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Re: st: Multilevel analysis and GLLAMM


From   Stas Kolenikov <skolenik@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Multilevel analysis and GLLAMM
Date   Tue, 21 Sep 2004 10:46:38 -0400

> 4. If using the gamma family with the canonical link function. Is
> interpretation of the signs of the slope coefficients opposite to the
> direction of the relationship. That is, given it is a reciprocal link, does
> a negative coefficient actually signify a positive relationship between the
> variables? Is it reasonable to use an identity link instead?

The canonical links should be giving more efficient estiamates, and
also guarantee that you don't get outside of the natural range of your
responses, so you are statitically better off sticking to them. You
are right with the interpretation.

> 5. Finally, being new to this type of analysis, I was wondering if anyone
> could comment on the relative strengths and weaknesses of using GLLAMM
> versus a program such as HLM.

HLM (or M-plus) are more specific, and thus faster. With -gllamm-, you
can use all Stata tricks for data management, testing, etc.

> Here is an example of my commands, varx  and vary are the repeated measures
> the remaining variables are level 2 predictors:
> 
> Gen cons=1
> Eq cons: cons
> Eq slope: varx
> 
> gllamm vary varx varz varw ,i(id) family(gamma) nrf(2) eqs(cons slope)
> adapt

I would tend to think that -gllamm- would take it that -vary- depends
on -varx varz varw-, as it only takes one dependent variable. You
would need to take your data to the long form by -reshape-, and then
code individual variables by dummies. Then your -s()- option would
give the name of those dummies so that you can have different
variances for different measures. See Section 4.1 of the manual for
similar treatment.

This also explains your -1 correlations: you have -varx- both as an
explanatory variable for -vary-, and as a slope for random
coeffcients. That's kind of weird for -gllamm-.

-- 
Stas Kolenikov
http://stas.kolenikov.name
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