Re: [R] (more) computationally singular

From: Christian Hennig <>
Date: Tue 09 Aug 2005 - 03:23:43 EST

More ideas:

You can also perform an Eigenvalue decomposition of the covariance matrix and see along which
directions the singularity occurs and how strong it is. Consequences could be: rescaling (or omission) of variables that are strong in these
directions, taking principal components, or linear transformation of the whole data in order to attain less extreme ratios between cov eigenvalues.

Generally I would say that information reduction (principal components or leaving out variables) should only be done if "small variance along a direction" means that "this direction is not important" in terms of the subject matter problem. Otherwise transformation could help. (Perhaps my guess was wrong in the first mail, you don't have to multiply something by 1e20 to repair a 1e-25 condition number and a more moderate transformation suffices.)


On Mon, 8 Aug 2005, Weiwei Shi wrote:

> Hi,
> I have a dataset which has around 138 variables and 30,000 cases. I am
> trying to calculate a mahalanobis distance matrix for them and my
> procedure is like this:
> Suppose my data is stored in mymatrix
> > S<-cov(mymatrix) # this is fine
> > D<-sapply(1:nrow(mymatrix), function(i) mahalanobis(mymatrix, mymatrix[i,], S))
> Error in solve.default(cov, ...) : system is computationally singular:
> reciprocal condition number = 1.09501e-25
> I understand the error message but I don't know how to trace down
> which variables caused this so that I can "sacrifice" them if there
> are not a lot. Again, not sure if it is due to some variables and not
> sure if dropping variables is a good idea either.
> Thanks for help,
> weiwei
> --
> Weiwei Shi, Ph.D
> "Did you always know?"
> "No, I did not. But I believed..."
> ---Matrix III
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> mailing list PLEASE do read the posting guide! Received on Tue Aug 09 03:29:22 2005

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