Hello, I observed ant colony formation under different conditions. Every week I counted the number of eggs, number of larvae, number of pupae and number of workers untile the colony reach a set number of workers. I have 3 types of condition for which I have replicates (colony ID). As the number of each variable I measured can be influenced by the number of the others, I want to do a multivariate GLMM with the four dependent variables. However, I do not find how to do it using GLIMMIX. Is it possible? the data are like colony_ID  condition  week  egg_ng  larvae_nb pupae_nb  worker_nb        1               A            1         0              0               0                 0        1               A            2         2              0               0                 0        1               A            3         3              1               0                 0        2               A            1         0              0               0                 0        2               A            2         4              3               0                 0  . . .        70              C            62        10          15           12                8     Thanks for your help. ... View more

I am analyzing growth data to determine if sibling competition affect growth using a GLIMMIX with different class and continuous variables and 2 random effects. I want to predict the out of sample data. I appended the dataset with missing values for the data I want to predict. It seems to be working but how do I get the standard error and the 95% confidence limits for the predicted data. Thanks for your help! proc GLIMMIX data=WORK.stdizedata namelen=200 method=MSPL scoring=3 IC=Q EXPHESSIAN PLOTS=all; class hatch_year hatch_area rear_area rear_nest_trt home_or_away hatch_nest_breed_ID rear_nest_breed_ID ; model SMI_robust = hatch_year hatch_area rear_area rear_nest_trt home_or_away d14_rear_nest_brood_size rear_Cs_at_start_of_rearing net_rearing_manipulation Date_of_day14 Date_of_day14*hatch_year hatch_year*hatch_area hatch_year*rear_area hatch_year*rear_nest_trt hatch_year*home_or_away hatch_area*rear_area hatch_area*rear_nest_trt rear_area*rear_nest_trt d14_rear_nest_brood_size*rear_Cs_at_start_of_rearing rear_Cs_at_start_of_rearing*net_rearing_manipulation / dist=gamma DDFM=KENWARDROGER solution; nloptions maxiter=15000 technique=NRRIDG; random intercept / subject=hatch_nest_breed_ID type=UN(1); random intercept / subject=rear_nest_breed_ID type=UN(1); output out=pred pred=xbeta pred(ilink)=predprob pred(ilink noblup)=fix_predprob; run; ... View more