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.
Nick
[email protected]
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?
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
