I agree that the random statements could be   random loc a*loc a*b*loc; random rep(loc) a*rep(loc); and are generally/probably what I would use unless, as you note, I have a strip-plot element in the design or perhaps a lack of random assignment of treatment to experimental units. Still, most of the designs I work with are small samples, and estimating fewer variance components is nearly always the better route!     ... View more

Neither Code 1 or Code 2 is correct. You need to include Age_group, Income_group, Education, and Gender in the CLASS statement (as in Code 2) as well as in the MODEL statement (Code 1).   The default is PARAM=EFFECT, so you do not need to include that specification unless you just want to.   I hope this helps.   ... View more

There may be more elegant ways, but this is how I do it:   /* Example data from https://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#statug_glimmix_sect025.htm modified by making zero counts be ones for tidiness */ data foot_mv; input yr b1 b2 b3 k1 k2 k3; sire = _n_; datalines; 1 1 0 0 52 25 1 1 1 0 0 49 17 1 1 1 0 0 50 13 1 1 1 0 0 42 9 1 1 1 0 0 74 15 1 1 1 0 0 54 8 1 1 1 0 0 96 12 1 1 -1 1 0 57 52 9 1 -1 1 0 55 27 5 1 -1 1 0 70 36 4 1 -1 1 0 70 37 3 1 -1 1 0 82 21 1 1 -1 1 0 75 19 1 1 -1 -1 0 17 12 10 1 -1 -1 0 13 23 3 1 -1 -1 0 21 17 3 -1 0 0 1 37 41 23 -1 0 0 1 47 24 12 -1 0 0 1 46 25 9 -1 0 0 1 79 32 11 -1 0 0 1 50 23 5 -1 0 0 1 63 18 8 -1 0 0 -1 30 20 9 -1 0 0 -1 31 33 3 -1 0 0 -1 28 18 4 -1 0 0 -1 42 27 4 -1 0 0 -1 35 22 2 -1 0 0 -1 33 18 3 -1 0 0 -1 35 17 4 -1 0 0 -1 26 13 2 -1 0 0 -1 37 15 2 -1 0 0 -1 36 14 1 -1 0 0 -1 63 20 3 -1 0 0 -1 41 8 1 ; data footshape; set foot_mv; array k{3}; do Shape = 1 to 3; count = k{Shape}; output; end; drop k:; run; /* Fit model */ proc glimmix data=footshape method=quad; class sire; model Shape = yr b1 b2 b3 / s link=cumprobit dist=multinomial; random int / sub=sire; /* save BLUEs (predmupa) and BLUPs (predmu) */ output out=glmm pred(noblup ilink)=predmupa pred(ilink)=predmu; freq count; run; /* Process BLUEs */ proc sort data=glmm out=blue nodupkeys; by yr b1 b2 b3 _level_; run; proc transpose data=blue out=blue_wide prefix=shape; by yr b1 b2 b3 ; id _level_; var predmupa; run; data blue_wide; set blue_wide; prob1 = shape1; prob2 = shape2 - shape1; prob3 = 1 - shape2; drop _name_ _label_; run; /* Process BLUPs */ proc sort data=glmm out=blup nodupkeys; by yr b1 b2 b3 sire _level_; run; proc transpose data=blup out=blup_wide prefix=shape; by yr b1 b2 b3 sire; id _level_; var predmu; run; data blup_wide; set blup_wide; prob1 = shape1; prob2 = shape2 - shape1; prob3 = 1 - shape2; drop _name_ _label_; run;   ... View more