similar to: eigen of matrix of Inf hangs (PR#5406)

Full_Name: Sundar Dorai-Raj Version: 1.8.1 OS: Windows 2000 Professional Submission from: (NULL) (12.64.199.173) I discovered this problem when trying to use princomp in package:mva when a column in my matrix was all zeros and I set cor = TRUE (thus division by 0). Doing so hangs R, never to return. I have to shut down Rterm in the Task Manager and lose all work from the current image. I tracked

Hi, I am using eigen to get an eigen decomposition of a square, symmetric matrix. For some reason, I am getting a column in my eigen vectors (the 52nd column out of 601) that is a column of all NAs. I am using the option, symmetric=T for eigen. I just discovered that I do not get this behavior when I use the option EISPACK=T. With EISPACK=T, the 52nd eigenvector is (up to rounding error) a

Hi all, Recently our statistics students noticed that their Gibbs samplers were crashing due to some NaNs in some parameters. The NaNs came from mvrnorm (Ripley & Venables' MASS package multivariate normal sampling function) and with some more investigation it turned out that they were generated by function eigen, the eigenvalue computing function. The problem did not seem to happen

Hi all, Hi, When I want to compute the eigenvalues & eigenvectors of a specific matrix, R crashes (i.e. it stops responding to any input). I've tried it with different versions of R (2.1.1, 2.2.0, 2.2.1) - all with crashing as result. What I did before the crash was: M <- as.matrix(read.table("thematrix",header=T)) eigen(M) If, instead of eigen(M), I use eigen(M,

Hi, All: Attached please find a symmetric, indefinite matrix for which 'eigen(...)$vectors' included NAs: > load("eigenBug.Rdata") > sum(is.na(eigen(eigenBug)$vectors)) [1] 5670 > sessioninfo() Error: could not find function "sessioninfo" > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32 locale: LC_COLLATE=English_United

Dear all I am so glad the R can provide the efficient calculate about eigenvector and eigenvalue. However, i have some puzzle about the procedure of eigen. Fristly, what kind of procedue does the R utilize such that the eigen are obtained? For example, A=matrix(c(1,2,4,3),2,2) we can define the eigenvalue lamda, such as det | 1-lamda 4 | =0 | 2 3-lamda | then

I'm only now realizing that we have severe problems with R on our AMD 'Opteron' and 'Athlon64' clients running Redhat Enterprise with all 64-bit libraries (AFAICS). The Lapack problem happens for R-patched and R-devel both on the Opteron and the Athlon64. Here are platform details: o "gcc -v" and "g77 -v" both end with the line gcc version 3.2.3

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

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,

At 01:37 PM 5/10/00 +0200, ralle wrote: >Hi, >I have a problem understanding what is going on with eigen() for >nonsymmetric matrices. >Example: >h<-rnorm(6) >> dim(h)<-c(2,3) >> c<-rnorm(6) "c" is not a great choice of identifier! >> dim(c)<-c(3,2) >> Pi<-h %*% c >> eigen(Pi)$values >[1] 1.56216542 0.07147773 These could

R-3.0.1 rev 62743, binary downloaded from CRAN just now; macosx 10.8.3 Hello, eigen(symmetric=TRUE) behaves strangely when given complex matrices. The following two lines define 'A', a 100x100 (real) symmetric matrix which theoretical considerations [Bochner's theorem] show to be positive definite: jj <- matrix(0,100,100) A <- exp(-0.1*(row(jj)-col(jj))^2) A's being

Hi all, when testing the new improvements in the new 1.7.0-version I stumbled over the following: >eigen(matrix(c(0,.3,2,.9),2,2)) Error in eigen(matrix(c(0,.3,2,.9),2,2)) : LAPACK routine DGEEV gave error code -13 >eigen(matrix(c(0,.3,2,.9),2,2),EISPACK=TRUE) $values [1] 1.3458236 -0.4458236 $vectors [,1] [,2] [1,] -1.1436890 -0.9760443 [2,] -0.7696018

I'm using R version 2.0.1 on a Windows 2000 operating system. Here is some actual code I executed: > test [,1] [,2] [1,] 1000 500 [2,] 500 250 > eigen(test, symmetric=T)$values [1] 1.250000e+03 -3.153033e-15 > eigen(test, symmetric=T)$values[2] >= 0 [1] FALSE > eigen(test, symmetric=T, only.values=T)$values [1] 1250 0 > eigen(test, symmetric=T,

Hi all, As a molecular biologist by training, I'm fairly new to R (and statistics!), and was hoping for some advice. First of all, I'd like to apologise if my question is more methodological rather than relating to a specific R function. I've done my best to search both in the forum and elsewhere but can't seem to find an answer which works in practice. I am carrying out

Batch: Putting q(save=F) at the end of my file does not work in my context because I can no longer source the file without quitting. I have that quit statement in my .First so that I always quit that way interactively. The problem is that it is ignored in batch. eigen: The crash occurs on my 586 running Red Hat Linux 2.0.27 but not on my son's 486 running SLackware Linix 2.0.29. We both

hi all i have a question that about the eigen analysis found in R and in eviews. i used the same data set in the two packages and found different answers. which is incorrect? the data is: aa ( a correlation matrix) 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 0.9801 0.9801 0.9801 0.9801 0.9801 1 now > svd(aa) $d [1] 4.9204

>From one of my students' simulations: m <- matrix(1, 0, 0) # 1 to force numeric not logical eigen(m) and segfault in TRED2 in src/appl/eigen.f Easy to fix, but I wonder what else might have been overlooked? (svd is protected). --please do not edit the information below-- Version: platform = sparc-sun-solaris2.7 arch = sparc os = solaris2.7 system = sparc, solaris2.7 status =

Dear R-listers Is there anyone who knows why I get different eigenvectors when I run MatLab and R? I run both programs in Windows Me. Can I make R to produce the same vectors as MatLab? #R Matrix PA9900<-c(11/24 ,10/53 ,0/1 ,0/1 ,29/43 ,1/24 ,27/53 ,0/1 ,0/1 ,13/43 ,14/24 ,178/53 ,146/244 ,17/23 ,15/43 ,2/24 ,4/53 ,0/1 ,2/23 ,2/43 ,4/24 ,58/53 ,26/244 ,0/1 ,5/43) #R-syntax

Hi everyone, It's a very minor point, but could we ensure that eigen and La.eigen return a *matrix* for the "vectors" component of the list by including a "drop = FALSE", as specified in the help file, ie put list(values = z$values[ord], vectors = if (!only.values) z$vectors[, ord, drop = FALSE]) Thanks, Jonathan. --please do not edit the information

Hi: I create a hermitian matrix and then perform its singular value decomposition. But when I put it back, I don't get the original hermitian matrix. I am having the same problem with spectral value decomposition as well. I am using R 1.7.0 on Windows. Here is my code: X <- matrix(rnorm(16)+1i*rnorm(16),4) X <- X + t(X) X[upper.tri(X)] <- Conj(X[upper.tri(X)]) Y <-