In an experiment, we evaluated the effect of decreasing or increasing the roughage content in feedlot diets: We have a standard diet with 12% roughage, one as a negative control with 7% roughage for the entire 100 days of confinement (D1 and D2), a diet that starts with 12% roughage for 50 days and then decreases to 7% in the last 50 days (D3), and the last diet that starts with 7% roughage for 50 days and then increases to 12% in the last 50 days (D4). The hypothesis of the study is that by increasing the roughage at the end of confinement, we increase feed intake and consequently the performance of beef cattle. I thought about analyzing this experiment as a 2x2 factorial (two diets with fixed roughage content and two changes in roughage content), or conducting orthogonal contrasts D1vsD2, D1vsD3, and D1vsD4. Or performing a response surface regression analysis over time, but I have no idea how to do that because I've never done it before. ... View more

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? ... View more