Compute posterior predictive draws averaged across models. Weighting can be done in various ways, for instance using Akaike weights based on information criteria or marginal likelihoods.

- x
A

`brmsfit`

object.- ...
More

`brmsfit`

objects or further arguments passed to the underlying post-processing functions. In particular, see`prepare_predictions`

for further supported arguments.- weights
Name of the criterion to compute weights from. Should be one of

`"loo"`

,`"waic"`

,`"kfold"`

,`"stacking"`

(current default), or`"bma"`

,`"pseudobma"`

, For the former three options, Akaike weights will be computed based on the information criterion values returned by the respective methods. For`"stacking"`

and`"pseudobma"`

, method`loo_model_weights`

will be used to obtain weights. For`"bma"`

, method`post_prob`

will be used to compute Bayesian model averaging weights based on log marginal likelihood values (make sure to specify reasonable priors in this case). For some methods,`weights`

may also be a numeric vector of pre-specified weights.- method
Method used to obtain predictions to average over. Should be one of

`"posterior_predict"`

(default),`"posterior_epred"`

,`"posterior_linpred"`

or`"predictive_error"`

.- ndraws
Total number of posterior draws to use.

- nsamples
Deprecated alias of

`ndraws`

.- summary
Should summary statistics (i.e. means, sds, and 95% intervals) be returned instead of the raw values? Default is

`TRUE`

.- probs
The percentiles to be computed by the

`quantile`

function. Only used if`summary`

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`

.- model_names
If

`NULL`

(the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.- control
Optional

`list`

of further arguments passed to the function specified in`weights`

.- seed
A single numeric value passed to

`set.seed`

to make results reproducible.

Same as the output of the method specified
in argument `method`

.

Weights are computed with the `model_weights`

method.

```
if (FALSE) {
# model with 'treat' as predictor
fit1 <- brm(rating ~ treat + period + carry, data = inhaler)
summary(fit1)
# model without 'treat' as predictor
fit2 <- brm(rating ~ period + carry, data = inhaler)
summary(fit2)
# compute model-averaged predicted values
(df <- unique(inhaler[, c("treat", "period", "carry")]))
pp_average(fit1, fit2, newdata = df)
# compute model-averaged fitted values
pp_average(fit1, fit2, method = "fitted", newdata = df)
}
```