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
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,
Please also provide the following information in addition to your question:
- Operating System: MacOS Mojave or
- brms Version: 2.8.0