similar to: non-reproducible eigen() output with MKL

Hi, I have a question for my simulation problem: I would like to generate a positive (or semi def positive) covariance matrix, non singular, in wich the spectral decomposition returns me the same values for all dimensions but differs only in eigenvectors. Ex. sigma [,1] [,2] [1,] 5.05 4.95 [2,] 4.95 5.05 > eigen(sigma) $values [1] 10.0 0.1 $vectors [,1]

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

I would like to deprecate La.eigen. It is used in a few packages (ade4, fpc, gss, mvtnorm and smoothSurv on CRAN), but only in usages where replacing `La.eigen' by `eigen' would call exactly the same code. The reason for wanting to deprecate it is that little-used interfaces tend to get overlooked, e.g. PR#5406, a report on eigen, needed to be applied to La.eigen as well. We have also

Hi all, I managed to at least compile R-patched (2002-01-08) against MKL 5.1 (not beta). The release notes for MKL said that the libraries are threaded, and the test codes needed to be linked against the pthread library. Therefore I added -lpthread flag in config.site for R-patched, and the compile went through. However, when I did make check, it choked on a call to La.eigen, complaining that

Hi, with small matrices eigen works as expected: > eigen(cbind(c(1,4),c(4,7)), only.values = TRUE) $values [1] 9 -1 $vectors NULL > eigen(cbind(c(1,4),c(4,7))) $values [1] 9 -1 $vectors [,1] [,2] [1,] 0.4472136 -0.8944272 [2,] 0.8944272 0.4472136 > eigen(cbind(c(1,-1),c(1,-1))) $values [1] -3.25177e-17+1.570092e-16i -3.25177e-17-1.570092e-16i $vectors

Dear R-users, Recently, I have come across a weird problem. I run a large number of iterations and at one of the step within each iteration, I calculate the eigen values of a updated covariance matrix. From all my intermediate output, the code freezes after printing out the covariance matrix but before printing out the eigen values. So, obviously it stops at the only step, the eigen() function

Dear Sir, I am an R user. I am in problem to find eigen vectors in R. For the following matrix eigen vectors are not right. I can not understand why?? For the 1st eigen value and 2nd eigen value are same, but the eigen vectors are not same. *HOW CAN I RESOLVE THE PROBLEM??* *>c=matrix(c(1,0,0,1,2,0,-3,5,2),nrow=3,byrow=T)> eigen(c)$values[1] 2 2 1$vectors [,1] [,2]

Sorry to bother everyone, but I've looked in all of the help files and manuals I have and I can't find the answer to this question. I'm doing principle component analysis by calculating the eigen vectors of a correlation matrix that I have that is composed of 21 parameters. I have the eigen vectors and their values that R produced for me but I'm not sure how to tell which

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,

I'm using R 1.7.1 under linux redhat it seems that the eigen values produced by eigen() do not follow a consistant order; I mean either ascending or discending e.g for one system: eigenV<-eigen(V) > print(eigenV$values) [1] -7.706828e+13 -4.702980e+13 -3.267579e+13 -1.701297e+13 -8.041677e+12 [6] -5.707311e+12 -5.053941e+12 -4.774652e+12 -4.280423e+12 -3.798905e+12

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

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

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

It was a real surprise, but a student in my class found that the function eigen is buggy. He traced to the problem from his inability of getting principal component analysis to work on his data. Chong Gu Here is a matrix I generated through X'X, where X is 2x3. > jj [,1] [,2] [,3] [1,] 0.8288469 -1.269783 -0.7533517 [2,] -1.2697829 2.162132 2.0262917 [3,]

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,

Hi I am having difficulty with eigen() on R-devel_2006-01-05.tar.gz Specifically, in R-2.2.0 I get expected behaviour: > eigen(matrix(1:100,10,10),FALSE,TRUE)$values [1] 5.208398e+02+0.000000e+00i -1.583980e+01+0.000000e+00i [3] -4.805412e-15+0.000000e+00i 1.347691e-15+4.487511e-15i [5] 1.347691e-15-4.487511e-15i -4.269863e-16+0.000000e+00i [7] 1.364748e-16+0.000000e+00i

Hi, dear R pros I try to understand eigen(). I have seen, that eigen() gives the eigenvectors normalized to unit length. What shall I do to get the eigenvectors not normalized to unit length? E.g. take the example: A [,1] [,2] V1 0.7714286 -0.2571429 V2 -0.4224490 0.1408163 Calculating eigen(A) "by hand" gives the eigenvectors (example from Backhaus,

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

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,