I feel like I had pretty good luck with the following code:
proc glimmix data=tab1 noprofile;
class gr experiment;
model var1 = gr experiment/ddfm=contain;
random experiment/residual type=ar(1) subject=gr; lsmeans gr experiment/diff=control;
run;
You cannot fit this as a full factorial model as it is completely saturated.
Another approach would be to fit a GEE model. I used PROC GEE (SAS/STAT 15.2) to have access to the Type3 option for testing the model effects.
proc gee data=tab1;
class gr experiment;
model var1 = gr experiment/type3 ;
repeated subject=gr /type=un;
lsmeans gr experiment/diff=control;
run;
The results are different, but reflect the different approaches of the two procedures. In GLIMMIX, the lsmeans and tests are conditional (subject specific) while for GEE they are marginal (population averaged). Both are appropriate, but the choice depends on your research question.
SteveDenham
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