similar to: Need Advice on Matrix Not Positive Semi-Definite with cholesky decomposition

Can we do Cholesky Decompositon in R for any matrix --------------------------------- [[alternative HTML version deleted]]

Hi everyone: I try to use r to do the Cholesky Decomposition,which is A=LDL',so far I only found how to decomposite A in to LL' by using chol(A),the function Cholesky(A) doesnt work,any one know other command to decomposte A in to LDL' My r code is: library(Matrix) A=matrix(c(1,1,1,1,5,5,1,5,14),nrow=3) > chol(A) [,1] [,2] [,3] [1,] 1 1 1 [2,] 0 2 2

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

I am trying to estimate a covariance matrix from the Hessian of a posterior mode. However, this Hessian is indefinite (possibly because of numerical/roundoff issues), and thus, the Cholesky decomposition does not exist. So, I want to use a modified Cholesky algorithm to estimate a Cholesky of a pseudovariance that is reasonably close to the original matrix. I know that there are R packages that

Dear all, I made a simple test of the Cholesky decomposition in the package 'Matrix', by considering 2 variables 100% correlated. http://blogs.sas.com/content/iml/2012/02/08/use-the-cholesky-transformation-to-correlate-and-uncorrelate-variables/ The full code is below and can be simply copy&paste in the R prompt. After uncorrelation I still have a correlation of +-100%...

The gchol function in library(kinship) does an LDL decomposition. An updated version has just recently been posted on Rforge, in the bdsmatrix library which is part of survival. > temp <- matrix(c(1,1,1,1,5,8,1,8,14), 3) > gt <- gchol(temp) > as.matrix(gt) # L [,1] [,2] [,3] [1,] 1 0.00 0 [2,] 1 1.00 0 [3,] 1 1.75 1 > diag(gt) # D [1]

Hi: what I want to do is decompose the a symmetric matrix A into this form A=LDL' hence TAT'=D,T is inverse of (L)and T is a lower trangular matrix,and D is dignoal matrix for one case A=1 1 1 1 5 5 1 5 14 T=inverse(L)= 1 0 0 -1 1 0 0 -1 1 D=(1,4,9) I tried to use chol(A),but it returns only trangular, anyone know the function can return

Dear R-devel members, I am looking for a fast Cholesky update/downdate. The matrix A being symmetric positive definite (n, n) and factorized as A = L %*% t(L), the goal is to factor the new matrix A +- C %*% t(C) where C is (n, r). For instance, C is 1-column when adding/removing an observation in a linear regression. Of special interest is the case where A is sparse. Looking at the

I am new to using R and would appreciate some advice on which books to start with to get up to speed on using R. My Background: 1-C# programmer. 2-Programmed directly using IMSL (Now Visual Numerics). 3- Used in past SPSS and Statistica. I put together a list but would like to pick the "best of" and avoid redundancy. Any suggestions on these books would be helpful (i.e. too much

Hello Everyone I have a MCMC loop to calculate a time varying hierarchical Bayesian structure. This requires me to use around 5-6 matrix inversions in the loop. I use cholesky and chol2inv for the matrix decomposition. Because of the data I am working with I am required to invert a 167 by 167 matrix twice in one iteration. I need to run the iteration for 10000 times, but I get the error

Dear all: In what I am doing I sometimes get a (Hessian) matrix that has a couple of tiny negative eigenvalues (e.g. -6 * 10^-17). So, I can't run a Cholesky decomp on it - but I need to. Is there an established way to regularize my (Hessian) matrix (e.g., via some transformation) that would allow me to get a semi-positive definite matrix to be used in Cholesky decomp? Or should I try some

m <- matrix(nrow=5, ncol=5) m <- ifelse(row(m)==col(m), 1, 0.2) c <- chol(m) # Choleski decomposition u <- matrix(rnorm(2000*5), ncol=5) uc <- u %*% c cr <- pnorm(uc) cr <- qbinom(cr,1,0.5) cor(cr) I expected that the cor(cr) to be 0.2 as i set in m, but the result is around 0.1. Why is that? Thanks -- View this message in context:

Hi all, This question is not really R related, rather on Statistics subject itself. Even I did not do those using R. however still I want to post it here, because my hope is I could get help from great statisticians who are the very active member of this group. My problem is to interpret Variance decomposition of VAR model in layman's language. Using EViews I got following : Variance

I mistakenly left a write in "/tmp" in the rockchalk package (version 1.8.109) that I uploaded last Friday. Kurt H wrote and asked me to fix today. While uploading a new one, I became aware of a problem I had not seen. The version I uploaded last Friday, 1.8.109, has OK status on all platforms except r-release-windows-ix86+x86_64. I get OK on oldrel-windows and also on devel-windows.

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

I have been playing around with sparse matrices in the Matrix package, in particularly with the Cholesky factorization of matrices of class dsCMatrix. And BTW, what a fantastic package. My problem is that I have to carry out repeated Cholesky factorization of a spares symmetric matrices, say Q_1, Q_2, ...,Q_n, where the Q's have the same non-zero pattern. I know in this case one does

Can anyone advise me on some pratical papers or books On Gaussian Copulas? Anything in the genre of Copulas Dummies Would be a help. As simpe, and approachable with minimal pedantic style. Thanks, Neil -------------------------------------------------------- This information is being sent at the recipient's reques...{{dropped:16}}

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%

Dear R-Users I used the nlme library to fit a linear mixed model (lme). The random effect standard errors and correlation reported are based on a Log-Cholesky parametrization. Can anyone tell me how to get the Covariance matrix of the random effects, given the above mentioned parameters based on the Log-Cholesky parametrization?? Thanks in advance Pryseley

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