[R] lme() F-values disagree with aov()

[i.m.s.white]

Your lme statement is OK. To get the usual split-plot anova, your aov
statement should be

fit2 <- aov(y ~ a*b*c + Error(s), data = d)

This gives the same F-values as lme.


On Mon, Aug 14, 2006 at 10:27:19AM -0400, William Simpson wrote:
> I have used lme() on data from a between-within subjects experiment. The correct
> ANOVA table is known because this is a textbook example (Experimental Design by
> Roger Kirk Chapter 12: Split-Plot Factorial Design). The lme() F-values differ from
> the known results. Please help me understand why.
> 
> d<-read.table("kirkspf2.dat",header=TRUE)
> for(j in 1:4) d[,j] <- factor(d[,j])  ### Make vars into type "factor"
> 
> ##lme() results
> library(nlme)
> fit<-lme(y~a*b*c,random=~1|s, data=d)
> anova(fit)
> 
> ##correct anova table
> ##subjects are nested within a; a between, b & c within
> fit2<-aov(y ~ a*b*c + Error(s/(c*b)), data=d)
> summary(fit2)
> 
> I suspect I need a different random=... statement in lme().
> Thanks very much for any help
> Bill
> 
> The data file is attached -- kirkspf2.dat
> Here it is again:
> 
> s a c b y
> 1 1 1 1 3
> 1 1 1 2 7
> 1 1 2 1 4
> 1 1 2 2 7
> 2 1 1 1 6
> 2 1 1 2 8
> 2 1 2 1 5
> 2 1 2 2 8
> 3 1 1 1 3
> 3 1 1 2 7
> 3 1 2 1 4
> 3 1 2 2 9
> 4 1 1 1 3
> 4 1 1 2 6
> 4 1 2 1 3
> 4 1 2 2 8
> 5 2 1 1 1
> 5 2 1 2 5
> 5 2 2 1 2
> 5 2 2 2 10
> 6 2 1 1 2
> 6 2 1 2 6
> 6 2 2 1 3
> 6 2 2 2 10
> 7 2 1 1 2
> 7 2 1 2 5
> 7 2 2 1 4
> 7 2 2 2 9
> 8 2 1 1 2
> 8 2 1 2 6
> 8 2 2 1 3
> 8 2 2 2 11

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