@mthorne wrote: I am working with a dataset from an agricultural weed management study. The data are counts per sq. meter. There are 2 study sites, 6 treatments, 4 replications per treatment, and 2 subsamples per plot. Plot is the experimental unit and I am considering study site as a random effect. This is a 6-year trial but I have already determined that I need to evaluate each year separately because of an interaction with year sequence and treatment. Using a negative binomial distribution doesn't always work because of zeros resulting from 100% effectiveness of the treatments. This is the model that seems to work the best and produce dimensions that seem realistic. ods graphics on; proc glimmix data=ltf method=laplace plots=studentpanel; by seq; class site seq trt sub rep; tctm2 = sqrt(ctm2); model tctm2 = trt / dist=normal link=identity; random intercept / subject=rep(sub); lsmeans trt / ilink diff lines plot=meanplot; run; quit; ods graphics off; It produces this structure. (site=field site, seq=year, trt=treatment, sub=subplot, rep=replication) Class Level InformationClass Levels ValuesSiteSeqTrtsubRep 2 ED ST 1 6 6 1 2 3 4 5 6 2 1 2 4 1 2 3 4 Number of Observations ReadNumber of Observations Used 96 96 DimensionsG-side Cov. ParametersR-side Cov. ParametersColumns in XColumns in Z per SubjectSubjects (Blocks in V)Max Obs per Subject 1 1 7 1 8 12 If I try to add "site" as a random effect, the model structure seems all wrong, as follows. ods graphics on; proc glimmix data=ltf method=laplace plots=studentpanel; by seq; class site seq trt sub rep; tctm2 = sqrt(ctm2); model tctm2 = trt / dist=normal link=identity; random intercept / subject=site; random intercept / subject=rep(sub); lsmeans trt / ilink diff lines plot=meanplot; run; quit; ods graphics off; Class Level InformationClass Levels ValuesSiteSeqTrtsubRep 2 ED ST 1 6 6 1 2 3 4 5 6 2 1 2 4 1 2 3 4 Number of Observations ReadNumber of Observations Used 96 96 DimensionsG-side Cov. ParametersR-side Cov. ParametersColumns in XColumns in ZSubjects (Blocks in V)Max Obs per Subject 2 1 7 10 1 96 The number of subjects is wrong, and the number of obs per subject is the total number of obs. Any thoughts on this analysis? Thanks! To properly include site as a random effect in your GLIMMIX model while keeping the structure correct, you can try nesting rep within site, rather than adding a second intercept statement. Here’s a revised model: sas Copy code ods graphics on; proc glimmix data=ltf method=laplace plots=studentpanel; by seq; class site seq trt sub rep; tctm2 = sqrt(ctm2); model tctm2 = trt / dist=normal link=identity; random intercept / subject=site; random intercept / subject=rep(site*sub); lsmeans trt / ilink diff lines plot=meanplot; run; quit; ods graphics off; This way, you should see more realistic subject structure and parameter estimates. Let me know if this helps!
... View more
