MCMCglmm multinomial model in R

Question

I\'m trying to create a model using the MCMCglmm package in R.

The data are structured as follows, where dyad, focal, other are all random effects, predict

Answer 1

Answer to my first question, based on the HLP post and some help from a colleage/stats consultant:

# values for prior
k <- 5 # originally: length(levels(dative$SemanticClass)), so k = # of outcomes for SemanticClass     aka categorical outcomes 
I <- diag(k-1) #should make matrix of 0's with diagonal of 1's, dimensions k-1 rows and k-1 columns
J <- matrix(rep(1, (k-1)^2), c(k-1, k-1)) # should make k-1 x k-1 matrix of 1's 

And for my model, using the multinomial5 family and 5 outcome variables, the prior is:

prior = list(
             R = list(fix=1, V=0.5 * (I + J), n = 4),
             G = list(
               G1 = list(V = diag(4), n = 4))

For my second question, I need to add an interaction term to the fixed effects in this model:

 m <- MCMCglmm(cbind(Resp1, Resp2...) ~ -1 + trait*predictorvariable,
 ...

The result gives both main effects for the Response variables and posterior estimates for the Response/Predictor interaction (the effect of the predictor variable on each response variable).