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
