Hi Stephen:

It's the inputs given to me by a end-user. Ultimately trying to fit a student-t 
copula to a bunch of simulated price returns
while maintaining the structure of the estimated correlation matrix.

The other challenge is I use R to test and work a solution but then have also 
done in matlab as I had to write a  C# wrapper around the code for the end-user.

In a way I am stuck with Matalb but was hoping to try and solve with R which is 
great.

I appreciate your responding to my email help request.

Neil

________________________________
From: sten...@go.com [mailto:sten...@go.com]
Sent: Wednesday, April 01, 2009 10:47 AM
To: Gottlieb, Neil; r-help@r-project.org
Subject: Re: [R] Need Advice on Matrix Not Positive Semi-Definite with cholesky 
decomposition

Neil,

pls tell why do you need the correlation matrix? if you are trying to simulate 
correlated variables then you can go around the cholesky by using svd.
if you really need the correlation ( I think it is always possible to avoid it 
) then Rmetrics have a function to turn your matrix into positive semi-definite.
you can also consider a factor model which will reduce the dimensionality 
tremendously.

Stephen C. Bond



On Apr 1, 2009, ngottl...@marinercapital.com wrote:

Dear fellow R Users:

I am doing a Cholesky decomposition on a correlation matrix and get error 
message
the matrix is not semi-definite.

Does anyone know:
1- a work around to this issue?
2- Is there any approach to try and figure out what vector might be co-linear 
with another in thr Matrix?
3- any way to perturb the data to work around this?

Thanks for any suggestions.
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