Hello,

I’m fitting a logistic risk model on hospital data, given Region and Class predictors, with cases and denominators for each observation, with the aim of making simulations.

the model is:

`brm(cases | trials(num) ~ (1 | Region / Class), family = binomial(), data = DF, chains = 8, cores = 8, iter = 8000)`

I would like to predict the per hospital risk, but `predict.brmsfit()`

produces only the predicted count of events, and does not take a `type = 'response'`

argument as `predict.glm()`

to predict risk. I could use `posterior_linpred()`

and then `invlogit()`

but it doesn’t include the residual error in the prediction, which I need.

What am I missing?