If you can demonstrate that results that should be
the same, from the same data and the same model,
are different from -xtmelogit- and -gllamm-, then
there is a problem.
I think you need at least to show (examples of) the commands
you used and the results you got for experts
on these commands to comment.
Nick
[email protected]
Martina Brandt
> Dear Nic, thanks a lot for your answer - i know that
> pseudo-r-squares are
> quite tricky. But the probem here is, that the same
> pseudo-r-square changes
> using xtmemixed instead of gllamm because the estimated
> variance of phat in
> comparison to the level 1 to level 4 variances is much
> smaller than it is
> using gllamm?!
> On Mon, 15 Oct 2007 17:03:56 +0100
> "Nick Cox" <[email protected]> wrote:
> > There is a entire bestiary of pseudo-R-squares
> > based on different kinds of analogy to R-square,
> > strong, weak and otherwise. There is no
> > reason in general why they should agree.
Martina Brandt
> >> mc kelvey and zavoina suggest an r2 for multilevel logit
> regression,
> >> which is the variance of the predicted probabilities
> divided by the
> >> total variance of the model (=proportion of explained
> >> variance). in the
> >> four level model this would be
> >> (var(phat))/((var(phat)+((pi2)/3))+var(level2)+var(level3)+var
> >> (level4))
> >> (see snijders & bosker 1999: 225).
> >> using gllamm i always had pseudo r2 around 0.20, and now
> >> using xtmelogit
> >> it is supposed to be only around 0.01. does anyone have an
> idea, why
> >> this could have happened and how these differences could
> be explained?
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