similar to: Found explanation for R-2.15.2 slowdown in one case; caution for any users of La_chol

Speaking of optimization and speeding up R calculations... I mentioned last week I want to speed up calculation of generalized inverses. On Debian Wheezy with R-2.15.2, I see a huge speedup using a souped up generalized inverse algorithm published by V. N. Katsikis, D. Pappas, Fast computing of theMoore-Penrose inverse matrix, Electronic Journal of Linear Algebra, 17(2008), 637-650. I was so

Dear R contributors, Considering the following sample C code, that illustrates two possible uses of a Cholesky decomp for inverting a matrix, equally valid at least in theory: SEXP test() { int d = 2; int info = 0; double mat[4] = {2.5, 0.4, 0.4, 1.7}; double id[4] = {1.0, 0.0, 0.0, 1.0}; double lmat[3]; F77_CALL(dpotrf)("L", &d, mat, &d, &info); lmat[0] = mat[0]; lmat[1]

Hi all, im triyng to implement a bayesian model with R and c++. I have a strange problem. I can't reproduce the error with a small script and then i post the original one. The problem is after the line for(MCMC_iter2=0;MCMC_iter2<thin;MCMC_iter2++) For the first 34 iterations all work fine, after, all the simulations of mu_acc_P return an "nan". If i delete the line

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

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

Hey All, I was wondering if I could solicit a little advice. I have been experiencing some quirkiness in my C code through .Call. Unfortunately, my program is rather large, so I'm trying to create a brief example. Yet before I do, I thought maybe a conceptual question would be sufficient. Basically, I'm writing up a multidimensional Gaussian likelihood (with spatial covariances and

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 R list members, I have a vector of Cholesky parameterization of a matrix let say A. I would like to compute the determinant and inverse of the original matrix A from the vector of cholesky parameters , would you suggest an R function to do the task. I have tried hard but unable to find anything like that. Please direct me any package or please share R code that can do the task. Thanks and

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 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

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

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.

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

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

Dear R users, I hope you all are doing great. Is there a way to automatically upgrade my R 2.15.0 to the new R 2.15.2 release instead of having to reinstall the new release? Any information regarding this would be greatly appreciated. Best regards, Paul -- View this message in context: http://r.789695.n4.nabble.com/Updating-R-2-15-0-to-2-15-2-tp4647542.html Sent from the R help mailing

I was feeling masochistic the other day and we have been having some wierd memory problems so I started digging into the source for L-BFGS-B. In the lbgfsb.c file I see the following code: /* Cholesky factorization of (2,2) block of wn. */ F77_CALL(dpofa)(&wn[*col + 1 + (*col + 1) * wn_dim1], &m2, col, info); if (*info != 0) { *info = -2; return; } If I am not mistaken

Hello, I have been attempting to run a poisson glmm using lme4 for some time now and have had a lot of trouble. I would say 9 times out of 10 I receive the following warning: CHOLMOD warning: %h Error in mer_finalize(ans) : Cholmod error `not positive definite' at file:../Cholesky/t_cholmod_rowfac.c, line 432 My data are counts of microbe colony forming units (CFUs) collected from

Hi, I'm trying to use Frank Harrell's Design:::ols function to do regression of y (numeric) on the interaction of two factors (x1 and x2), but Lapack throws an error: > library(Design) ... > load(url("http://www.csse.unimelb.edu.au/~gabraham/x")) > ols(y ~ x1 * x2, data=x) Error in chol2inv(fit$qr$qr) : 'size' cannot exceed nrow(x) = 20 > traceback()

Hi, I'm wondering if it is possible to install a package for R 2.0 on R 1.9.1 on Mac OS X? I'm getting this error which seems to be known issue: library("quantreg") Error in firstlib(which.lib.loc, package) : couldn't find function "lazyLoad" In addition: Warning message: package quantreg was built under R version 2.0.1 Error in

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