Let me use the example code about multivariate modeling in Paul Bürkner’s blog post to demonstrate my question:
library(MCMCglmm) library(brms) data("BTdata", package = "MCMCglmm") fit1 <- brm( mvbind(tarsus, back) ~ sex + hatchdate + (1|p|fosternest) + (1|q|dam), data = BTdata, chains = 2, cores = 2 )
I can obtain the posterior probability of an effect being positive for each of the two response variables with the following
pe <- fixef(fit1, summary = FALSE) sum(pe[,"tarsus_sexUNK"] > 0)/length(pe[,"tarsus_sexUNK"])  0.9555 sum(pe[,"back_sexUNK"] > 0)/length(pe[,"back_sexUNK"])  0.834
Suppose that I want to estimate the posterior probability of the effects for both response variables being positive (e.g.,
pe[,"back_sexUNK"]). How can I achieve this?