Dear all,
I am trying to fit, using MCMCglmm a multivariate mixed model (gaussian)
with an underlying multiple membership structure and I am not certain about
my code...
Background: I have raters (N~200) who came from different origins (A,B,C)
and having different sexes (M,F), they ranked 4 sets of 20 objects (sets
1,2,3,4) according to their preferences.
I want to analyse wether origin and sex influence similarity in rankings so
I have computed correlations between all pairs of raters (~20,000).
Cor1 represents the correlation observed between pairs of rater for the set
1, cor2 represents the correlation observed between pairs of rater for the
set 2 (in the same order of pairs), cor3 for set3 and cor4 for set4.
id1 represents the identity of one rater and id2 represents the identity of
the other rater within each pair of raters.
I assume that the set of objects should not influence my fixed effects, but
could influence residuals and random effects.
I coded:

    prior <- list(R=list(V=1,nu=0.002),G=list(G1=list(V=1,nu=0.002)))
    mod <- MCMCglmm(fixed=cbind(cor1,cor2,cor3,cor4)~sex*origin,
                  random=~idv(mult.memb(~trait:id1+trait:id2)),
                  rcov=~trait:units,
                  family=rep("gaussian",4),
                  data=data,
                  prior=prior)

Does it sounds good? I am particularly worried about the part
"random=~idv(mult.memb(~trait:id1+trait:id2))".
Thanks a lot.
_
alex

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