I don't really know if Mplus will handle the weights the way you want.
But other than that it might just compute all the
within-between-whatever matrices at different levels and run the model
very quickly with them I don't really know much more though. -gllamm-,
on the other hand, works by integrating everything numerically, and
that's why it is so slow. Sophia herslef discrecommends -gllamm- for
continuous response data. If Mplus would also have to do integration,
it would probably be through a faster binary code (rather than the
interpreted code of -gllamm-). On the other hand, on a parallel
computer, -gllamm- under Stata/MP4 or MP8 might beat Mplus since Stata
is parallelized (and I doubt Mplus is).
You should be able to get decent starting values from unweighted
analysis using -xtmixed-. Good starting values can improve estimation
time by a factor of two.
On 6/18/09, P C <[email protected]> wrote:
> Wow, it does take a lot of time. I heard about the negative side of gllamm. I am wondering whether I should try Mplus. Do you know if it does a better job (time-wise) for this kind of modelling?
>
> There is a need for special handling of the school-level weights. I am trying to understand it from her paper but it's not just as simple as scaling.
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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