`R/posterior_epred.R`

`fitted.brmsfit.Rd`

This method is an alias of `posterior_epred.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.- scale
Either

`"response"`

or`"linear"`

. If`"response"`

, results are returned on the scale of the response variable. If`"linear"`

, results are returned on the scale of the linear predictor term, that is without applying the inverse link function or other transformations.- 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`

).- 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 *mean* 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 E x C array, where N is the
number of observations, E is the number of summary statistics, and C is the
number of categories. For all other families, the output is an N x E
matrix. The number of summary statistics E is equal to ```
2 +
length(probs)
```

: 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`

.

In multivariate models, an additional dimension is added to the output which indexes along the different response variables.

```
if (FALSE) {
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler)
## compute expected predictions
fitted_values <- fitted(fit)
head(fitted_values)
## plot expected predictions against actual response
dat <- as.data.frame(cbind(Y = standata(fit)$Y, fitted_values))
ggplot(dat) + geom_point(aes(x = Estimate, y = Y))
}
```