computationally singular
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 at 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 at 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
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Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III