similar to: Cholesky Decomposition

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]

Dear All, I have problem installing the 'kinship' package. I used the 'install.packages' from R command line and had no problem installing other packages before. Here is what I get: * Installing *source* package 'kinship' ... ** libs gcc -no-cpp-precomp -I/Library/Frameworks/R.framework/Resources/include -I/usr/local/include -fno-common -g -O2 -c agfit6b.c -o agfit6b.o

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

How to select training data set and test data set from the original data for performing cross-validation --------------------------------- [[alternative HTML version deleted]]

Hi, How can I find and download a function in R to do the LU decompostion for finding the upper and lower triangular matrix.  Thank you so much. Joe ___________________________________________________ 您的生活即時通 － 溝通、娛樂、生活、工作一次搞定！ [[alternative HTML version deleted]]

I'm new to the topic of wavelets. When I tried to use the mra function in the wavelets package, the data is not getting compressed. eg. if the original data has 500 values , the output data also has the same. However in MATLAB, depending on the level of decompositon, the data gets compressed. How do I implement this in R? -- View this message in context:

I specifically need rq matrix decomposition (and not qr). Looking at netlib site for LAPACK it does provide rq whereas LINPACK not. Looking at companion qr in R I see how in base it wraps with a .Call but I do not have success in doing that for a similar .Call for rq. Anyone done this or can provide matrix rewrites that allow me to do the rq decomposition with existing R funcs? Regards MJ

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

>From Internet I downloaded the file Hmisc.zip and used it for R package updation. and R gave the message 'Hmisc' successfull unpacked. But when I use the functions like aregImpute the package is displaying coundn't find the function Where as in help.search it is giving that use of the function >

The survival package uses a generalized cholesky decompostition throughout.? If A is a symmetric matrix A= LDL' where L is lower triangular with 1s on the diagonal, D is diagonal, and D[i,i] =0 if column i of A is redundant.? Being able to read the rank and dependencies directly off of D is very handy. The bdsmatrix package uses the same, but exposes it to the user as gchol and solve

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 have timeseries data for some factors, and some missing values are there in those factors, I want impute those missing values without disturbing the distribution of that factor, and maintaining the correlation with other factors. Pl. suggest me some imputation methods. I tried some functions in R like aregImpute, transcan. After the imputation I am unable to retrive the data with imputed

I've converted the bdsmatrix package (used by coxme) to the newer style of S4, and uploaded it. It is in the pkg directory of survival, on r-forge. I'm stuck on something that is almost certainly a namespace issue. I've borrowed liberally from Matrix (a big help) and read the documentation, and just upgraded to 2.8.1 - but still stuck. All the tests work when I run them "by

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%...

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

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

I've added a "backsolve" method to the bdsmatrix library. Per the Extending manual section 7.1 I've also added the following 3 lines along with my setMethod definitions for 2 classes. backsolve <- function(r, ...) UseMethod("backsolve") backsolve.default <- base:::backsolve formals(backsolve.default) <- c(formals(backsolve.default), alist(... = )) I've

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'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 get the following error from one of my Rd files in R CMD check (R 2-14.0) * checking Rd files ... WARNING Error in switch(attr(block, "Rd_tag"), TEXT = if (!grepl("^[[:space:]]* $", : EXPR must be a length 1 vector problem found in ?backsolve.Rd? This is likely something that will be glaringly obvious once it's pointed out, but without a line number I can't