From: Weiwei Shi <helprhelp_at_gmail.com>

Date: Thu 11 Aug 2005 - 12:24:24 EST

Date: Thu 11 Aug 2005 - 12:24:24 EST

PCA definately is worth of trying, which was my second thought. But thanks for the help and also on the suggestion.

On 8/10/05, Kjetil Brinchmann Halvorsen <kjetil@acelerate.com> wrote:

> Weiwei Shi wrote:

*>
**> >I think the problem might be caused two variables are very correlated.
**> >Should I check the cov matrix and try to delete some?
**> >But i am just not quite sure of your reply. Could you detail it with some steps?
**> >
**> >thanks,
**> >
**> >
**> Why not do principal component analysis? To identify the zero variance
**> linear combination(s) look at the nzero eigenvalues. Also, it *might*
**> make sense
**> to calculate a " mahalanobis" distance replacing the matrix inverse with a
**> pseudoinverse.
**>
**> Kjetil
**>
**>
**> >weiwei
**> >
**> >On 8/8/05, Christian Hennig <chrish@stats.ucl.ac.uk> wrote:
**> >
**> >
**> >>Once I had a situation where the reason was that the variables were
**> >>scaled to extremely different magnitudes. 1e-25 is a *very* small number
**> >>but still there is some probability that it may help to look up standard
**> >>deviations and to multiply the
**> >>variable with the smallest st.dev. with 1e20 or something.
**> >>
**> >>Best,
**> >>Christian
**> >>
**> >>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
**> >>>
**> >>>______________________________________________
**> >>>R-help@stat.math.ethz.ch mailing list
**> >>>https://stat.ethz.ch/mailman/listinfo/r-help
**> >>>PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
**> >>>
**> >>>
**> >>>
**> >>*** NEW ADDRESS! ***
**> >>Christian Hennig
**> >>University College London, Department of Statistical Science
**> >>Gower St., London WC1E 6BT, phone +44 207 679 1698
**> >>chrish@stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche
**> >>
**> >>
**> >>
**> >
**> >
**> >
**> >
**>
**>
**> --
**>
**> Kjetil Halvorsen.
**>
**> Peace is the most effective weapon of mass construction.
**> -- Mahdi Elmandjra
**>
**>
**>
**>
**>
**> --
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*

-- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Thu Aug 11 12:29:15 2005

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