Hi Caroline,
I would never code it with proc glm. See Walter Stoup (2013) Generalized Linear Mixed Models.
PROC GLM just does not do a good job of analyzing repeated measures or split plot models.
The fact that the crop is not the same across all dates does present a problem in terms of estimability of means. There will be "missing" cells by design. To get around this, I would fit a cell means model, and use LSMESTIMATE statements to derive lower order means and comparisons.
proc glimmix;
class nem trt crop blk date;
model lgsprCysts100=nem*trt*crop*date/noint; /* This fits a cell means model */
random intercept nem|trt|crop/subject=blk;
random date/ residual subject=nem*trt*crop*blk type=arh(1); /* This may be problematical to fit, due to the small number of levels for date, and incomplete subjects. If so, try type=chol */
run;
The LSMESTIMATE statements will need to be added later. I also have a hunch that the dependent variable is a count variable, and rather than a log transform prior to analysis, a log link should be used so that the dependence of the variance on the mean can also be captured.
Steve Denham
Message was edited by: Steve Denham
Thanks a lot Steve!
I used to run my models (also split plot models) as a first run with proc glm in order to pool, so I can get a clean model when using proc mixed. In this case, I think I do not have a repeated measures, because not only the crops are different every year, but also the planting dates, so the circunstances differ every year.
Caroline
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