Calculate marginal predictions from a `brms`

model.
Marginal predictions average over the input data for each posterior draw.
Marginal predictions for models with random effects will integrate
over random effects.
Arguments are labeled as *required* when it is required that the
user directly specify the argument. Arguments are labeled as
*optional* when either the argument is optional or there are
sensible default values so that users do not typically need to specify
the argument.

## Arguments

- object
A

*required*argument specifying a fitted`brms`

model object.- data
A

*required*argument specifying a data frame or data table passed to`fitted()`

as the new data to be used for predictions.- summarize
An

*optional*argument, a logical value, whether or not to calculate summaries of the posterior predictions. Defaults to`TRUE`

.- posterior
An

*optional*argument, a logical value whether or not to save and return the posterior samples. Defaults to`FALSE`

as the assumption is a typical use case is to return the summaries only.- index
An

*optional*argument, an integer vector, giving the posterior draws to be used in the calculations. If omitted, defaults to all posterior draws.- dpar
An

*optional*argument, the parameter passed on to the`dpar`

argument of`fitted()`

in brms. Defaults to`NULL`

indicating the mean or location parameter typically.- resample
An

*optional*argument, an integer indicating the number of bootstrap resamples of the posterior predictions to use when calculating summaries. Defaults to`0L`

. See documentation from`.averagePosterior()`

for more details. This should be considered experimental.- resampleseed
An

*optional*argument, a seed for random number generation. Defaults to`FALSE`

, which means no seed is set. Only used if`resample`

is a positive, non-zero integer. See documentation from`.averagePosterior()`

for more details. This should be considered experimental.- effects
An

*optional*argument, a character string indicating the type of prediction to be made. Can be one of “fixedonly” meaning only use fixed effects, “includeRE” meaning that random effects should be included in the predictions, or “integrateoutRE” meaning that random effects should be integrated out / over in the predictions. It defaults to “fixedonly” so is not typically required for a user to specify it.- backtrans
An

*optional*argument, a character string indicating the type of back transformation to be applied. Can be one of “response” meaning to use the response scale, “linear” or “identity” meaning to use the linear predictor scale, or a specific back transformation desired, from a possible list of “invlogit”, “exp”, “square”, or “inverse”. Custom back transformations should only be needed if, for example, the outcome variable was transformed prior to fitting the model. It defaults to “response” so is not typically required for a user to specify it.- k
An

*optional*argument, an integer providing the number of random draws to use for integrating out the random effects. Only relevant when`effects`

is “integrateoutRE”. It defaults to`100L`

, a rather arbitrary number attempting to balance the increased precision that comes from a larger value, with the increased computational cost of more Monte Carlo simulations when integrating out random effects.- raw
An

*optional*argument, a logical value indicating whether to return the raw output or to average over the Monte Carlo samples. Defaults to`FALSE`

. Setting it to`TRUE`

can be useful if you want not only the full posterior distribution but also the`k`

Monte Carlo samples used for the numerical integration. This cannot be used with`summarize = TRUE`

.- ...
An

*optional*argument, additional arguments passed to`fitted()`

.

## References

Pavlou, M., Ambler, G., Seaman, S., & Omar, R. Z. (2015) doi:10.1186/s12874-015-0046-6 “A note on obtaining correct marginal predictions from a random intercepts model for binary outcomes” and Skrondal, A., & Rabe-Hesketh, S. (2009) doi:10.1111/j.1467-985X.2009.00587.x “Prediction in multilevel generalized linear models”