About data:
(1) Prepared 4 different diets for pig industry:
diet | Ingredient A | Ingredient B |
---|---|---|
A_low_B_low | Low | Low |
A_high_B_low | High | Low |
A_low_B_high | Low | High |
A_high_B_high | High | High |
(2) Twelve (12) group pigs were randomly assigned to one of the diets, so 3 group pigs per diet treat.
(3) Twelve group pigs' weight (total) was monitored for 30 days (daily total weight).
The following questions might be asked:
I have tried to build the flowing model, but I am not so sure it is correct, please help:
Title "proc mixture";
proc mixed data=pigs;
Class group ingredA ingredB Age ;
Model Weight =ingredA|ingredB;
random group;
repeated Age/type=cs subject=group;
run;
This is close, but does not address possible interactions between age and the ingredients. How about:
proc mixed data=pigs;
Class group ingredA ingredB Age ;
Model Weight =ingredA|ingredB|Age;
random group;
repeated Age/type=cs subject=group;
run;
There are several other covariance structures you may want to investigate. In particular, heterogeneous compound symmetry (type=csh), and, if the age variable is equally spaced in time, autoregressive structures such as ar(1) and arh(1).
In addition, if the interaction of Age with the dietary factors is significant (either two-way or three-way), when it comes time to compare marginal means using the lsmeans statement, you will want to look at the AT= option.
I would strongly recommend looking at Littel et al.'s SAS for Mixed Models, 2nd ed., which is a storehouse of information on repeated measures analysis of covariance.
Steve Denham
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