similar to: Error La.svd(method="dgesvd") in R 2.4.0

Generator Microsoft Word 11 (filtered medium) Hi, I'm the mannova package maintainer. We used La.svd(X, method="dgesvd") in maanova package before. After R-2.3.0, the old La.svd() method was deprecated for option method="dgesvd". I changed maanova code correspondingly, which will call method="dgesdd" instead. But after that, we keep getting below error message

Per the discussion about the problems with prcomp() when n << p, which boils down to a problem with svd() when n << p, here is a patch to prcomp() which substitutes La.svd() instead of svd(). -Greg (This is really a feature enhancement, but submitted to R-bugs to make sure it doesn't get lost. ) *** R-1.6.0/src/library/mva/R/prcomp.R Mon Aug 13 17:41:50 2001 ---

(I tried to send this earlier, but it doesnt seem to have come through, due to problems on my system) Hola: Both cannot be correct: > m <- matrix(1:4, 2) > svd(m) $d [1] 5.4649857 0.3659662 $u [,1] [,2] [1,] -0.5760484 -0.8174156 [2,] -0.8174156 0.5760484 $v [,1] [,2] [1,] -0.4045536 0.9145143 [2,] -0.9145143 -0.4045536 > La.svd(m) $d [1]

Prof. Bates & R-devel, I've done more test with the following results: I have two versions of ATLAS 3.2.1. One was compiled on my old Thinkpad 600E (PII), the other was compiled on my new Thinkpad T22 (PIIISSE1). I compiled R-devel dated 10/31, 11/01 and 11/04, linked against either of the two ATLAS libs. All gave Dr. Watson when given this code: La.svd(matrix(runif(1e5), 1e3,

Dear R-devel, I had attempted to compile r-devel (dated Oct. 31, 2001) on WinNT with link to ATLAS, with mostly success. However, when I tried the following, I got a visit from Dr. Watson: R : Copyright 2001, The R Development Core Team Version 1.4.0 Under development (unstable) (2001-10-31) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under

Dear All, Under Debian GNU/Linux La.svd (with method = "dgesdd") sometimes gives the error "Error in La.svd(data, nu = 0, nv = min(nrow, ncol), method = "dgesdd") : LAPACK routine DGESDD gave error code -12" It seems not to depend on the data per se, but on the relationship between numbers of rows and columns. For example, if the number of columns is 100,

Dear Philipp, this is just a tentative answer because debugging is really not possible without a reproducible example (or, at a very bare minimum, the output from traceback()). Anyway, thank you for reporting this interesting numerical issue; I'll try to replicate some similar behaviour on a similarly dimensioned artificial dataset when I have some time (which might not be soon). As for now,

Lately I'm getting this error quite a bit: Error in La.svd(x, nu, nv) : error code 1 from Lapack routine 'dgesdd' I'm running R 2.5.0 on a 64 bit Intel machine running Fedora (8 I think). Maybe the 64 bit platform is more fragile about declaring convergence. I'm seeing way more of these errors than I ever have before. From R-Help I see that this issue comes up from time to

Error in La.svd(x, nu, nv) : error code 1 from Lapack routine ‘dgesdd’ what resources are there to track down errors like this [[alternative HTML version deleted]]

Dear R helpers, I am working with R 2.4.1 GUI 1.18 (4038) for MacOSX. I have a matrix of 10 000 genes and try to run the following commands: > model.mix<-makeModel (data=data, formula=~Dye+Array+Sample+Time, random=~Array+Sample) > anova.mix<-fitmaanova (data, model.mix) > test.mix<-matest (data, model=model.mix, term="Time", n.perm=100, test.method=c(1,0,1,1))

Hi, I'm just wondering if it is planned to include the Lapack routine DGESDD (and friends) in R-1.4.0? This is faster (supposedly by a factor of ~6 for large matrices) than DGESVD which is currently (R-1.3.1) called by La.svd. And if it is not in the plans yet, is there a chance it could be? I've added it to my local version of R-1.3.1 and so far see a factor of 4 improvement over

Dear R list users, the singluar value decomposition of a symmetric matrix M is UDV^(T), where U = V. La.svd(M) gives as output three elements: the diagonal of D and the two orthogonal matrices u and vt (which is already the transpose of v). I noticed that the transpose of vt is not exactly u. Why is that? thank you for your attention and your help Stefano AVVISO IMPORTANTE: Questo messaggio di

Hi, Over the past six months we have had a few problems with deprecation and Seth Falcon and I want to propose a few additions to the mechanism that will help deal with cases other than the deprecation of functions. In the last release one of the arguments to La.svd was deprecated, but the warning message was very unclear and suggested that in fact La.svd was deprecated. Adding a

After fixing princomp(), recently, {tiny negative eigen-values are possible for non-negative definite matrices} Fritz Leisch drew my attention to the fact the not only eigen() can be funny, but also svd(). Adrian Trappleti found that the singular values returned can be "-0" instead of "0". This will be a problem in something like sd <- svd(Mat) $ d

Hi, I'm trying to do PCA on a n by p wide matrix (n < p), and I'd like to get more principal components than there are rows. However, svd() only returns a V matrix of with n columns (instead of p) unless the argument nv=p is set (prcomp calls svd without setting it). Moreover, the eigenvalues returned are always min(n, p) instead of p, even if nv is set: > x <-

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi all, this is probably simple and I'm just doing something stupid, sorry about that :-) I'm trying to convert words (strings of letters) into a fairly small dimensional space (say 10, but anything between about 5 and 50 would be ok), which I will call a feature vector. The the distance between two words represents the similarity of the

Hello! I suppose this is more a matrix theory question than a question on R, but I will give it a try... I am using La.svd to compute the singular value decomposition (SVD) of a variance matrix, i.e., a symmetric nonnegative definite square matrix. Let S be my variance matrix, and S = U D V' be its SVD. In my numerical experiments I always got U = V. Is this necessarily the case? Or I might

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct

Hello List, i need help for eigen and svd functions. I have a non-symmetric square matrix. These matrix is not positive (some eigenvalues are negative). I want to diagonalise these matrix. So, I use svd and eigen and i compare the results. eigen give me the "good" eigenvalues (positive and negative). I compare with another software and the results are the same. BUT, when i use svd,

I am trying to perform Singular Value Decomposition (SVD) on a Term Document Matrix I created using the 'tm' package. Eventually I want to do a Latent Semantic Analysis (LSA). There are 5677 documents with 771 terms (the DTM is 771 x 5677). When I try to do the SVD, it runs out of memory. I am using a 12GB Dual core Machine with Windows XP and don't think I can increase the memory