Calculate marginal coefficients from a `brms`

generalized linear mixed model using the method proposed by Hedeker (2018).

## Usage

```
marginalcoef(
object,
summarize = TRUE,
posterior = FALSE,
index,
backtrans = c("response", "linear", "identity", "invlogit", "exp", "square",
"inverse"),
k = 100L,
...
)
```

## Arguments

- object
A fitted brms model object that includes random effects. Required.

- summarize
A logical value, whether or not to calculate summaries of the posterior predictions. Defaults to

`TRUE`

.- posterior
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 integer vector, giving the posterior draws to be used in the calculations. If omitted, defaults to all posterior draws.

- backtrans
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.

- k
An integer providing the number of random draws to use for integrating out the random effects. Only relevant when

`effects`

is “integrateoutRE”.- ...
Additional arguments passed to

`fitted()`

## References

Hedeker, D., du Toit, S. H., Demirtas, H. & Gibbons, R. D. (2018) doi:10.1111/biom.12707 “A note on marginalization of regression parameters from mixed models of binary outcomes”