Stuart kindly hinted at MCMCglmm as a possible solution for the model I
would like to calculate.

I studied the MCMCglmm package and wonderfully detailed documentation in
the last days, but was not able to answer a couple of questions, the two
most important ones being:

(1) How would a sample syntax look like for both a (a) multivariate model
which is (b) at the same time repeated measure, including subjects as
random effect also?

(2) How does one differentiate between time-varying and time-invariant
covariates in MCMCglmm?

My goal is to calculate something like this:
* y1 - y9; they are intercorrelated ordinal (0,1,2,3) items from a
psychological screening instrument, left-skewed (plenty of 0s, few 3s)
* x1 - x5: baseline covariates (ordered and continuous)
* z1 - z10: time-varying dichotomous covariates (life events, assessed at
every measurement point for the time between this and the last measurement
point)
* 5 measurement points
* heterogeneity in intercepts and slopes between subjects, so this should
also go in as random effect.

The current hypothesis in the literature is that the predictors affect the
outcome variables in a similar fashion (because the outcome variables load
onto the same factor), my main goal is in showing that this is not the
case, and that there are differential effects of the predictors on the
response variables.

Any advice as to how to do this in MCMCglmm would be highly appreciated.

Thank you
Eiko

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