On 2004-07-13, at 21.35, Stas Kolenikov wrote:
Myself writing:
You could of course use the -geqs()- option of -gllamm- to model the
random effects as dependent variables in the same model. However, what
do you think about using the estimated standard deviation of the
random
effects to calculate a probability weight and use that probability
weight in a second model?
Well I am not sure I see how this would be done. Stata does have the
analytical weights that are related to variances... but here you've
only
got one estimate, not observation-by-observation variances. The
estimate
of the RE variance is consistent (provided there is no model
misspecification of course), so you can use it in any way you want.
That's
my view on this.
I think I need to clarify. -gllapred- estimates subject specific
standard deviations and means (i.e. one estimate for every subject).
Now, if the theory is that there is a "true" subject specific effect
and that effect is approximated as a random effect with a mean and a
standard deviation over a series of repeated measures, then it should
be ok to use the standard deviation of this estimate to calculate a
weight that adjust for uncertainties in the estimate.
My question is if you think that a subject specific probability weight
could be used to model this uncertainty of the estimated random effects
in a second model?
I haven't tried it yet but, I have a few ideas of exploring individual
differences in some of my data. It is possible (in theory) to model
most these models directly with -�gllamm-. However, it is often not
feasible because of the heavy burden on my computer.
Michael
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