The following usage note provides sample programs for obtaining subject-specific intercept and slope -- http://support.sas.com/kb/37109 The following note shows how to plot these subject-specific models -- http://support.sas.com/kb/37110   Hope this helps, Jill ... View more

Like @SteveDenham mentioned, you can create a new variable indicating which type the response is, turn the data into a long format and fit a model accordingly. Please note that there is only one MODEL statement allowed in PROC MIXED, and only one response variable in the MODEL statement to be specified. Here is an example to show you this approach: subject  height weight gender 1              165         56          F 2             180         76          M .... data two;  set one;  response=height;  type='Height'; output; response=weight; type='Weight'; output; drop height weight; run; Then use Response as your dependent variable in the MODEL statement, and include TYPE as one of your effect in the MODEL statement. In your case, the values for TYPE would be your original response variables. Hope this helps, Jill ... View more

This might not answer your question directly, but it looks to me that you omitted the dist=binary link=logit options in the MODEL statement. Also the RANDOM statement seems to be for the R-side random effect, which means you need to add the RESIDUAL option in the RANDOM statement. But because of the METHOD=LAPLACE option you specified, R-side random effect might not be possible. You either need to take out this METHOD=option when adding the RESIDUAL option in the RANDOM statement, or, if you keep the METHOD=LAPLACE option, you might try the following RANDOM statement for the G-side random effect -- random int / subject=PtID(Treatment);  Thanks, Jill ... View more

The program seems to take forever. If you interrupt it, then the reported Error message might appear. This was produced because the execution was in the middle and some matrices have not been cleared. If you terminate the program like this, then the error is kind of “normal” and there is nothing to do about it. I let it run over days and nights, and got the following messages in the Log -- NOTE: Saturated mean structures in models are analyzed with the FIML method. NOTE: Sample statistics such as correlation matrix or mean vector are based on the complete       observations only. ERROR: QUANEW Optimization cannot be completed. NOTE: QUANEW needs more than 100 iterations or 1000 function calls. NOTE: The FIML estimation of the saturated model converged (function value = 818.4849, df = 0). NOTE: The FIML estimation of the baseline model converged (function value = 893.9941, df =       5995). NOTE: Due to optimization failure, statistics in the Fit Summary table might not be       appropriate. NOTE: Due to optimization failure, fit statistics in path diagrams might not be appropriate. NOTE: Due to optimization failure, standard error estimates are not computed. NOTE: Because standard error estimates are not available, flagging for significant estimates       is not done in path diagrams. NOTE: PROCEDURE CALIS used (Total process time):       real time           65:55:43.65       cpu time            65:53:28.92   The program did not converge, unfortunately.   You might increase the maxiter= value and maxfunc value. For example, add maxiter=1000 maxfunc=5000 options in the PROC CALIS statement. But by increasing the number of iterations, you might or might not be able to find a converged solution. For a model this large, there is really no guarantee for this. You might want to try other optimization techniques or estimation methods, for example, method=ML, or other method= option as described in the documentation -- https://go.documentation.sas.com/doc/en/pgmsascdc/v_049/statug/statug_calis_syntax13.htm#statug.calis.calismethod   Hope this helps, Jill ... View more

Thanks you for sending me the data. The program has been running since last night.... I will update you as soon as I have more information. Thanks, Jill ... View more

Can you send in the data? Thanks, Jill ... View more

What release of SAS are you using? Please submit the following code and send me the Log -- %put Site: &syssite  Release: &sysvlong  System: &sysscp &sysscpl; Can you send in the data? If not here in the forum, you might open a case with SAS Technical Support and I will be happy to provide further assistance -- SAS Technical Support | SAS Support Thanks, Jill     ... View more

cross classified models do take a lot of resources. Is this really a cross-classified model? Or can it be written as a nested model? You might want to take a closer look at your data, especially how var1 and var2 are releated to subject, to answer this question. It is also possible that your memsize is set at the default 2GB, which is not nearly enough. You can run the following code and find out your memsize: proc options option=memsize value; run; if it is 2GB, you can edit the sasv9.cfg file to increase the memsize. Hope this helps, Jill ... View more

The graph from PROC PLM shows the lsmeans when the values for other effects are at zero, as shown in the footnote of the plot; the graph from LSMEANS shows the lsmeans when the values for other related effects are at their means. You can add the E option in the LSMEANS statement to see exactly how the lsmeans correspond to. Hope this helps, Jill ... View more

You may ignore the warning message in this case. Thanks, Jill ... View more

yes, it seems that either model works for your data. ... View more

Your second PROC MIXED program is missing subject=record_id option in the REPEATED statement. ... View more

remove the "/" before NOINT.  ... View more