In order to treat day as a repeated effect, you need to include it in the model statement. What happens with something like:
ods graphics on;
PROC mixed DATA=test method=reml plots(maxpoints=none);
Class subject day (ref=first) hour (ref=first);
MODEL prot_day = proportion_protein|hour|day / solution ddfm=kr ;
Random prot_hour /type=un subject=subject v vcorr;
Repeated day / type=un subject=hour(subject) r rcorr; lsmeans proportion_protein|hour|day/cl;
run;
ods graphics off;
Not quite sure about this, because I think you will have a LOT of values for prot_hour, and fitting an unstructured covariance structure may lead to convergence or estimation problems.
Also, this approach assumes that hour is not a repeated measure on a subject, which is probably not a good assumption. You may need to recast day and hour into a single time variable, if you want to fully capture the repeated nature of the design. Alternatively, you might consider day as a random effect, and look at marginal effects over day for proportion protein and hour.
Steve Denham
... View more