I have a truncated dependent variable, and I need to run a cross-classified growth model. If it weren’t truncated, my model would look like this: proc mixed data=std noclprint covtest; class sch time raceses; model learn= raceses time raceses*time alagcollfact alagcollfact*time alagcollfact*raceses alagcollfact*raceses*time alagcommfact alagcommfact*time alagcommfact*raceses alagcommfact*raceses*time/ ddfm=bw s ; repeated time/type=cs subject=childid3; random intercept / subject=sch; *random intercept/ subject=sch*childid3; weight weightvar; by _imputation_ ; lsmeans raceses*time/at alagcommfact=-.78 diff cl; lsmeans raceses*time/at alagcommfact=.792 diff cl; format raceses raceses.; run; So, I see that nlmixed allows truncated dependent variables, but as far as I can tell, it doesn’t allow for cross-classified models because it only permits one subject. Can you recommend a way for me to turn the model above into a tobit model that is right censored (The dependent variable ranges from 1 to 4 in .1 increments and it is top censored (or truncated) at 4. FYI: I found the following syntax online for truncated data (but again, I don’t think I can add two random statements): http://www.ats.ucla.edu/stat/sas/code/random_effect_tobit.htm Thank you!
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