I recommend looking at chapter 6 of Paul Allison's Fixed Effects Regression 
Models.  This chapter outlines how you can use a structural equation modeling 
framework to estimate a multi-level model (a random effects model).  This 
approach is slower than just using MLM software like lmer() in the lme4 
package, but has the advantage of being able to specify correlations between 
errors across time, the ability to control for time-invariant effects of 
time-invariant variables, and allows you to use the missing data maximum 
likelihood that comes in structural equation modeling packages.

Andrew Miles
Department of Sociology
Duke University

On Apr 6, 2012, at 9:48 AM, Eiko Fried wrote:

> Hello,
> 
> I've been trying to answer a problem I have had for some months now and
> came across multivariate multilevel modeling. I know MPLUS and SPSS quite
> well but these programs could not solve this specific difficulty.
> 
> My problem:
> 9 correlated dependent variables (medical symptoms; categorical, 0-3), 5
> measurement points, 10 time-varying covariates (life events; dichotomous,
> 0-1), N ~ 900. Up to 35% missing values on some variables, especially at
> later measurement points.
> 
> My exploratory question is whether there is an interaction effect between
> life events and symptoms - and if so, what the effect is exactly. E.g. life
> event 1 could lead to more symptoms A B D whereas life event 2 could lead
> to more symptoms A C D and less symptoms E.
> 
> My question is: would MMM in R be a viable option for this? If so, could
> you recommend literature?
> 
> Thank you
> --T
> 
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> 
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