All statistical analyses are conducted in SAS 9.4 I have a 2x4 experimental design with Diet (4 increments of nutrient x, 0, 5, 10 and 15%) and infection (Infected vs control) as my independent factors and feed intake (FI) is the dependent variable . The replicate is pen (6 animals per pen, 6 pens per treatment combination) and I measure feed intake over 15 days. FI is analysed with the repeated measurements mixed procedure (PROC MIXED) Covariance structures were chosen based on the lowest value for the Akaike and Bayesian information criteria. Given this covariance structure, fixed effects were tested, and least square means along with pooled standard errors (SEM) are calculated using the LSMEANS and PDIFF-statements of PROC MIXED, respectively. For multiple comparisons I follow the Bonferroni adjustment. For all statistical procedures, the normality of the residuals are assessed with the Shapiro-Wilk test. Significance was determined at P < 0.05. All values are expressed as model-predicted least square means with the SEM. The code: proc mixed data=Coc; class diet infection day pen; model fi=diet infection day diet*infection diet*infection*day; repeated day/ subject=pen type=ANTE(1); lsmeans diet infection day diet*infection*day/ pdiff=all Adjust=Bon; run; 1st Question Should I insert pen as a random factor in the model (e.g Pen(diet)) or not. If not can you help with syntax? 2nd I am primarily interested in the 3-way interaction and pairwise comparisons only within the same day are of interest. Pdiff gives me all possible comparisons which are of no interest to me. Should I use a different approach e.g use the SLICE option or should I use a CONTRAST statement? 3rd Could you provide a bit of help with the coding according to the approach (es) I should follow?
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