SPM Statisticians,
Let's say I have an SPM model from which I calculate 14 contrasts. I
know that the contrasts are not all independent. Is it reasonable to
run principal components analysis on the 14 contrast vectors to
determine the number of independent contrasts? If so then I can use
that number to correct for multiple comparisons in the p value for
each contrast using bonferroni correction.
I realize this may be simplistic, but surely there IS a way to find
out how many independent contrasts are represented in my collection of
14? Has anyone seen a paper that uses this approach?
As a specific example, my fourteen contrasts are listed below, and
using the matlab princomp command I get 100% of the variance
represented by 7 latent variables, so I would use .05/7 for the p
value of each of the 14 contrasts.
Many Thanks!
Jim
matx(:,1) = [1 0 0 0 0 0 -1 0];
matx(:,2) = [0 0 0 0 1 0 -1 0];
matx(:,3) = [0 0 1 0 0 0 -1 0];
matx(:,4) = [1 0 -1 0 0 0 0 0];
matx(:,5) = [-1 0 1 0 0 0 0 0];
matx(:,6) = [1 0 0 0 -1 0 0 0];
matx(:,7) = [-1 0 0 0 1 0 0 0];
matx(:,8) = [0 0 1 0 -1 0 0 0];
matx(:,9) = [0 0 -1 0 1 0 0 0];
matx(:,10) = [0 -1 0 0 0 0 0 0];
matx(:,11) = [0 0 0 -1 0 0 0 0];
matx(:,12) = [0 0 0 0 0 -1 0 0];
matx(:,13) = [0 0 0 0 0 0 0 1];
matx(:,14) = [0 0 0 0 0 0 0 -1];
[coeff,score,latent]=princomp(matx);
disp(latent/sum(latent));
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