similar to: R-devel & ATLAS generates Dr. Watson on NT (was RE: Look, Wa tson! La.svd & ATLAS)

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

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 ---

On two laptops running 32-bit kubuntu, I have found that svd(), invoked within R 2.9.1 as supplied with the current ubuntu package, returns very incorrect results when presented with complex-valued input. One of the laptops is a Dell D620, the other a MacBook Pro. I've also verified the problem on a 32-bit desktop. On these same systems, R compiled from source provides apparently

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

(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]

I have just installed R 2.4.0 (Windows XP OS) and have tried to run code that had worked with previous versions of R. However, I now get an error message: Error in La.svd(iCmat, method = "dgesvd") : unused argument(s) (method = "dgesvd") The R NEWS page about the release and issue 6/4 of the newsletter states: La.svd(method = "dgesvd") is defunct. As a

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,

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))

Hola a todos, me ha gustado mucho la solución de Carlos, muy eficiente y muy ingeniosa al utilizar la funcion col() que o no la conocia o no me acordaba de ella. La parte mas "lenta" sigue siendo el apply que en el fondo no es mas que un ciclo for a traves de las filas, asi que inspirado por el metodo de Carlos pense que podria ser mas rapido si iteramos a traves de las columnas por lo

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

From: Prof Brian Ripley [mailto:ripley@stats.ox.ac.uk] [...] > Also, AFAIK no one is working on the Windows port at present, so the build > of R-devel is only tested irregularly. I expect some work will be done on > it once 1.4.0 goes into feature freeze, but can't guarantee even that. I had been trying to find out why La.svd on moderately large matrices (say 500 x 100) crash

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 <-

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,

-----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