similar to: svd() (Linpack) problems/bug for ill-conditioned matrices (PR#594)

Ordinary functions should not alter their inputs but in R-2.15.2 svd(LINPACK=TRUE,X) does. (It worked in 2.15.0 but not in 2.15.1 or 2.15.2 and became deprecated in 2.15.2.) > X <- matrix(c(1,2,3, 5,7,11, 13,17,19), 3, 3) > X [,1] [,2] [,3] [1,] 1 5 13 [2,] 2 7 17 [3,] 3 11 19 > svd(X, LINPACK=TRUE)$d [1] 31.9718214 2.3882717 0.3143114 Warning message:

Hello, Like QR decomposition, I am looking for decomposition to get U and V matrix of SVD decomposition (according to LINPACK, X = UDV'). Do you know if there is a function which could calculate this decomposition? Look forward to your reply, Haleh

--=====================_24736660==_ Content-Type: text/plain; charset="iso-8859-1"; format=flowed Content-Transfer-Encoding: quoted-printable SVD-Error on R 1.1.0 Windows 98 I get the following error applying svd on a positive definite matrix : > sk2 [,1] [,2] [,3] [,4] [,5] [1,] 1.0460139783 0.084356992 -2.810553e-04

I use R Version 0.60 Alpha (September 18, 1997) on a Linux Pentium (Debian 1.3) and on a Sparc-Sun-Solaris 2.5.=20 R> svd(matrix(1:16,4,4)) =09yields on both machines Error: error 4 in dsvdc R> svd(matrix(1:20,4,5)) =09gives a result on the Linux computer $d [1] 0 0 0 NA $u [,1] [,2] [,3] [,4] [1,] 1 0 0 0 [2,] 0 1 0 0 [3,] 0 0 1 0 [4,] 0

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

Thanks for all the helpful responses. I include the data file and the syntax file for reference. Again, if I use the fields function, as is, I get the message: Error in svd(tempM) : error 159 in dsvdc using traceback, I get: > traceback() 4: stop(paste("error ", z$info, " in dsvdc")) 3: svd(tempM) 2: Krig(x, Y, cov.function = rad.cov, m = m, decomp = decomp,

Hello, I was wondering whether anyone out there knows of the solution to a problem that I'm having with the Fields package. I am getting the error message when I try and run the fields function tps (thin plate splines). Namely, for two different sets of variables, I get: > bout <- Tps( bvolcap, bdsm) Error in svd(tempM) : error 159 in dsvdc > wout <- Tps( wvolcap, wdsm)

Hi, Jim: Could you please look at svd2.Rd and see what it says? It may give an example, where it gave a better answer than svd -- i.e., a marginal case, where svd2 honestly gave a better answer than svd. If we find -- either in svd2.Rd or in one of the revdepchecks -- an example where svd2 gives a demonstrably different answer, we need to consider what to do about that. 1.

Full_Name: Ravi Varadhan Version: 2.8.1 OS: Windows Submission from: (NULL) (162.129.251.19) Inverting a matrix with solve(), but using LAPACK=TRUE, gives erroneous results: Here is an example: hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, "+") } h5 <- hilbert(5) hinv1 <- solve(qr(h5)) hinv2 <- solve(qr(h5, LAPACK=TRUE)) all.equal(hinv1, hinv2) #

I just tried the rsync version of R-1.1.0 on one of my alphas: It compiles without problems (gcc/g77 2.95.2, system is DU4.0E) but make check stops in base-Ex.R at > X <- cbind(1, 1:7) > str(s <- svd(X)); D <- diag(s$d) List of 3 $ d: num [1:2] 12.07 1.16 $ u: num [1:7, 1:2] -0.0976 -0.1788 -0.2601 -0.3413 -0.4225 ... $ v: num [1:2, 1:2] -0.198 -0.980 -0.980 0.198 >

Greetings, compiling R 1.7.0 with gcc 3.1.1 on Debain Linux (woody stable) with the configure option --with-lapack works but make check fails in test base-R with the message [...] > kappa(x1 <- cbind(1,1:10))# 15.71 [1] 15.70590 > kappa(x1, exact = TRUE) # 13.68 [1] 13.67903 > kappa(x2 <- cbind(x1,2:11))# high! [x2 is singular!] [1] 8.351867e+16 > > hilbert

Dear R Developers, I should have had the Design library running in R by now but have kept putting off changing some calls to LINPACK routines to use those builtin to R. Specifically I call dqrsl1 and dqr. Would it be an easy task to put those in the next release of R? If not I'll finally bite the bullet and get back into reading LINPACK documentation (which I have but haven't examined

rvaradhan at jhmi.edu wrote: > Full_Name: Ravi Varadhan > Version: 2.8.1 > OS: Windows > Submission from: (NULL) (162.129.251.19) > > > Inverting a matrix with solve(), but using LAPACK=TRUE, gives erroneous > results: Thanks, but there seems to be a much easier fix. Inside coef.qr, we have coef[qr$pivot, ] <- .Call("qr_coef_real", qr, y, PACKAGE =

Refiling this. The actual fix was slightly more complicated. Will soon be committed to R-Patched (aka 2.9.1 beta). -p rvaradhan at jhmi.edu wrote: > Full_Name: Ravi Varadhan > Version: 2.8.1 > OS: Windows > Submission from: (NULL) (162.129.251.19) >=20 >=20 > Inverting a matrix with solve(), but using LAPACK=3DTRUE, gives erroneo= us > results: Thanks, but there seems

Hello, I'm an absolute beginner with R and neophite in data analysis, so please bear with me if I ask stupid question. I'm trying to do a correspondence analysis using R and MASS corresp function, but I get an error message which I'm unable to interpret: > data(weblog) > library(MASS) > corresp(~ url + fromurl, data=weblog) Error in svd(t(t(x1 * Dr) * Dc)) : error 306 in

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 ---

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 <-

Hi, I understand that the glm.fit calls LINPACK fortran routines instead of LAPACK because it can handle the 'rank deficiency problem'. If my data matrix is not rank deficient, would a glm.fit function which runs on LAPACK be faster? Would this be worthwhile to convert glm.fit to use LAPACK? Has anyone done this already?? What is the best way to do this? I'm looking at very

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

> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: > > F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); > if (*info == 0){ > F77_CALL(dpodi)(*hessian,