similar to: eigenvalues and correlation matrices

Hello, I guess, it's a rather simple thing but I cannot find a short way to reduce a matrix, removing all rows and columns having just NA elements. testmatrix <- matrix(nrow=6, ncol=4) testmatrix[2:5,2:3] <- seq(2) > testmatrix [,1] [,2] [,3] [,4] [1,] NA NA NA NA [2,] NA 1 1 NA [3,] NA 2 2 NA [4,] NA 1 1 NA [5,] NA 2 2 NA

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 <-

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

Hello everybody When one is working with complex matrices, "transpose" very nearly always means *Hermitian* transpose, that is, A[i,j] <- Conj(A[j,i]). One often writes A^* for the Hermitian transpose. I have only once seen a "real-life" case where transposition does not occur simultaneously with complex conjugation. And I'm not 100% sure that that wasn't a

Hello, I am attempting to write a function that assigns a value to a row or column within the matrix that it runs on. I am, however, having trouble accessing the row or column within this dynamic variable. I have looked through the archives (nothing there) and read the rules for posting to this list. Please help me out! Here is a sample of the problem: testMatrix = matrix(data=2, nrow=10,

Dear list, Please find below an example of odd behaviour of the by function. It occurs both under GNU/Linux R 2.6.2 and Windows R 2.7.0alpha. Respective sessionInfo()'s are given below. I hope I do not overlook anything. testFactor <- factor(sample(LETTERS[1:6], size = 42, replace = TRUE)) testMatrix <- matrix(rnorm(42 * 6), nrow = 42) testDf <- as.data.frame(testMatrix) by(data

I'm having trouble making contour lines for this attached, sparse dataset (low data:NA ratio!). Is it the high number of NA's, or funny layout of the densities, or something else that's causing this? This is a subsample of the dataset, and I get the same problem when using the full data. The only reason I get any contours is because I'm forcing specific levels. If no levels are

This thread reveals that R has some holes in the solution of some of the linear algebra problems that may arise. It looks like Jim Ramsay used a quick and dirty approach to the generalized eigenproblem by using B^(-1) %*% A, which is usually not too successful due to issues with condition of B and making a symmetric/Hermitian problem unsymmetric. In short, the problem is stated as follows:

from ?eigen symmetric: if 'TRUE', the matrix is assumed to be symmetric (or Hermitian if complex) and only its lower triangle is used. If 'symmetric' is not specified, the matrix is inspected for symmetry. I think that could mislead a naive reader as it suggests that, with symmetric=TRUE, the result of eigen() (vectors and values) depends only on

Dear All I am trying to do a partial canonical analysis of principal coordinates using Bray-Curtis distances. The capscale addin to R appears to be the only way of doing it, however, when I try and calculate a Bray-Curtis distance matrix either using Capscale or Vegedist (capscale I understand uses Vegedist anyway to calculate its distance matrix), R uses up all available memory on the computer,

Fangrui Song via cfe-dev <cfe-dev at lists.llvm.org> writes: > > One property of "Squash and merge" is that it will add intermediate > commits as bullet points (`* `). In many cases the merger does not spend > more time cleaning up the description so a commit may look like: > > ``` > RFC: treat small negative λ as 0 for sqrt(::Hermitian) (#35057) > >

Hi, I have a set of covariance matrices but not the original data. I want to carry out some exploratory factor analysis. So, I am trying to construct a covariance matrix list as the input for factanal. I can construct a list which includes the cov, the centers, and the n.obs. But it doesn't work. I get an error that says "Error in sqrt(diag(cv)) : Non-numeric argument to mathematical

Hello. I'm trying to solve a quadratic programming problem of the form min ||Hx - y||^2 s.t. x >= 0 and x <= t using solve.QP in the quadprog package but I'm having problems with Dmat not being positive definite, which is kinda okay since I expect it to be numerically semi-definite in most cases. As far as I'm aware the problem arises because the Goldfarb and Idnani method first

On Fri, Sep 11, 2020 at 6:53 PM David Blaikie <dblaikie at gmail.com> wrote: > Is there any observable difference between "Squash and Merge" or "Rebase > and Merge" when "enforce linear history" is enabled, then? > "Squash and Merge" will only generate one commit. > > On Fri, Sep 11, 2020 at 3:45 PM Stephen Neuendorffer via llvm-dev

Hello all, I was wondering if there is a function in R that only computes the eigenvector corresponding to the largest/smallest eigenvalue of an arbitrary real matrix. Thanks Minh -- Living on Earth may be expensive, but it includes an annual free trip around the Sun.

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 to everybody, I have a quadratic programming problem that I am trying to solve by various methods. One of them is to use the quadprog package in R. When I check positive definiteness of the D matrix, I get that one of the eigenvalues is negative of order 10^(-8). All the others are positive. When I set this particular eigenvalue to 0.0 and I recheck the eigenvalues in R, the last

Full_Name: Hao Yu Version: 1.4.1 OS: Windws and Linux Submission from: (NULL) (129.100.45.161) Hi, Recently we did some benchmarks regarding matrix inverse operation and found some strange things. See the following results (the package Matrix is most updated). 1. load the function Toeplitz and library(Matrix) "Toeplitz" function(x) { matrix(x[1 + abs(outer(seq(along = x),

hHllo, I'm looking for an algroithm to transform an existing toeplitz matrix (autocorrelation matrix) to the nearest positive semidefinite toeplitz matrix. I merely found an algorithm to transform an correlation matrix via the function nearcor() based on the algorithm of Higham. But as I examined, it destroys the toeplitz structure of my underlying matrix. Does any function already exist

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