cross classified tobit model

[smoller]

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!

[SteveDenham]

I hope Dale drops by.  He has posted a lot on NLMIXED, and I believe he has addressed the multiple random effect by a vectored approach.