This was very helpful. Thank you so much. ... View more

Thank you very very much. The code worked and I got all the results I was looking for. I appreciate your help a lot. ... View more

I appreciate all the suggestions I received so far. I tried simplifying the random statement, using the subject in the random statement and adding the NLOPTIONS maxiter=1000 statement. I still have the convergence issues and I did not get the results I am looking for. Here is the sample data: RespID pant_num group_num time DemAge00 HHFS_bi 16 1 1 1 2 0 16 1 1 2 4 1 20 3 1 1 2 1 20 3 1 2 3 1 21 2 2 1 4 1 21 2 2 2 3 1 22 4 2 1 4 1 22 4 2 2 2 1 24 3 2 1 3 0 24 3 2 2 4 1 30 4 2 1 2 0 30 4 2 2 3 1   RespId is the respondent ID, Pant_num is the pantry number, Group_num: 1=control group, 2=intervention group. I would like to get the odds of people in either group(control and intervention) being in the food secure category after the intervention. if HHFS_bi (0=food insecure and 1=food secure) has changed overtime.   This is my code currently: proc glimmix data=home.FS_Binary; NLOPTIONS maxiter=1000; class RespID pant_num group_num time DemAge00; model HHFS_bi(ref="0")=group_num time DemAge00 group_num*time / dist=binary link=logit solution ddfm=kr oddsratio; /* since we added PartID and site in the random statement, they are considered as random effect and do not need to be included in the model */ random RespID(pant_num); lsmeans group_num*time /oddsratio ilink e;/*see the position of the levels below*/ estimate 'intervention T2-T1 - control T2-T1' group_num*time 1 -1 -1 1/ exp cl; run; and this is the log message:  378 proc glimmix data=home.FS_Binary; NLOPTIONS maxiter=1000; 379 class RespID pant_num group_num time DemAge00; 380 model HHFS_bi(ref="0")=group_num time DemAge00 group_num*time / dist=binary link=logit 380! solution ddfm=kr oddsratio; /* since we added PartID and site in the random statement, 380! they are considered as random effect and do not need to be included in the model */ 381 random RespID(pant_num); 382 lsmeans group_num*time /oddsratio ilink e;/*see the position of the levels below*/ 383 estimate 'intervention T2-T1 - control T2-T1' group_num*time 1 -1 -1 1/ exp cl; 384 run;   NOTE: Some observations are not used in the analysis because of: missing response values (n=60), missing fixed effects (n=2), missing random effects (n=2). NOTE: The GLIMMIX procedure is modeling the probability that HHFS_bi='1'. NOTE: Did not converge. NOTE: PROCEDURE GLIMMIX used (Total process time): real time 2.38 seconds cpu time 2.35 seconds   ... View more

Thank you. I changed that and I still do not have the odds ratios. Please see below the results:        Here is the log message:     ... View more

Thank you so much. I changed the model statement and the code is running ok however, I still have the convergence issue and I am not getting the odds ratios in my results. Please see below how my results are coming out:      ... View more

My apologies for the confusion. it is 4 independent categorical variables and 1 dependent continous variable ... View more

Here is my code PROC CORR data=Home.BaselineCH OUTPLC=home.polyCorr; with HEI2010_TOTAL_SCORE; var S1Q2; run; and this is the my log <418  PROC CORR data=Home.BaselineCH OUTPLC=home.polyCorr nomiss; 419  with HEI2010_TOTAL_SCORE; 420  var S1Q2; 421  run;   WARNING: The number of groups for variable HEI2010_TOTAL_SCORE is greater than 20, the number in the          NGROUPS= option, the affected polychoric correlations are set to missing. NOTE: The data set HOME.POLYCORR has 4 observations and 3 variables. NOTE: PROCEDURE CORR used (Total process time):       real time           0.08 seconds       cpu time            0.06 seconds> ... View more