similar to: Cholesky Decomposition in R

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

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

Hi there, Given a positive definite symmetric matrix, I can use chol(x) to obtain U where U is upper triangular and x=U'U. For example, x=matrix(c(5,1,2,1,3,1,2,1,4),3,3) U=chol(x) U # [,1] [,2] [,3] #[1,] 2.236068 0.4472136 0.8944272 #[2,] 0.000000 1.6733201 0.3585686 #[3,] 0.000000 0.0000000 1.7525492 t(U)%*%U # this is exactly x Does anyone know how to obtain L such

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

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.

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

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

I've created some Splus code for a microarray problem that - needed to be in C, to take advantage of some sparse matrix properties - uses a cholesky decompostion as part of the computation For the cholesky, I used the cholesky2 routine, which is a part of the survival library. It does just what I want and I'm familiar with it (after all, I wrote it). In Splus, this all works

I think I am missing something with the chol() function. Here is my calculation: ? > mat ???? [,1] [,2] [,3] [,4] [,5] [1,]??? 1??? 3??? 0??? 0??? 0 [2,]??? 0??? 1??? 0??? 0??? 0 [3,]??? 0??? 0??? 1??? 0??? 0 [4,]??? 0??? 0??? 0??? 1??? 0 [5,]??? 0??? 0??? 0??? 0??? 1 > eigen(mat) $values [1] 1 1 1 1 1 $vectors ???? [,1]????????? [,2] [,3] [,4] [,5] [1,]??? 1 -1.000000e+00??? 0??? 0??? 0

Hi all, I am trying to use mle() to find a self-defined function. Here is my function: test <- function(a=0.1, b=0.1, c=0.001, e=0.2){ # omega is the known covariance matrix, Y is the response vector, X is the explanatory matrix odet = unlist(determinant(omega))[1] # do cholesky decomposition C = chol(omega) # transform data U = t(C)%*%Y WW=t(C)%*%X beta = lm(U~W)$coef Z=Y-X%*%beta

Hi there, Please help me this out. > m [,1] [,2] [1,] 1.1 1.0 [2,] 1.0 1.1 > chol2inv(m) [,1] [,2] [1,] 1.5094597 -0.7513148 [2,] -0.7513148 0.8264463 > CT -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

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

Hi, I want to simulate a data set with similar covariance structure as my observed data, and have calculated a covariance matrix (dimensions 8368*8368). So far I've tried two approaches to simulating data: rmvnorm from the mvtnorm package, and by using the Cholesky decomposition (http://www.cerebralmastication.com/2010/09/cholesk-post-on-correlated-random-normal-generation/). The problem is

I have been running into difficulties with dispatching on an S4 class defined in the SparseM package, when the method calls are inside a function passed as the f= argument to optimize() in functions in the spdep package. The S4 methods are typically defined as: setMethod("det","matrix.csr", function(x, ...) det(chol(x))^2) that is within setMethod() rather than by name before

I've been writing some matrix-type methods for a new class of sparse matrices and for most methods this has been straightforward. However, there are examples, like %*% and chol, that (apparently) R doesn't automatically recognize as generic. What to do in these cases? At this point, I've been writing new generic methods with slightly perturbed names %m% and cholesky for example in

Dear All, My question is simple but I need someone to help me out. Suppose I have a positive definite matrix A. The funtion chol() gives matrix L, such that A = L'L. The inverse of A, say A.inv, is also positive definite and can be factorized as A.inv = M'M. Then A = inverse of (A.inv) = inverse of (M'M) = (inverse of M) %*% (inverse of M)' = ((inverse of

Dear all, (sorry I got the wrong button for subscribing a minute ago) At the moment I'm writing on a package for random field simulation that I'd like to make publically availabe in near future. To this end I've asked Martin Maechler to have a look at my R-code. He was very surprised about how I perform the ".C"-calls, and encouraged me to make this request for comments.

Dear R People: I am attempting to install SparseM on R 2.15.0 on a Linux 11.10 system. Here is the output > install.packages("SparseM",depen=TRUE) Installing package(s) into ?/home/erin/R/x86_64-pc-linux-gnu-library/2.15? (as ?lib? is unspecified) --- Please select a CRAN mirror for use in this session --- Loading Tcl/Tk interface ... done trying URL

Hi, I'm writing an R package using the C code i've written. I'm wondering if anyone knows an easy way to calculate an inverse and cholesky factor of a matrix using the Fortran/C library of R: and how to call them from C. My code is based on the Numerical Reciepe code, and I'm trying to use something that is already in R. Thanks for your help in advance, Kosuke

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