similar to: Modified Cholesky decomposition for sparse matrices

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

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

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%...

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

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.

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

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

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

I am experiencing some odd behavior with the .Call interface, and I am hoping someone out there can help me with it. Below is a simple example (in that there are R packages that do exactly what I want), but this code illustrates the problem. Thanks in advance for any help you can provide. Suppose I want to compute the log density of a multivariate normal distribution using C code and the gsl

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 Caitlin and Ben, Thanks for your responses! My issue is that I'd like to create one continuous line, rather than 3 lines overlayed. The code I've attached works for a population of 400 and samples 100 times. I'd like to extend this to 300 samples and 3 populations. So, the x-axis would range from 0-300 samples. What I'm having trouble with is finding a way to change the

I hope that someone reading this list can offer some suggestions on how I can incorporate external C libraries in C functions that I want to call from R. I have gone through all of the documentation, help files and mailing list archives and have not been able to find a satisfactory solution. I have constructed a simple example of the problem, since my "real" program is much more

Hi Kirsten, I can run your example code but I can't quite follow your division of sampling. Can you restate the the task? Below is what I think you are asking for, but I have the feeling I may be off the mark. Set A: 400 samples, draw 100 in range of 5 to 15 Set B: 800 samples, draw 100 in range of 5 to 15 Set C: 300 samples, draw 100 in range of 5 to 15 Ben > On Aug 5, 2017, at

Hi! Thanks for taking the time to read this. The code below creates a graph that takes 100 samples that are between 5% and 15% of the population (400). What I'd like to do, however, is add two other sections to the graph. It would look something like this: from 1-100 samples take 100 samples that are between 5% and 15% of the population (400). From 101-200 take 100 samples that are between

Hi Ben, That's exactly right! Except for each set it's the sample population that is 400, 800 or 300. I want to take 3 samples, each of 100, where only the population differs. I can do this separately, but I'm having trouble putting them all on the same graph. I'd like to have sample on the x axis (1-300) and estimate on the y axis. I want to show how population affects the

I hope that someone on this list can help me with this issue. I have searched through all of the documentation and mail archives for a solution, but have been unable to find an answer that addresses my particular problem directly. Suppose I am writing some C code that I want to access from R through the .C function. As part of the C function, I want to include an external C library, such as the

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 have library in development with a function that works when called from the top level, but fails under R CMD check. The paricular line of failure is rsum <- rowSums(kmat>0) where kmat is a dsCMatrix object. I'm currently stumped and looking for some ideas. I've created a stripped down library "ktest" that has only 3 functions: pedigree.R to create a pedigree or