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. 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 Universit?t Heidelberg Im Neuenheimer Feld 400 69120 Heidelberg Telefon: 06221 - 56 5196 Email: stefan.schadwinkel at med.uni-heidelberg.de
How do I compute interactions with anova.mlm ?
2 messages · Schadwinkel, Stefan, Peter Dalgaard
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 Universit?t Heidelberg Im Neuenheimer Feld 400 69120 Heidelberg Telefon: 06221 - 56 5196 Email: stefan.schadwinkel at med.uni-heidelberg.de
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