Dear Steve, your response was a huge help. I'm not there yet but I think I finally have a grasp of what the final model might look like!! Working backwards -- I'm glad I am interpreting the AR(1) covariance parameter Wald test appropriately, and glad to put that issue aside for now. Your answer to my second question regarding interpreting an overall "sampling" effect to mean that there are differences among samplings overall, confirms that I have been working with an incorrectly coded model -- that is nonsensical in the framework of the actual experimental design. Your suggestions about recoding "sampling" are eye-opening, and I think I am finally getting closer to understanding. (hooray, and thank you). Samplings occurred every 3 months (once per season per year), and began when we enrolled subjects and ended when we reached 8 samplings, so clearly coding "sampling" as a fixed effect as I have been is incorrect. Based on your suggestion I created a new variable, "season_year". Each house was sampled up to 8 season_years, and the data set has 19 season_years, and 15 houses and 2305 real_locs, and a total of 11676 observations. I ran a number of models just including house, season, season_year (i.e., excluding environment for now, for simplicity and to try to make things run faster/run at all). What I found is that if I try to use a LaPlace method, I get the error "Integer overflow on computing amount of memory required", and SAS stops processing due to insufficient memory. If I try to use the Quadrature method, I get the error "Estimation by quadrature is available only if the data can be processed by subjects. Make sure that all G-side RANDOM statements have SUBJECT=effect. If there are multiple SUBJECT= effects they need to form a containment hierarchy, e.g., SUBJECT=A, SUJECT=A*B, SUBJECT=A(B), …" My current model has two lines: random int / subject=house; random year_season / subject=realloc type=AR(1); My eventual model, once I put environment back in will include: random int environment/ subject=house; random year_season / subject=realloc type=AR(1); I finally tried the R-side approach, and SAS ran for 6 hours before stopping with the warning: "Obtaining minimum variance quadratic unbiased estimates as starting values for the covariance parameters failed." So at this point, I am excited about having a model structure that makes intuitive sense given the way the data were collected and the questions I want to ask, but still need help getting the it implemented. Your feedback is very much appreciated! Susi
... View more