Hi all,
I'm using R 2.0.1. for Windows to analyze the influence of following factors
on response Y:
A (four levels)
B (three levels)
C (two levels)
D (29 levels) with
E (four replicates)
The dataset looks like this:
A B C D E Y
0 1 1 1 1 491.9
0 1 1 1 2 618.7
0 1 1 1 3 448.2
0 1 1 1 4 632.9
250 1 1 1 1 92.4
250 1 1 1 2 117
250 1 1 1 3 35.5
250 1 1 1 4 102.7
500 1 1 1 1 47
500 1 1 1 2 57.4
500 1 1 1 3 6.5
500 1 1 1 4 50.9
1000 1 1 1 1 0.7
1000 1 1 1 2 6.2
1000 1 1 1 3 0.5
1000 1 1 1 4 1.1
0 2 2 2 1 6
0 2 2 2 2 4.2
0 2 2 2 3 20.3
0 2 2 2 4 3.5
250 2 2 2 1 8.4
250 2 2 2 2 2.8
etc.
If I ask the following: summary(aov(Y~A+B+C+D+E))
R gives me this answer:
Df Sum Sq Mean Sq F value Pr(>F)
A 3 135.602 45.201 310.2166 <2e-16 ***
B 2 0.553 0.276 1.8976 0.1512
C 1 0.281 0.281 1.9264 0.1659
D 25 92.848 3.714 25.4890 <2e-16 ***
E 3 0.231 0.077 0.5279 0.6634
Residuals 411 59.885 0.146
Can someone explain me why factor C has only 25 Df (in stead of 28, what I
expected), and why this number changes when I leave out factors B or C (but
not A)? Why do factors B and C (but again: not A) not show up in the
calculation if they appear later in the formula than D?
When I ask summary.lm(aov(Y~A+B+C+D+E)), R tells me that three levels of D
were not defined because of "singularities" (what does this word mean?).
After checking and playing around with the dataset, I find no logical reason
for which levels are not defined. Even if I construct a "perfect" dataset
(balanced, no missing values) I never get the correct number of Df.
My other datasets are analyzed as expected using the similar function calls
and similar datasets. Am I doing something wrong here?
Many thanks,
Ren� Eschen.
___
drs. Ren� Eschen
CABI Bioscience Switzerland Centre
1 Rue des Grillons
CH-2800 Del�mont
Switzerland
+41 32 421 48 87 (Direct)
+41 32 421 48 70 (Secretary)
+41 32 421 48 71 (Fax)
http://www.unifr.ch/biol/ecology/muellerschaerer/group/eschen/eschen.html
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