I know.
Let me re-phrase the question: How do I convert a non-positive definite
correlation matrix to a positive-definite correlation matrix in R? I don't
think cov2cor is relevant here.
Example:
print(corr.mat)
[,1] [,2] [,3] [,4]
[1,] 1.00 -0.95 -0.28 -0.64
[2,] -0.95 1.00 -0.81 -0.38
[3,] -0.28 -0.81 1.00 -0.11
[4,] -0.64 -0.38 -0.11 1.00
is.positive.definite(corr.mat)
[1] FALSE
make.positive.definite(corr.mat)
[,1] [,2] [,3] [,4]
[1,] 1.2105898 -0.7221551 -0.1246443 -0.4971036
[2,] -0.7221551 1.2465138 -0.6419150 -0.2253951
[3,] -0.1246443 -0.6419150 1.1146085 -0.0045829
[4,] -0.4971036 -0.2253951 -0.0045829 1.0969628
----- Original Message -----
You could use cov2cor() to convert from covariance matrix to
correlation matrix. If the correlation is >1, the matrix won't be
positive definite, so you can restandardize the matrix to get a pos
def correlation matrix.
Jeremy
On 21 October 2010 15:50, HAKAN DEMIRTAS <demir...@uic.edu> wrote:
Hi,
If a matrix is not positive definite, make.positive.definite() function in
corpcor library finds the nearest positive definite matrix by the method
proposed by Higham (1988).
However, when I deal with correlation matrices whose diagonals have to be
1 by definition, how do I do it? The above-mentioned function seem to mess
up the diagonal entries. [I haven't seen this complication, but obviously
all entries must remain in (-1,1) range after conversion.]
Any R tools to handle this?
I'd appreciate any help.
Hakan Demirtas
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Psychology Research Methods Wiki: www.researchmethodsinpsychology.com
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