[R] PROC MIXED vs. lme()

[Manuel A. Morales]

I'm trying to learn how to do a repeated measures ANOVA in R using lme().

A data set that comes from the book Design and Analysis has the following
structure: Measurements (DV) were taken on 8 subjects (SUB) with two
experimental levels (GROUP) at four times (TRIAL).

In SAS, I use the code:

PROC MIXED DATA=[data set below];
  CLASS sub group trial;
  MODEL dv = group trial group*trial;
  REPEATED trial / SUBJECT=sub TYPE=CS;
run;

which gives the results:

Tests of Fixed Effects

Source        NDF   DDF  Type III F  Pr > F
GROUP           1     6        2.51  0.1645
TRIAL           3    18       22.34  0.0001
GROUP*TRIAL     3    18        0.58  0.6380

In R, I'm trying the code:

results.cs <- lme(DV ~ factor(GROUP)*factor(TRIAL), data=[data set below],
random= ~factor(TRIAL)|SUB, correlation=corCompSymm() )
anova(results.cs)

which gives the results:

                            numDF denDF  F-value p-value
(Intercept)                     1    18 3383.953  <.0001
factor(GROUP)                   1     6    4.887  0.0691
factor(TRIAL)                   3    18  239.102  <.0001
factor(GROUP):factor(TRIAL)     3    18    1.283  0.3103

Why are these results different? I'm a newbie to R, have the book "Mixed
Effects Models in S and S-Plus", but can't seem to get this analysis to
work. Any suggestions?

Thanks!

Manuel

Data:
SUB	GROUP	DV	TRIAL
1	1	3	1
1	1	4	2
1	1	7	3
1	1	3	4
2	1	6	1
2	1	8	2
2	1	12	3
2	1	9	4
3	1	7	1
3	1	13	2
3	1	11	3
3	1	11	4
4	1	0	1
4	1	3	2
4	1	6	3
4	1	6	4
5	2	5	1
5	2	6	2
5	2	11	3
5	2	7	4
6	2	10	1
6	2	12	2
6	2	18	3
6	2	15	4
7	2	10	1
7	2	15	2
7	2	15	3
7	2	14	4
8	2	5	1
8	2	7	2
8	2	11	3
8	2	9	4