To follow up on John's advice:

Start with something like

my.mod <- lm(cbind(OpenR1, OpenR2, OpenR3) ~ AgeClass * Treatment, data=my.data)

then read ?Anova to see how to specify idata= and idesign= for the repeated factor.

hth,
-Michael

John Fox wrote:
Dear Ingo,

One approach would be to use the Anova() function in the car package. See
?Anova and in particular the O'Brien and Kaiser example, which is for a more
complicated repeated-measures design. If you want to get "type-III" tests
(as opposed to the default "type-II" tests), be careful with the contrast
coding for the between-subjects factors.

I hope this helps,
 John

--------------------------------
John Fox
Senator William McMaster Professor of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
web: socserv.mcmaster.ca/jfox


-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On
Behalf Of protonringe
Sent: December-13-09 11:29 AM
To: r-help@r-project.org
Subject: [R] Repeated Measures Analysis - GLM


Hello to the R world...

I have some problems regarding a GLM - repeated measures analysis.

I want to test overall differences between AgeClass and Treatment (between
subject) with OpenR1+OpenR2+OpenR3 (repeated measures, within subject).
The
table looks kind like this:

AgeClass        Treatment       OpenR1  OpenR2  OpenR3
1       1       0       0       12.63
1       1       12.67   3.83    45.67
1       1       38.46   65.38   75.21
1       1       14.46   0       17.96
1       2       27.83   47.33   66.38
1       2       15.75   0       10.21
1       2       43.96   41.04   51.88
1       2       52.96   55.54   41.58
1       3       43.13   71.25   82.71
1       3       0.25    18.46   27.04
1       3       0.79    21.75   68.38
2       1       0       0       0
2       1       0       0       0
2       1       1.17    18.75   45.67
2       1       0       0       0
2       1       0       0       49.42
2       2       2.13    0       26.63
2       2       0       8.13    23.88
2       2       2.25    0       0
2       2       30.96   25.71   10.92
2       3       33.33   30.71   16.63
2       3       0       20.04   14.88
2       3       24.96   0       3.88
.
.
.

I tried several things, for example this:

aov(?????~(OpenR1*OpenR2*OpenR3*AgeClass*Treatment)+Error(??????/(OpenR1*Ope
n
R2*OpenR3))+(AgeClass*Treatment))

I don't really know what response-variable to use, or what the
subject-variable is....

There is no problem to create the model with SPSS, but for my
Diploma-Thesis
in biology i want to do all the statistics with R...

Regards and many thanks in advance....

Ingo


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