cross classified tobit model

smoller — Thu, 28 Jul 2011 18:53:59 GMT

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!

cross classified tobit model

SteveDenham — Fri, 29 Jul 2011 14:56:23 GMT

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.

topic cross classified tobit model in SAS Procedures

cross classified tobit model

cross classified tobit model