The very last question for understanding. So, basically, SAS do not say anything about V matrix in general and I have to search for different solutions in these situations? It just seems strange that SAS check the positive definiteness of G and R matrices but not the V matrix. ... View more

I did but thank you so much too, you helped me with the solution! ... View more

Thank you so much for the explanation and your help! ... View more

I think it has every estimate because I have specified nobound option, otherwise some variances are 0. The matrix in general is NPD apparently. I have to use proc mixed because it is the procedure of my master thesis but I think I have found that type=chol is the same as type=FA0(q) in proc mixed, so I have now:   proc mixed data=stat method=reml nobound COVTEST;                                         class id bmi_mother sex country isced troubled; model bmi_z_score=slope_before slope_after bmi_mother sex country isced troubled/s cl ddfm=kr;              random int slope_before slope_after/ type=FA0(3) subject=id_no; format sex sex_new. isced isc. country count. troubled tr. bmi_mother catA.; by categ; run;   Both groups have no warning about NPD G matrix anymore and the estimates are changed just a little bit. Does it mean that I still have estimated the unstructured matrix just with this factor-analysis (as far as I understood from the Internet) and everything is good? I am not familiar with this method unfortunately.. It took more iterations too. Could you please look on my results? I made SE as well, it is possible with COVTEST. ... View more

Here is my SAS Code:   proc mixed data=stat method=reml nobound;                                         class id bmi_mother sex country isced troubled; model bmi_z_score=slope_before slope_after bmi_mother sex country isced troubled/s cl ddfm=kr;              random int slope_before slope_after/ type=un subject=id_no; format sex sex_new. isced isc. country count. troubled tr. bmi_mother catA.; by categ; run;   I have a piecewise mixed model, so I am assuming two random slopes-slope before the event and slope after. I saved some output in Word for one group where G matrix was NPD and attached it here. I deleted the reference categories too, I hope it is ok, was not sure what I am allowed to post. The output with random slopes and intercepts is too big because I have 760 subjects, so in the document is G matrix, intercept and R, iteration history, as well as fixed effects estimates. Without stratifying, the G matrix is ok. I think the problem is that it is just not enough variability in the data or something bacause for this group I have less observations per subject. The results look good compared to the other models but I am still not sure about conclusions. ... View more

Thank you very much for your answer. As random effects I have an intercept and two slopes, I can't remove any of them because it is what I have assumed in my analysis- that every subject is allowed to have random intercept and random slope. I suppose the problem is that I have somehow not enough observations after stratifying by group (one group is bigger than second and for bigger group D matrix is PD). Which part of the output or code would be helpful?     ... View more