In the continuing thread on grouping both variance structures and main effects
in -xtmixed- Stas Kolenikov <[email protected]> adds:
> Scott's suggestion gives you almost exactly what you need. He rendered the
> multiple group analysis of the linear predictor with the second model in
> which the two genders had separate intercepts and slopes (note the -nocons-
> option that gives you identification of the constant). It is just a matter
> of grouping the estimates in the final table.
> If you need the full fledge multiple group analysis with every parameter
> broken down into groups (and you would want to think about various
> invariances between your groups, really), you could also want to model the
> variances according to your poor/nonpoor status (gender in Scott's example).
> My naive attempt to do that with -xtmixed-, however, failed:
> xtmixed weight male agemale female agefemale, nocons || id:agemale
> agefemale, var cov(un) ml coll
> returns missing standard errors for the random effects. It probably could be
> fixed by imposing a block structure on that matrix, but -xtmixed- does not
> have that as an option.
-xtmixed- actually does support blocked structures for random effects. To
achieve this effect, you repeat random-effects specifications on the same
level ID variable (in this case, -id-) and you use one specification for each
block
. xtmixed weight male agemale female agefemale, nocons ///
|| id: agemale, cov(un) ///
|| id: agefemale, cov(un) var ml
This will ensure that no covariance is estimated between the effects on
-agemale- and those on -agefemale-. Such a covariance is, of course,
unidentified because a child cannot be both male and female within the course
of this study.
--Bobby
[email protected]
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