similar to: problem with eigen() function

Hi I need to extract the data returned by Eigen to plot the eigenvectors. However, when I try and eigv = eigen(covariance); it returns an object with the matrices containing eigenvalues and vectors.. how can I extract the eigenvector matrix from this?? When I try mat = eig["vectors"] it returns a matrix with the "$vectors" string on top , how can I remove this? code: > eig

Full_Name: cajo ter Braak Version: 2.1.1 OS: Windows Submission from: (NULL) (137.224.10.105) # I would like to attach the matrix C in the Rdata file; it is 50x50 and comes from a geostatistical problem (spherical covariogram) > rm(list=ls(all=TRUE)) > load(file= "test.eigen.Rdata") > ls() [1] "C" "eW" > > sym.check = max(abs(C - t(C))) # should

Dear, My problems are; 1. Simulate a P-variate multivariate data set (N by P) 2. Draw samples of size n from N (with or without replacement) 3. Obtain eigen values of the variance-covariance matrix of each sample drawn in (2) above. 4. print out the matrix of eigen values as well as the identity of sample generating them. Thanks ---------------------------------

... I checked and Brian and I are both right (see bottom for prior mail exchange). Let me explain: ============================================================= 1. Indeed, in principle, princomp allows data matrices with are wider than high. Example: > x1 [,1] [,2] [,3] [,4] [1,] 1 1 2 2 [2,] 1 1 2 2 > princomp(x1) Call: princomp(x = x1) Standard deviations:

... I checked and Brian and I are both right (see bottom for prior mail exchange). Let me explain: ============================================================= 1. Indeed, in principle, princomp allows data matrices with are wider than high. Example: > x1 [,1] [,2] [,3] [,4] [1,] 1 1 2 2 [2,] 1 1 2 2 > princomp(x1) Call: princomp(x = x1) Standard deviations:

I would presume this is another manifestation of what I reported (reproduced below) on 2003-12-01. cajo.terbraak at wur.nl wrote: >Full_Name: cajo ter Braak >Version: 2.1.1 >OS: Windows >Submission from: (NULL) (137.224.10.105) > > ># I would like to attach the matrix C in the Rdata file; it is 50x50 and comes >from a geostatistical problem (spherical covariogram) >

Hi, I try to calculate the angle between two first eigenvectors of different covariance matrices of biological phenotypic traits for different populations. My issue here is, that all possibilities to do so seem to normalize the eigenvectors to length 1. Although the helpfile of eigen() states, that using eigen(, symmetric = FALSE, EISPACK =TRUE) skips normalization this is (I guess) not applicable

Hi, I want to simulate a data set with similar covariance structure as my observed data, and have calculated a covariance matrix (dimensions 8368*8368). So far I've tried two approaches to simulating data: rmvnorm from the mvtnorm package, and by using the Cholesky decomposition (http://www.cerebralmastication.com/2010/09/cholesk-post-on-correlated-random-normal-generation/). The problem is

Full_Name: Patrick Burns Version: 1.8.1 OS: Windows 2000 Submission from: (NULL) (81.129.3.151) The command: eigen(matrix(Inf, 2, 2)) causes version 1.8.1 to hang in both Windows 2000 and SuSe 8.2. (Hang defined as apparently needing to kill the process in order to get a prompt back.) This is similar to bug #4366 where the same command used NaN instead of Inf.

This is undoubtedly a bug, but I doubt that it is really down to R. Synopsis: a certain matrix causes eigen(symmetric=TRUE) to produce NaN's in some of the returned eigenvectors. This happens using SuSe 8.2 Professional and the precompiled R rpm (happens in both 1.8.1 and 1.7.1). I don't see it under Windows. To reproduce the bug: The matrix (75 by 75, about 45K) is in

Hello, I would like to simulate randomly from a multivariate normal distribution using a correlation matrix, rho. I do not have sigma. I have searched the help archive and the R documentation as well as doing a standard google search. What I have seen is that one can either use rmvnorm in the package: mvtnorm or mvrnorm in the package: MASS. I believe I read somewhere that the latter was

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, 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

While using the rmvnorm function, I get the error: Error in eigen(sigma, sym = TRUE) : error code 5 from Lapack routine 'dsyevr' The same thing happens when I try the eigen() function on my covariance matrix. The matrix is a symmetric 111x111 matrix. Well, it is almost symmetric; there are slight deviations from symmetry (the largest is 3e-18). I have this in an MCMC loop, and it

Hi everyone. My problem is about multivariate analysis. Given a multivariate data X, of dimension (n by p), n is the number of observations while p is the number of variables. I wanted a sample of size p+1 that will have its product of the eigen values of the covariance matrix to be the minimum among all the possible samples (n combination (p+1)). I have written a syntax with worked well, but the

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