Peter Dalgaard wrote:
The models I use for the anovas are the following:
aov(vecData ~ (facWithin + facBetweenROI + facBetweenCond)^2) aov(vecData ~ facBetweenROI + facBetweenCond %in% facWithin + Error(facBetweenROI %in% facWithin)) aov(vecData ~ facBetweenCond + facBetweenROI %in% facWithin + Error(facBetweenCond %in% facWithin))
SAS seems to calculate the Huynh-Feldt test for the first and the
second model. The SAS output is (for the second aov)
Would you happen to know what logic SAS uses to recognize cases where the corrections apply? I thought it only did it in the case of repeated measurements, as in
proc glm;
model bmc2-bmc7=bmc1 grp / nouni;
repeated time ...
Yes, of course, the entire SAS script is available to me:
PROC GLM;
MODEL col1 col3 col5 col7 col9 col11 col13 col15 col17 col19 col21 col23 =/nouni;
repeated roi 6, ord 2/nom mean;
TITLE 'ABDERUS lat ACC 300-500';
This script wasn't written by me and unfortunately, I don't know anything about SAS scripting, which makes it hard for me to follow the script.
http://www.psych.upenn.edu/~baron/rpsych.pdf
Thanks for that link, it seems like a quite useful paper!
Now, I've tried to follow it and apply the steps to my own problem, but I came up with a H-F and G-G value that is still slightly different from the one that SAS calculates (I'll attach my code at the end of this mail). With my old code (see my last email), I get 0.4682799. With the new code, I get 0.4494588 as G-G epsilon and 0.6063626 for the H-F correction. I think that this corresponds (or should do so, rather) to the SAS value of 0.6333.
Now, as you can see, the differences are small, but still there.
The main difference between my two versions of the code are that in the old code (based on that book), a covariance matrix is calculcated for every condition and then these are averaged for the covariance matrix that is used in the H-F formula. The new code, on the other hand, averages the data over all conditions and then calculcates the covariance matrix for that, which could probably explain the differences. There also seems to be a difference between the original H-F correction and the algorithm that SAS uses; this is mentioned in the PDF, but they don't explain it any further. I suspect that this difference could be exactly the difference between my two code versions, but I don't know for sure.
Now, with the difference between my value and the one from SAS being so small, I suspect that there's only a very slight difference between the algorithms. Do you have any hints what these could be, or how I could go about investigating it?
Thanks a lot for your help!
Bela Bauer
mtx <- NULL
for (iROI in 1:length(unique( roi )) ) {
for (iSubj in 1:length(unique (subj )) ) {
mtx <- c(mtx,
mean(asa[subj==unique(subj)[iSubj] & roi==unique(roi)[iROI]],
aoa[subj==unique(subj)[iSubj] & roi==unique(roi)[iROI]]))
}
}
mtx <- matrix(mtx,ncol=length(unique( roi )),byrow=F)S <- var(mtx)
k <- 6 D <- (k^2 * (mean(S) - mean(diag(S)))^2) N1 <- sum(S^2) N2 <- 2 * k * sum(apply(S, 1, mean)^2) N3 <- k^2 * mean(S)^2 epsiGG <- D / ((k - 1) * (N1 - N2 + N3)) epsiHF <- (10 * (k-1) * epsiGG - 2) / ((k-1) * ((10-1) - (k-1)*epsiGG)) print(epsiGG) print(epsiHF)
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