Hi Everyone, I ran across and interesting issue in Proc Mixed. First, my data are doubly longitudinal. I have multiple behavioral measures for participants, nested within groups, at four different time periods. My goal for the initial analysis is to compute the "Latent Group Model" (Gonzalez & Griffin, 2002), which essentially estimates covariances at both group and individual levels of analysis. One repeated measure is a breeze, but 2 present a problem. After some head scratching and web searching, I found a solution. Here's the code: proc mixed method=reml lognote maxiter=500 covtest nobound data=scale1 noitprint noclprint; class group_id ami_ID trial id_var; model DV = trial|id_var / noint ddfm=satterth; random trial*id_var / sub= group_id type=un g; repeated trial*id_var / sub= ami_ID(group_id) type=un r; run; Pretty simple--the trial*id_var interaction is the random effect at the group level and the repeated effect at the individual level. "Trial" is the repeated measure across time and "id_var" identifies each of the four behavioral measures within time. And "group_id" and "ami_ID" are the group and individual-level nesting variables, respectively. Output is attached (with reduced data--2 time periods and 2 behavioral measures within time). Notice the R matrix, especially row 2, column 2, then compare that to what ought to be the corresponding estimate, UN(2,2), in the "Covariance Parameter Estimates" section--that estimate is actually UN(3,3). (I've circled those items in the screenshot.) It seems the R matrix does not preserve matrix order, or least doesn't use the same ordering. The G matrix lines up fine with the covariance parameter estimates section. Note I've also attached a print out of the ODS R covariance matrix. Any thoughts? BTW, I'm using 9.4 on a Linux system. Thanks in advance. Joe
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