Dear All:

I use lme function in the nlme R package to test if levels of factor items
has significant interaction with levels of factor condition. The factor
condition has two levels: Control and Treatment, and the factor items has 3
levels: E1,E2,E3. I use the following code:

f.lme.1 = lme(response ~ 0 + factor(condition) * factor(items), random =
~1|subject)

where subject is the random effect. In this way, when I run:
summary(f.lme.1)$tTable, I will get the following output:

factor(condition)Control
factor(condition)Treatment
factor(items)E2
factor(items)E3
factor(condition)Treatment:factor(items)E2
factor(condition)Treatment:factor(items)E3

together with Value, Std.Error, DF, t-value, p-value columns. I have two
questions:

(1) If I want to compare Control vs. Treatment, shall I follow the steps:
(a) run a smaller model

f.lme.0 =  lme(response ~ 0 + factor(condition) + factor(items), random =
~1|subject)

and (b) anova(f.lme.0, f.lme.1), and (c) if fail to reject, just use
estimable() function in gmodels and make a contrast of (-1,1,0,0) in the
smaller model?

(2) I am actually more interested in whether levels of items, i.e. E1, E2,
E3 are different across condition, i.e. whether the interaction terms are
significant (by just checking the p-value column?):

factor(condition)Treatment:factor(items)E2
factor(condition)Treatment:factor(items)E3

However, how can I tell if the first interaction term:

factor(condition)Treatment:factor(items)E1

is significant or not? This term is not shown in the summary output and I
think it has something to do with the contrast option used in R... How can
I get the p-values for all interactions directly? Thanks a lot!

Best,
Gu

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