similar to: positive semi-definite 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

1. Martin Maechler's comments should be taken as replacements for anything I wrote where appropriate. Any apparent conflict is a result of his superior knowledge. 2. 'eigen' returns the eigenvalue decomposition assuming the matrix is symmetric, ignoring anything in m[upper.tri(m)]. 3. The basic idea behind both posdefify and nearPD is to compute the

************************************************************ 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

Dear fellow R Users: I am doing a Cholesky decomposition on a correlation matrix and get error message the matrix is not semi-definite. Does anyone know: 1- a work around to this issue? 2- Is there any approach to try and figure out what vector might be co-linear with another in thr Matrix? 3- any way to perturb the data to work around this? Thanks for any suggestions.

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

Dear All, I was wondering whether any one knows of a matrix bending function in R that can turn non-positive definite matrices into the nearest positive definite matrix. I was hoping there would be something akin to John Henshall's flbend program (http://agbu.une.edu.au/~kmeyer/pdmatrix.html), which allows the standard errors of the estimated matrix elements to be considered in the

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%

I apologise for not including a reproducible example with this query but I hope that I can make things clear without one. I am fitting some finite mixture models to data. Each mixture component has p parameters (p=29 in my application) and there are q components to the mixture. The number of data points is n ~ 1500. I need to select a good q and I have been considering model selection methods

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Hello I am trying to determine wether a given matrix is symmetric and positive matrix. The matrix has real valued elements. I have been reading about the cholesky method and another method is to find the eigenvalues. I cant understand how to implement either of the two. Can someone point me to the right direction. I have used ?chol to see the help but if the matrix is not positive definite it

I am creating covariance matrices from sets of points, and I am having frequent problems where I create matrices that are non-positive definite. I've started using the corpcor package, which was specifically designed to address these types of problems. It has solved many of my problems, but I still have one left. One of the matrices I need to calculate is a cross-covariance matrix. In other

I want to compute B=A^{1/2} such that B*B=A. For example a=matrix(c(1,.2,.2,.2,1,.2,.2,.2,1),ncol=3) so > a [,1] [,2] [,3] [1,] 1.0 0.2 0.2 [2,] 0.2 1.0 0.2 [3,] 0.2 0.2 1.0 > a%*%a [,1] [,2] [,3] [1,] 1.08 0.44 0.44 [2,] 0.44 1.08 0.44 [3,] 0.44 0.44 1.08 > b=a%*%a i have tried to use singular value decomposion > c=svd(b) > c$u%*%diag(sqrt(c$d))

Hey, all Given a square matrix, how can I check if this matrix is positive definite or not? Thanks. Fred

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

Dear R-listers Is there anyone who knows why I get different eigenvectors when I run MatLab and R? I run both programs in Windows Me. Can I make R to produce the same vectors as MatLab? #R Matrix PA9900<-c(11/24 ,10/53 ,0/1 ,0/1 ,29/43 ,1/24 ,27/53 ,0/1 ,0/1 ,13/43 ,14/24 ,178/53 ,146/244 ,17/23 ,15/43 ,2/24 ,4/53 ,0/1 ,2/23 ,2/43 ,4/24 ,58/53 ,26/244 ,0/1 ,5/43) #R-syntax

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]

Hey, All In principal component analysis (PCA), we want to know how many percentage the first principal component explain the total variances among the data. Assume the data matrix X is zero-meaned, and I used the following procedures: C = covriance(X) %% calculate the covariance matrix; [EVector,EValues]=eig(C) %% L = diag(EValues) %%L is a column vector with eigenvalues as the elements percent

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

Hi there, I'm stuck, but since I just started learning R, this might be a trivial problem. I need to do a bootstrap on the variance among the eigenvalues of a matrix. I can get this variance doing this: >var.eigenvalues=function(x) >var(eigen(cov(x), symmetric = T, only.values = T)$values) but if I try to run: >matrix=read.table("matrix.txt", header=T)

I know, this is a forum about R. But I am so desperate of this problem (BTW, anyone knows any good Statistics/Math forum to post question like this?): A and B are both n x n positive definite matrix. Denote A > B, if A - B is positive definite. I know this is true: if A > B, then A^{-1} < B^{-1}. But how to prove this? I tried to diagonalize A and B, but since they can have different