Here is one possible way.
You can first generate a positive semi-definite matrix of covariances, cov,
and then convert it to the correlation matrix: cor[i,j] =
cov[i,j]/sqrt(cov[i,i]*cov[j,j]). You can use ?outer function to do it
efficiently.
In turn, the covariance matrix can, for example, be generated as
x <- matrix(rnorm(5*5), ncol=5)
cov <- t(x) %*% x
P.S. Note that non-negative determinant is a necessary, but not sufficient,
condition for a matrix to be a covariance matrix. You need a stronger
property of being semi-definite
-----Original Message-----
From: Eric Zivot [mailto:ezivot@u.washington.edu]
Sent: Monday, February 25, 2002 6:02 PM
To: S-News
Subject: [S] simulate correlation matrices
I am trying to simulate random 5 x 5 correlation matrices and I am getting
negative determinants for some of the correlation matrices. Apparently, my
method is not working. Does anyone have a reliable method for generating
valid arbitrary correlation matrices? thanks, ez
Eric Zivot
Associate Professor
Department of Economics
Box 353330
University of Washington
(206) 543-6715
http://faculty.washington.edu/ezivot
http://myprofile.cos.com/zivote47
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