From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>

Date: Sun, 9 Dec 2007 06:43:43 +0000 (GMT)

Date: Sun, 9 Dec 2007 06:43:43 +0000 (GMT)

Hmm, S'S is numerically singular. crossprod(S) would be a better way to compute it than crossprod(S,S) (it does use a different algorithm), but look at the singular values of S, which I suspect will show that S is numerically singular.

On Sun, 9 Dec 2007, maj_at_stats.waikato.ac.nz wrote:

> I thought I would have another try at explaining my problem. I think that

*> last time I may have buried it in irrelevant detail.
**>
**> This output should explain my dilemma:
**>
**>> dim(S)
**> [1] 1455 269
**>> summary(as.vector(S))
**> Min. 1st Qu. Median Mean 3rd Qu. Max.
**> -1.160e+04 0.000e+00 0.000e+00 -4.132e-08 0.000e+00 8.636e+03
**>> sum(as.vector(S)==0)/(1455*269)
**> [1] 0.8451794
**> # S is a large moderately sparse matrix with some large elements
**>> SS <- crossprod(S,S)
**>> (eigen(SS,only.values = TRUE)$values)[250:269]
**> [1] 9.264883e+04 5.819672e+04 5.695073e+04 1.948626e+04 1.500891e+04
**> [6] 1.177034e+04 9.696327e+03 8.037049e+03 7.134058e+03 1.316449e-07
**> [11] 9.077244e-08 6.417276e-08 5.046411e-08 1.998775e-08 -1.268081e-09
**> [16] -3.140881e-08 -4.478184e-08 -5.370730e-08 -8.507492e-08 -9.496699e-08
**> # S'S fails to be non-negative definite.
**>
**> I can't show you how to produce S easily but below I attempt at a
**> reproducible version of the problem:
**>
**>> set.seed(091207)
**>> X <- runif(1455*269,-1e4,1e4)
**>> p <- rbinom(1455*269,1,0.845)
**>> Y <- p*X
**>> dim(Y) <- c(1455,269)
**>> YY <- crossprod(Y,Y)
**>> (eigen(YY,only.values = TRUE)$values)[250:269]
**> [1] 17951634238 17928076223 17725528630 17647734206 17218470634 16947982383
**> [7] 16728385887 16569501198 16498812174 16211312750 16127786747 16006841514
**> [13] 15641955527 15472400630 15433931889 15083894866 14794357643 14586969350
**> [19] 14297854542 13986819627
**> # No sign of negative eigenvalues; phenomenon must be due
**> # to special structure of S.
**> # S is a matrix of empirical parameter scores at an approximate
**> # mle for a model with 269 paramters fitted to 1455 observations.
**> # Thus, for example, its column sums are approximately zero:
**>> summary(apply(S,2,sum))
**> Min. 1st Qu. Median Mean 3rd Qu. Max.
**> -1.148e-03 -2.227e-04 -7.496e-06 -6.011e-05 7.967e-05 8.254e-04
**>
**> I'm starting to think that it may not be a good idea to attempt to compute
**> large information matrices and their determinants!
**>
**> Murray Jorgensen
**>
**> ______________________________________________
**> R-help_at_r-project.org mailing list
**> https://stat.ethz.ch/mailman/listinfo/r-help
**> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
**> and provide commented, minimal, self-contained, reproducible code.
**>
*

-- Brian D. Ripley, ripley_at_stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help_at_r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.Received on Sun 09 Dec 2007 - 06:52:07 GMT

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