similar to: ?eigen documentation suggestion

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'm trying to test if a correlation matrix is positive semidefinite. My understanding is that a matrix is positive semidefinite if it is Hermitian and all its eigenvalues are positive. The values in my correlation matrix are real and the layout means that it is symmetric. This seems to satisfy the Hermitian criterion so I figure that my real challenge is to check if the eigenvalues are all

I thought that the function eigen(A) will return a matrix with eigenvectors that are independent of each other (thus forming a base and the matrix being invertible). This seems not to be the case in the following example A=matrix(c(1,2,0,1),nrow=2,byrow=T) eigen(A) ->ev solve(ev$vectors) note that I try to get the upper triangular form with eigenvalues on the diagonal and (possibly) 1 just

Hi, It is my understanding that the eigenvectors of a circulant matrix are given as follows: 1,omega,omega^2,....,omega^{p-1} where the matrix has dimension given by p x p and omega is one of p complex roots of unity. (See Bellman for an excellent discussion on this). The matrix created by the attached row and obtained using the following commands indicates no imaginary parts for the

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 <-

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

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

Dear all, It is not a R related problem rather than statistical/mathematical. However I am posting this query hoping that anyone can help me on this matter. My problem is to get the Geometrical Interpretation of Eigen value and Eigen vector of any square matrix. Can anyone give me a light on it? Thanks and regards, Arun [[alternative HTML version deleted]]

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. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

General goal: Write R code to find the inverse matrix of an nxn positive definite symmetric matrix. Use solve() to verify your code works. Started with a 3x3 matrix example to build the code, but something dosen't seem to be working. I just don't know where I am going wrong. ##Example matrix I found online A<-c(4,1,-1,1,2,1,-1,1,2) m<-matrix(A,nrow=3,ncol=3) ##Caculate the eigen

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,

Dear R-tisans, I am trying to calculate the 12th root of a transition (square) matrix, but can't seem to obtain an accurate result. I realize that this post is laced with intimations of quantitative finance, but the question is both R-related and broadly mathematical. That said, I'm happy to post this to R-SIG-Finance if I've erred in posting this to the general list. I've

Full_Name: Pierre Legendre Version: 2.1.1 OS: Mac OSX 10.4.3 Submission from: (NULL) (132.204.120.81) I am reporting the mis-behaviour of the function 'eigen' in 'base', for the following input matrix: A <- matrix(c(2,3,4,-1,3,1,1,-2,0),3,3) eigen(A) I obtain the following results, which are incorrect for eigenvalues and eigenvectors 2 and 3 (incorrect imaginary portions):

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

DeaR list, I happily use eigen() to compute the eigenvalues and eigenvectors of a fairly large matrix (200x200, say), but it seems over-killed as its rank is limited to typically 2 or 3. I sort of remember being taught that numerical techniques can find iteratively decreasing eigenvalues and corresponding orthogonal eigenvectors, which would provide a nice alternative (once I have the

Hi everyone, I am using eigen() to extract the 2 major eigenpairs from a large real square symmetric matrix. The procedure is already rather efficient, but becomes somehow slow for real time needs with moderately large matrices (few thousand lines). The R implementation statically extracts all eigenvalues (and optionally associated eigenvectors). I heard about optimizations of the eigen

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

Dear Peter, thank you very much for your answer. My problem is that I need to calculate the following quantity: solve(chol(A)%*%Y) Y is a 3*3 diagonal matrix and A is a 3*3 matrix. Unfortunately one eigenvalue of A is negative. I can anyway take the square root of A but when I multiply it by Y, the imaginary part of the square root of A is dropped, and I do not get the right answer. I tried

Dear all R users, Can anyone tell me to calculate Eigen value of any real symmetric matrix which algorithm R uses? Is it Jacobi method ? If not is it possible to get explicit algorithm for calculating it? Thanks and regards, Arun [[alternative HTML version deleted]]