified via argument `probs`

.

```
if (FALSE) {
## fit a model
fit <- brm(time | cens(censored) ~ age + sex + (1 + age || patient),
data = kidney, family = "exponential", init = "0")
## predicted responses
pp <- predict(fit)
head(pp)
## predicted responses excluding the group-level effect of age
pp <- predict(fit, re_formula = ~ (1 | patient))
head(pp)
## predicted responses of patient 1 for new data
newdata <- data.frame(
sex = factor(c("male", "female")),
age = c(20, 50),
patient = c(1, 1)
)
predict(fit, newdata = newdata)
}
```