Hi, my new problem is: In our trail, we need to control a lot of effects, example: animal, mother this animal, father this animal, season of bron (3 months rang) and pen. We were using the 2x2 factorial array. Each treatments are composed for 2 factors for each animal. Our problem is that all this factors influence the response varible ex average gain daily (animal, mother this animal, father this animal, season of bron and pen). Before we use the model : PROC MIXED; CLASS mother father factor1 factor2 animal born_season pen time; model PV= factor1|factor2|time / DDFM=KR; RANDOM mother father animal born_season pen time; REPEATED time/ TYPE = ar(1) SUBJECT = animal(factor1|factor2); RUN; But now we want to isolate the effects of the factors 1 and 2, we think that for this we need to use the covariate effect, The model looks like this: PROC MIXED; CLASS mother father factor1 factor2 animal born_season pen time; model PV= factor1|factor2|time mother father animal born_season pen time / DDFM=KR; RANDOM animal; REPEATED time/ TYPE = ar(1) SUBJECT = animal(factor1|factor2); RUN; Do you agree with this approach?
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