search for: irlba

When running irlba on a big.matrix as in the example from the vignette (on Windows R 2.15.2 64 bit) it returns with an error (see below). irlba works fine on a regular R matrix > library(bigalgebra) > library(irlba) Loading required package: Matrix Loading required package: lattice > matmul <- function...

Hello, good mornig, i have one questions, anybody know how to calculate svd of a matrix of the library bigmatrix with library irlba? thanks [[alternative HTML version deleted]]

Dear All, I need to perform a SVD on a very large data matrix, of dimension ~ 500,000 x 1,000 , and I am looking for an efficient algorithm that can perform an approximate (partial) SVD to extract on the order of the top 50 right and left singular vectors. Would be very grateful for any advice on what R-packages are available to perform such a task, what the RAM requirement is, and indeed what

This is kind of like asking for a solution to x+1=x+1. Go back to linear algebra and look up Singular Value Decomposition, and decide if you really want to proceed. See also ?svd and package irlba. -- Sent from my phone. Please excuse my brevity. On April 20, 2016 4:22:34 AM PDT, A A via R-help <r-help at r-project.org> wrote: > > > >I have a situation in R where I would like to find any x (if one >exists) that solves the linear system of equations Ax = b, where A is &...

Dear R-devel readers: I am really stuck trying resolving an issue with the use of the Matrix in one of my packages, irlba, with R-2.14.0. When I use crossprod with at least one sparse argument in the packaged code I receive the error: Error in crossprod(x, y) : requires numeric/complex matrix/vector arguments However, when I run the code outside of the context of the package it works fine. I have constructed the...

I have a situation in R where I would like to find any x (if one exists) that solves the linear system of equations Ax = b, where A is square, sparse, and singular, and b is a vector. Here is some code that mimics my issue with a relatively simple A and b, along with three other methods of solving this system that I found online, two of which give me an error and one of which succeeds on the

Hi everyone, I am using eigen() to extract the 2 major eigenpairs from a large real square symmetric matrix. The procedure is already rather efficient, but becomes somehow slow for real time needs with moderately large matrices (few thousand lines). The R implementation statically extracts all eigenvalues (and optionally associated eigenvectors). I heard about optimizations of the eigen

Dear Sir/Madam, I have a large data set of 10,17,289 observations of 10,830 variables. I need to use PCA to reduce the dimension of dataset. I have already tried irlba, prcomp and nsprcomp packages in R but couldn't do for huge data sets. i.e pc <- prcomp_irlba(sparseYY[1:5000,], n=50, retx = TRUE, center = TRUE, scale. = FALSE) able to get only few PCs for 5000 rows only so can you please help me what package can i use to do PCA in R for large dataset?...