similar to: nearest positive semidefinit toeplitz matrix

Dear all, Has someone implemented in R (or any other language) Knol DL, ten Berge JMF. Least-squares approximation of an improper correlation matrix by a proper one. Psychometrika, 1989, 54, 53-61. or any other similar algorithm? Best regards Jens Oehlschl?gel Background: I want to factanal() matrices of polychoric correlations which have negative eigenvalue. I coded Highham 2002

Hi, If a matrix is not positive definite, make.positive.definite() function in corpcor library finds the nearest positive definite matrix by the method proposed by Higham (1988). However, when I deal with correlation matrices whose diagonals have to be 1 by definition, how do I do it? The above-mentioned function seem to mess up the diagonal entries. [I haven't seen this complication, but

Dear R users, Has anyone implemented a "matrix log" function in R similar to the function logm() in Matlab? I did a quick R site search and browsed the contributed packages to no avail. The octave function is far too simplistic and fails for the Matlab test matrix. Ideally, the code of Cheng, Higham, and Laub (2001) or something similar could be utilized. Just checking before I

Hello, currently I'm working on a model based on Monte-Carlo-Simulations. I observed that a generated normal distributed times series using rnorm(100,mean=0,sd=1) is far away from being not autocorrelated. Is there any other gerenator implemented in R, which might solve my problem? -- View this message in context:

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

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

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

Dear All: I am trying to fit a special case of a 2-banded Toeplitz correlation structure. A 2-banded Toeplitz has ones on the diagonal, a correlation, RHO1, on the first off-diagonal, and a correlation, RHO2, on the second off-diagonal, with zeros on all subsequent off-diagonals. After reading relevant sections in Mixed-Effects Models in S and S-PLUS (Pinheiro & Bates, 2000) and searching

Is there a function in r that let's you efficiently invert a positive definite symmetric Block Toeplitz matrix? My matrices are the covariance matrices of observations of a multivariate time series and can be 1000*1000 or larger. I know the package 'ltsa' which seems to use the Trench algorithm to compute the inverse of a Toeplitz matrix. I am looking for a so to say

Dear All: I am trying to fit a special case of a 2-banded Toeplitz correlation structure. A 2-banded Toeplitz has ones on the diagonal, a correlation, RHO1, on the first off-diagonal, and a correlation, RHO2, on the second off-diagonal, with zeros on all subsequent off-diagonals. After reading relevant sections in Mixed-Effects Models in S and S-PLUS (Pinheiro & Bates, 2000) and searching

Recently, a package for convex optimization was announced for Python, based upon the LP solver GLPK, the SDP solver in DSDP5, and the LP and QP solvers in MOSEK. I'm aware GLPK is available for R, but wondered if anyone had good packages for convex optimization along these lines for R. TIA.

Hi, I am trying to implement Higham's algorithm for correcting a non positive definite covariance matrix. I found this code in R: http://projects.cs.kent.ac.uk/projects/cxxr/trac/browser/trunk/src/library/Recommended/Matrix/R/nearPD.R?rev=637 I managed to understand most of it, the only line I really don't understand is this one: X <- tcrossprod(Q * rep(d[p], each=nrow(Q)), Q) This

hello, I'm trying to improve the speed of my calculation but didn't get to a satisfying result. It's about the numerical Integration of a bivariate normal distribution. The code I'm currently using x <- qnorm(seq(.Machine$double.xmin,c(1-2*.Machine$double.eps),by=0.01), mean=0,sd=1) rho <- 0.5 integral <- function(rho,x1){

hello all, i'm trying to use QPmat, from the popbio package. it appears to be based on solve.QP and is intended for making a population projection matrix. QPmat asks for: nout, A time series of population vectors and C, C constraint matrix, (with two more vectors, b and nonzero). i believe the relevant code from QPmat is: function (nout, C, b, nonzero) { if (!"quadprog" %in%

Hi all, For computational reasons, I need to estimate an 18x18 polychoric correlation matrix two variables at a time (rather than trying to estimate them all simultaneously using ML). The resulting polychoric correlation matrix I am getting is non-positive definite, which is problematic because I'm using this matrix later on as if it were a legitimately estimated correlation matrix (in order

The "micEcon" package has been split into three packages: miscTools, micEconAids, and micEcon. a) miscTools (version 0.6-0) includes miscellaneous small tools and utilities that are not related to (micro)economics, e.g. colMedians(), rowMedians(), insertCol(), insertRow(), vecli(), symMatrix(), triang(), semidefiniteness(), compPlot(), and rSquared(). The miscTools package should depend

The "micEcon" package has been split into three packages: miscTools, micEconAids, and micEcon. a) miscTools (version 0.6-0) includes miscellaneous small tools and utilities that are not related to (micro)economics, e.g. colMedians(), rowMedians(), insertCol(), insertRow(), vecli(), symMatrix(), triang(), semidefiniteness(), compPlot(), and rSquared(). The miscTools package should depend

Dear list, I am trying to use the 'mvrnorm' function from the MASS package for simulating multivariate Gaussian data with given covariance matrix. The diagonal elements of my covariance matrix should be the same, i.e., all variables have the same marginal variance. Also all correlations between all pair of variables should be identical, but could be any value in [-1,1]. The problem I am

************************************************************ Important: We would draw your attention to the notices at the bottom of this e-mail, particularly before opening and reviewing any file attachment(s). ************************************************************ Here is some code we have used. a<-array(c(1,.9,.7,.9,1,.3,.7,.3,1),dim=c(3,3)) a s<-eigen(a)$vectors

Hi, I would like to generate a correlation matrix with a particular structure. For example, a 3n x 3n matrix : A_(nxn) aI_(nxn) bI_(nxn) aI_(nxn) A_(nxn) cI_(nxn) aI_(nxn) cI_(nxn) A_(nxn) where - A_(nxn) is a *specified* symmetric, positive definite nxn matrix. - I_(nxn) is an identity matrix of order n - a, b, c are (any) real numbers Many attempts have been unsuccessful because a