Dear R users:
I have some difficulties analizing data with mixed effects NLME and the
last version of R. More concretely, I have a repeated measures design with
a single group and 2 experimental factors (say A and B) and my interest is
to compare additive and nonadditive models.
suj rv A B
1 s1 4 a1 b1
2 s1 5 a1 b2
3 s1 7 a1 b3
4 s1 1 a2 b1
5 s1 4 a2 b2
6 s1 2 a2 b3
7 s2 6 a1 b1
8 s2 8 a1 b2
9 s2 10 a1 b3
10 s2 3 a2 b1
11 s2 6 a2 b2
12 s2 6 a2 b3
13 s3 1 a1 b1
14 s3 6 a1 b2
15 s3 5 a1 b3
16 s3 3 a2 b1
17 s3 5 a2 b2
18 s3 4 a2 b3
19 s4 2 a1 b1
20 s4 10 a1 b2
21 s4 12 a1 b3
22 s4 1 a2 b1
23 s4 4 a2 b2
24 s4 7 a2 b3
25 s5 5 a1 b1
26 s5 10 a1 b2
27 s5 10 a1 b3
28 s5 5 a2 b1
29 s5 6 a2 b2
30 s5 5 a2 b3
31 s6 1 a1 b1
32 s6 7 a1 b2
33 s6 8 a1 b3
34 s6 2 a2 b1
35 s6 8 a2 b2
36 s6 7 a2 b3
It is very easy to fit these data with base R function AOV:
NonAdditive model:
aov(rv ~ A*B + Error(suj+suj/A+suj/B)
Additive model:
aov(rv ~ A*B + Error(suj)
and also easy with SAS MIXED (I missed some obvious lines):
NonAdditive model
model vr = A B A*B;
random suj A*suj B*suj;
repeated / type=cs subj=suj;
Additive model;
model vr = A B A*B /ddfm=satterth;
repeated / type=cs subj=suj;
Using LME I do not find any problems to fit the additive model with
lme(vr~A*B, random=~1|suj, cor=corCompSymm())
but I have found some difficulties fitting the nonadditive model.
Can anyone help me?
Thanks in advance.
Manuel Ato
Dpto. Psic.Básica y Metodología
Apartado 4021
30080 MURCIA (Spain)
e-mail: [EMAIL PROTECTED]
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