Dear all, it took some time to study all your suggestions, but didn't want
to respond before reading up on all of it.
Thank you!

I will break my model down into two different analyses, and do one in LME4,
the other in MCMCglmm.

I asked the MCMCglmm questions I had already, this will be an email about
the LME4 idea.


I use a long long dataset, in which every person has 12 lines (life events:
1-10 for life events 1-10, 11 = no life event, 12 = missing). At the same
time, every life event has 9 lines (the changescore of my 9 dependent
variables).

E.g. subject XXX has 9 lines for life event 4 (relationship ended), each of
them demoninating the changescore of my response variable from the time
before that life event to the time after.

I should be able to conduct an interaction analysis in LMER now, right (I
follow an idea written up in Hox, 2010, suggesting to use dummy variables
on the lowest level of nesting for the multivariate response variables, and
suppress the intercept with "-1").

> model.1 <- lmer(PHQ_change~-1+as.factor(PHQ_type)+as.factor(ALE_typeN) +
(-1+as.factor(PHQ_type)|UserID), data=data2)

PHQ_change = multivariate response variable
PHQ_type = categorical variable denominating the categories of my
multivariate response variable
ALE_typeN = categorical variable denominating the categories of Life Events

Does that make sense?

Thank you
E.

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