> A curious difference between SAS and R. I wonder if anyone can explain it. > > Basic idea: Split-plot design (Male = whole plot, Trt = Sub plot). Rep is random, > Rep*Male variance component is 0 and deleted. Heterogeneous variances - each Trt > has different variance. > > The model with only Male and Trt as fixed effects is the same in SAS and R. > > When I add Male:Trt interaction, the results of the tests of fixed effects are no > longer the same (comparing SAS and R) for Male, but are the same for Trt and > Male:Trt. > > Is my model specification incorrect? > > Kevin Wright. Details follow. > > > Model with Male, Trt as fixed effects > > proc mixed data=pollen ; > class Trt Rep Male; > model Yield = Male Trt; > random Rep; > repeated / group = Trt; > lsmeans Trt Male; > run; > > Type 3 Tests of Fixed Effects > > Num Den > Effect DF DF F Value Pr > F > > Male 1 47 3.64 0.0624 > Trt 9 47 3.80 0.0012 > > > > > pollen.hetero<-lme(Yield~Male+Trt,pollen,random=list(Rep=~1), > + weights=varIdent(form=~1|Trt)) > > > > anova(pollen.hetero) > numDF denDF F-value p-value > (Intercept) 1 47 14.613222 0.0004 > Male 1 47 3.640521 0.0625 > Trt 9 47 3.797328 0.0012 > > > > Now add Male:Trt interaction as a fixed effect. > > proc mixed data=pollen ; > class Trt Rep Male; > model Yield = Male Trt Male*Trt; > random Rep; > repeated / group = Trt; > lsmeans Trt Male; > run; > > > Type 3 Tests of Fixed Effects > > Num Den > Effect DF DF F Value Pr > F > > Male 1 38 0.39 0.5384 > Trt 9 38 8.40 <.0001 > Trt*Male 9 38 2.97 0.0090 > > > pollen.hetero<-lme(Yield~Male+Trt+Male:Trt,pollen,random=list(Rep=~1), > + weights=varIdent(form=~1|Trt)) > > > > anova(pollen.hetero) > numDF denDF F-value p-value > (Intercept) 1 38 27.007016 <.0001 > Male 1 38 8.431796 0.0061 > Trt 9 38 8.396913 <.0001 > Male:Trt 9 38 2.964672 0.0090 > > >
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