similar to: R-alpha: bug in svd

--=====================_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

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)

I noticed that apply is VERY SLOW when applied to a "large" dimension as for example when computing the row sums of a matrix with thousands of rows. To demonstrate it, I did some benchmarking for different methods of computing the row sums of an nx10 matrix with n =3D 2000, ..., 10000. The first method (M1) I used is the normal apply command: y <- apply(x,1,sum) The second method

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

I have the following problem. I have some multidimensional data points "x" and a curve "fit" fitted to these points. How can I combine R> pairs(x) and R> pairs(fit,panel=lines) in one plot, so that I can see how good the curve fits the data? A command like R> pairs(x, panel=function(x,y) {points(x,y); lines(lowess(x,y))}) does not work, since I fit the curve in all

In S it is possible to "split" a matrix into its rows, using split(matrix, 1:number_of_rows). This is not possible in R. Example: R: R> split(matrix(rnorm(1:20),4, 5), 1:4) Error in split(x, as.factor(f)) : argument lengths differ S: > split(matrix(rnorm(1:20),4, 5), 1:4) $"1": [1] -0.1804794 0.5269439 0.6248224 -0.3243427 -1.2987407 $"2": [1] 0.9384254

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Andreas Weingessel <Andreas.Weingessel@ci.tuwien.ac.at> writes: > When I tried to port the statlib-Pkg rpart to R, I came across the > c-Function prlab which seems to be part of S+, but not of R. > > Does anyone know what prlab does and how difficult it would be to > emulate it in R? Can you show us the context? If I had to guess I would say it was a routine to print

(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]

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 <-

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

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-helpers, could anybody explain me briefly what is the difference between eigenvectors returned by 'eigen' and 'svd' functions and how they are related? Thanks in advance Ondrej Mikula

Dear All, I need to perform a SVD on a very large data matrix, of dimension ~ 500,000 x 1,000 , and I am looking for an efficient algorithm that can perform an approximate (partial) SVD to extract on the order of the top 50 right and left singular vectors. Would be very grateful for any advice on what R-packages are available to perform such a task, what the RAM requirement is, and indeed what

Hi, I met a probem recently and need your help. I would really appreciate it. I kept receiving the following error message when running a program: 'Error in svd(X) : infinite or missing values in x'. However, I did not use any svd function in this program though I did include the function pseudoinverse. Is the problem caused by doing pseudoinverse? Best regards, Tongtong

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

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

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

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,