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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