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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 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 [3,] 0 0 3 > Cholesky(A) Error in function (classes, fdef, mtable) : unable to find an inherited method for function &qu...

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

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

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]]

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

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

Dear list: I have tried MASS's mca function and SAS's PROC corresp on the farms data (included in MASS, also used as mca's example), the results are different: R: mca(farms)$rs: 1 2 1 0.059296637 0.0455871427 2 0.043077902 -0.0354728795 3 0.059834286 0.0730485572 4 0.059834286 0.0730485572 5 0.012900181 -0.0503121890 6

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

I am learning R, and although I have looked in the documentation, I may be asking something obvious. Sorry, if that is the case. In a split-plot design if there is a missing subunit summary gives me a table with two rows for the same factor, one in the error within section and one in the section using error between units. With no data missing the table is "normal". How does one