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Re: st: Predictions based on reoprob and gllamm


From   Buzz Burhans <wsb2@cornell.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Predictions based on reoprob and gllamm
Date   Fri, 27 Feb 2004 19:44:33 -0500

Dear Sophia,

I have used -gllamm- with adaptive quadrature to model a repeated measurement in time of a continuous outcome , using a model that has both a level 2 random intercept and slope. I would like to be able to make a statement about the relative proportion of the total variance associated with the 2nd level random effects relative to the proportion that is explained by a fixed effect treatment variable.

My reading (Snijders & Bosker, 1999; Hox, 2002) suggests this is problematic for a number of reasons. However, I think, in my case, that it would be very useful to be able to make such a statement, even if only approximate, about the relative magnitude of the total variance attributable to the level 2 random effects as it contrasts with the relative magnitude of the variance explained by a treatment variable. ( In my case, I would like to point out that despite the treatment being significant, the magnitude of the effect is small versus the inherent variance ascribable to the level 2 units.)

Can you suggest a reasonable basis for making this statement when gllamm is used for a model such as this? I would be more comfortable deriving an approximate R2 if the model was a random intercept only, I am unsure of how to do this given a random coefficient also. I will confess to following conceptually, but not specifically, the Snijders & Bosker discussion, and don't think I am proficient enough to implement it.

I would be most appreciative of any insight you can share on this.

Thanks very much.

Buzz Burhans

Buzz Burhans
wsb2@cornell.edu


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