Schadwinkel, Stefan skrev:
Hi,
I wish to compute multivariate test statistics for a within-subjects repeated
measures design with anova.mlm.
This works great if I only have two factors, but I don't know how to compute
interactions with more than two factors.
I suspect, I have to create a new grouping factor and then test with this
factor to get these interactions (as it is hinted in R News 2007/2),
but I don't really know how to use this approach.
Here is my current code:
Two Factors: fac1, fac2
mlmfit - lm(mydata~1)
mlmfit0 - update(mlmfit, ~0)
% test fac1, works, produces same output as SAS
anova(mlmfit, mlmfit0, M = ~ fac1 + fac2, X = ~ fac2, idata = idata, test =
Wilks)
% test fac1*fac2 interaction, also works, also the same output as SAS
anova(mlmfit, mlmfit0, X = ~ fac1 + fac2, idata = idata, test = Wilks)
Three Factors: fac1, fac2, fac3
mlmfit - lm(mydata~1)
mlmfit0 - update(mlmfit, ~0)
% test fac1, works, same as SAS
anova(mlmfit, mlmfit0, M = ~ fac1 + fac2 + fac3, X = ~ fac2 + fac3, idata =
idata, test = Wilks)
Now, I try to compute the interactions the same way, but this doesn't work:
% fac1*fac2
anova(mlmfit, mlmfit0, M = ~ fac1 + fac2 + fac3, X = ~ fac3, idata = idata,
test = Wilks)
% fac1*fac2*fac3
anova(mlmfit, mlmfit0, X = ~ fac1 + fac2 + fac3, idata = idata, test =
Wilks)
Both of these above differ quite much from the SAS output and I suspect, my
understanding of X and M is somewhat flawed.
I would be very happy, if someone could tell me how to compute the two
interactions above and an interaction of N factors in general.
You need to ensure that the difference between the X and M models is the
relevant interaction, so something like
M=~fac1*fac2*fac3
X=~fac1*fac2*fac3 - fac1:fac2:fac3
should test for fac1:fac2:fac3
If the within-subject design is fac1*fac2*fac3 with one observation per
cell (NB!), then you can omit M. X can also be written as
~fac1*fac2+fac2*fac3+fac1*fac3 or ~(fac1+fac2+fac3)^2.
For the next step, use, e.g.,
M=~fac1*fac2+fac2*fac3+fac1*fac3
X=~fac2*fac3+fac1*fac3
to test significance of fac1:fac2 (notice that the main effects are
still in X becaus of the meaning of the * operator in R).
I would also be interested in computing linear contrasts using the T matrix
and anova.mlm.
Thank you very much,
Stefan
--
Stefan Schadwinkel, Dipl.-Inf.
Neurologische Klinik
Sektion Biomagnetismus
Universität Heidelberg
Im Neuenheimer Feld 400
69120 Heidelberg
Telefon: 06221 - 56 5196
Email:[EMAIL PROTECTED]
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