[R] Model (aov): CI of interaction

[Karl Knoblick]

Hallo!

I have the a model with 3 time points, 2 treatments
and N subjects. I can calculate an ANOVA but I can not
calculate the CI of the interaction term (time and
treatment), which I need for a closer look at the
effect of the treatment to the 3 time points. I do NOT
want to use lme because I can not manage it to
reproduce text book examples (see my posting [R] lme:
reproducing example Karl Knoblick (Tue 02 Dec 2003 -
21:34:54 EST)).

Here some sample data:

# Data
ID<-factor(rep(1:35,each=3)) # 35 subjects
TREAT<-factor(c(rep("A", 60), rep("B", 45)))
TIME<-factor(rep(1:3, 35))
Y<-numeric(length=105)
set.seed(1234)
Y<-rnorm(105)
Y[TREAT=="A" & TIME==2]<-Y[TREAT=="A" & TIME==2] + 1 #
want to see an effect!
DF<-data.frame(Y, ID, TREAT, TIME)

# 2 possible designs:
# Design 1 with random term
DF.aov1<-aov(Y ~ TIME*TREAT + Error(TREAT:ID),
data=DF)
summary(DF.aov1)
# Design 2 without random term
DF.aov2<-aov(Y ~ TIME*TREAT, data=DF)
summary(DF.aov2)

I am also not sure about the design - I think design 1
is more appropriate.

What I have tried is to calculate the CI of the
coefficients:
confint(DF.aov1[[2]])
confint(DF.aov1[[3]])

(or:
confint(DF.aov2)
)

But how can I get the CI for a concrete difference for
example between the treatments at time point 2?

I really hope, sombody can help!

Karl