Bradley,
The zero-inflated Poisson with random effects (the latter bit to take into account the repeated measures design) might be useful. I haven't found any Stata program that does this model (maybe GLLAMM can do it with some tweaking?), but I've programmed the mixed ZIP using SAS' proc NL MIXED. Drop me a mail if you're interested in this code.
Mads
***
Mads Meier Jæger
Senior Researcher
The Danish National Centre for Social Research
[email protected]
http://www.sfi.dk/sw234.asp
-----Oprindelig meddelelse-----
Fra: [email protected] [mailto:[email protected]] På vegne af Anderson, Bradley
Sendt: 23. januar 2009 17:25
Til: [email protected]
Emne: st: Comparing ZINB Models with clustering by subject
I'm modelling a count outcome with an excess of 0 counts. Conceptually and empirically a zero-inflated model makes sense. It's a repeated measures design. The number of observations per subject is relatively large (typically over 200) but varies across subjects. Variance estimates clearly need to be adjusted for the within subject clustering. I'd like to compare two models: In model 1 the X is an ordered categorical variable that takes on the values 0-3. In model 1 the effect of X is assumed linear. In model 2, X is represented by three dummy indicators. I want to compare the models to determine if the linear effect assumption is reasonable. Normally I'd just do a likelihood-ratio test but that's not appropriate when using robust standard errors adjusted for clustering. How can I compare the two models?
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