Hi everybody!

First of all thanks for developing and supporting stan and in particular brms! I am developing a multivariate model, with the idea that most of my responses are biologically correlated. To do so my formula is something like:

`formula <- mvbind(trait1, trait2 , trait3) ~ Clim + elevation + (1 | gr(taxa))`

`fit <- brm(bf(formula) + set_rescor(T), data = train_data, cov_ranef = list(taxa = cv_mat))`

Now, I would like to test the value of using set_rescor(T) (including residuals correlations among responses) by testing *whether knowing one of the responses (e.g. trait1) will reduce the uncertainty in predictions on the others (trait2 and trait3), on held out data*.

I thought that using `predict`

and passing `list(trait1=test$trait1)`

as `new_objects`

would make the trick, but I am not sure it is the right way of going. What’s the proper way to include 1/more known responses to predict other responses in a multivariate model?

I am sorry if it is a fairly naive question, but I couldn’t find much surfing around.

Have a great day,

Sergio

Please also provide the following information in addition to your question:

- Operating System: MacOS Mojave or
- brms Version: 2.8.0