If you have the data that created the correlation matrices, then you can do a permutation test.
The first question to ask is, "What does 'different' mean?" Some choices include:
max(abs(cor1 - cor2))
max(abs(eigen(cor1 - cor2)$values))
Once you have decided what metric makes most sense, you can perform the test. First pool all of the data (if you think that there are different means in the two conditions, then remove the means within each group before pooling). Then perform a number of random allocations of the pooled data into two groups with the number in each group equal to the original numbers. The test compares your statistic using the original correlation matrices to the distribution of statistics of the correlations from the random allocations.
Patrick Burns
Burns Statistics [EMAIL PROTECTED] +44 (0)20 8525 0696 http://www.burns-stat.com (home of S Poetry and "A Guide for the Unwilling S User")
michael watson (IAH-C) wrote:
Hi
Now a more theoretical question. I have two correlation matrices - one of a set of variables under a particular condition, the other of the same set of variables under a different condition. Is there a statistical test I can use to see if these correlation matrices are "different"?
Thanks Mick
______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
