Depending on what you want your covariance matrices to be like, you
could form
random symmetric matrices with positive diagonals and then use nearPD
{Matrix}
to make them positive definite, but the resulting distribution of
covariance
matrices would be hard to guess. And giving a diagonal matrix to
rwishart {bayesm}
results in random matrices with non-zero correlations, so it's not so
hard.
-- David
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Robin Hankin
Sent: Friday, September 26, 2008 5:10 AM
To: [EMAIL PROTECTED]
Cc: [EMAIL PROTECTED]
Subject: Re: [R] Generating a valid covariance matrix
Megh
corr.matrix() in the 'emulator' package can calculate
P-D variance matrices using any of a very broad
class of methods.
HTH
rksh
Megh Dal wrote:
> I want to generate a valid variance-covariance matrix. One way could
be to generate some random sample from multivariate normal distribution
and then calculate cov. matrix. Another way could be to sample from
wishart distribution itself. However both cases need a valid i.e. PD
covariance matrix. As I need to generate that covariance matrix only, I
am not interested those two methods. Can anyone suggest me some other
way out?
>
> Regards,
>
> ______________________________________________
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>
--
Robin K. S. Hankin
Senior Research Associate
Cambridge Centre for Climate Change Mitigation Research (4CMR)
Department of Land Economy
University of Cambridge
[EMAIL PROTECTED]
01223-764877
______________________________________________
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and provide commented, minimal, self-contained, reproducible code.
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
