--- anne olean <annekolean@yahoo.com> wrote:

> > --- Dale McLerran <stringplayer_2@YAHOO.COM> wrote: > > > > Note that when you fit GLIMMIX, you cannot use the > > AIC and BIC > > statistics. The likelihood reported by PROC MIXED > > is not the > > correct likelihood model. Moreover, GLIMMIX > employs > > an updated > > response variable with each iteration. That means > > that the > > model you fit determines the response variable for > > which GLIMMIX > > reports likelihoods. Now, you can only compare > > likelihoods if > > you have the same response variable in all your > > models. Since > > the model determines the (PROC MIXED) response > > variable, you > > cannot use any of the likelihood-based statistics > > reported by > > PROC MIXED for model comparison. > > > > Is there a reference where I may read up on the how > to > do model comparison when using glimmix? I searched > online but didn't find anything. What in the output > from GLIMMIX can I use to evaluate the fit if not > AIC/BIC etc? > I should have added in addition to the Glimmix model statistics that list deviance, scaled deviance and extra-dispersion scale. If the extra-dispersion scale is about .8, do I have to address the underdistpersion, or is this tolerable?

ako

> Given that I have a count outcome (ranging from 0 to > 7), would it be wrong to use proc mixed? I > understand > that it assumes a continuous outcome, but how robust > is proc mixed to this violation? > > thanks, ako > > > > > > __________________________________ > Do you Yahoo!? > Read only the mail you want - Yahoo! Mail SpamGuard. > http://promotions.yahoo.com/new_mail >

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