similar to: Indexing with sparse matrices (SparseM)

I've been trying to get some linear classifiers (LiblineaR, kernlab, e1071) to work with a sparse matrix of feature data. In the case of LiblineaR and kernlab, it seems I have to coerce my data into a dense matrix in order to train a model. I've done a number of searches, read through the manuals and vignettes, but I can't seem to see how to use either of these packages with sparse

Hi there, I'm having a problem when trying to create a large matrix (1,000,000 x 1,000,000) of the .csr type (package 'SparseM'). > k <- rep(0,1000000) > tmp <- length(k) > tmp2 <- as.matrix.csr(0,tmp,tmp) Error in if (length(x) == nrow * ncol) x <- matrix(x, nrow, ncol) else { : missing value where TRUE/FALSE needed Warning message: In nrow * ncol : NAs

Hi all, I want to transform a dCgMatrix from package Matrix into a matrix.csr from package SparseM, and I found out this link : http://stat.ethz.ch/R-manual/R-devel/library/Matrix/html/SparseM-conv.html But there's no informaion about usage/description/arguments, so how do I use this SparseM-conversions method ?? Is it a function ?? By the way I already tried function: as.spam.matrix.csr

I have troubles installing the "SparseM" package on my Debian stable Linux system. Debian's version of R is: platform i386-pc-linux-gnu arch i386 os linux-gnu system i386, linux-gnu status major 1 minor 5.1 year 2002 month 06 day 17 language R This is the installation output: > R CMD INSTALL -l /usr/lib/R/ SparseM_0.36.tar.gz * Installing

Full_Name: Julien Gagneur Version: 2.2.1 OS: Linux (Suse 9.3) Submission from: (NULL) (194.94.44.4) Using the SparseM library (SparseM_0.66) and the e1071 library (e1071_1.5-12) I fail using svm method with a sparse matrix. Here is a sample example. I experienced the same problem under Windows. > library(SparseM) [1] "SparseM library loaded" > library("e1071")

Hello all, I am trying to use the "svm" method provided by e1071 (Version: 1.5-16) together with a matrix provided by the SparseM package (Version: 0.73) but it fails with this message: > model <- svm(lm, lv, scale = TRUE, type = 'C-classification', kernel = 'linear') Error in t.default(x) : argument is not a matrix although lm was created before with

read.matrix.csr does not close the connection: > library('SparseM') Package SparseM (0.96) loaded. > read.matrix.csr(foo) ... Warning message: closing unused connection 3 (foo) > -- Sam Steingold (http://sds.podval.org/) on Ubuntu 12.04 (precise) X 11.0.11103000 http://www.childpsy.net/ http://truepeace.org http://camera.org http://pmw.org.il http://think-israel.org

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

I have been running into difficulties with dispatching on an S4 class defined in the SparseM package, when the method calls are inside a function passed as the f= argument to optimize() in functions in the spdep package. The S4 methods are typically defined as: setMethod("det","matrix.csr", function(x, ...) det(chol(x))^2) that is within setMethod() rather than by name before

Hi! Would like to initialize a huge matrix.csc (Pacakge SparseM) with all elements 0 and afterwards set a few alements nonzero. The matrix which I like to allocate is so huge that I can not use A <- matrix(a,n1,p) before: A.csr <- as.matrix.csc(A) because I can not allocate such a huge matrix A. But I believe that the much more memmory efficient model in case of csc matrix should do it for

Hi, I am writing some basic smoothers in R for cleaning some spectral data. I wanted to see if I could get close to matlab for speed, so I was trying to compare SparseM with Matrix to see which could do the choleski decomposition the fastest. Here is the function using SparseM difsm <- function(y, lambda, d){ # Smoothing with a finite difference penalty # y: signal to be smoothed #

Full_Name: David Pleydell Version: 2.2.1 OS: Debian Etch Submission from: (NULL) (193.55.70.206) There appears to be a conflict between the chol functions from the Matrix and the SparseM packages. chol() can only be applied to a matrix of class dspMatrix if SparseM is not in the path. with gratitude David > library(Matrix) > sm <- as(as(Matrix(diag(5) + 1), "dsyMatrix"),

I'm trying to use stepAIC on sparse matrices, and I need some help. The documentation for slm.fit suggests: slm.fit and slm.wfit call slm.fit.csr to do Cholesky decomposition and then backsolve to obtain the least squares estimated coefficients. These functions can be called directly if the user is willing to specify the design matrix in matrix.csr form. This is often advantageous in large

In the last few days I've received couple of messages pointing out that our SparseM package fails to install on the patched version of 1.7.0. Laurent Gaultier kindly suggested that replacing: setGeneric("as.matrix.csr") by setGeneric("as.matrix.csr", function(x, nrow, ncol, eps) standardGeneric("as.matrix.csr")) was sufficient to fix the problem.

I'm trying to resolve a small problem that has arisen from introducing a NAMESPACE for the package SparseM. Prior to the namespace I had a class "matrix.diag.csr" that consisted of diagonal sparse matrices. It was defined to have the same attributes as the matrix.csr class and setAs was used to define how to coerce integers and vectors into this form:

I read somewhere (help list, documentation) that the random effects in lme4 uses sparse matrix "technology". I'd like to confirm with others that I can't use a sparse matrix as a fixed effect? I'm getting an "Invalid type (S4) " error. Thanks. ~~~~~~~~~~~~~~~~~~~ -Robin Jeffries Dr.P.H. Candidate in Biostatistics UCLA School of Public Health rjeffries@ucla.edu

Dear all, I just installed the new version of R, 2.10.1, and I am currently using the package sparseM. (I also use a 64 bit windows version) I got a problem that I never had: when I try to multiply with a kronecker product (%x%) two sparse matrixes I get the following message: Error in dim(x) <- length(x) : invalid first argument I never had this problem with previous versions of R. May

Dear all, I'm training an SVM with default settings on a matrix csr (SparseM package). I realized that if I train the SVM with the (hopefully) equivalent matrix (Matrix package) representation, the returned models and predictions sometimes differ. I expected both representations of the same data to lead to the same results though. It could be that it is a scaling problem, because unscaled

all of a sudden, after a SparseM upgrade(?) I get this error: > str(z) Formal class 'matrix.csr' [package "SparseM"] with 4 slots ..@ ra : num [1:85372672] -0.4288 0.0397 0.0104 -0.1843 -0.1203 ... ..@ ja : int [1:85372672] 1 2 3 4 5 6 7 8 9 10 ... ..@ ia : int [1:699777] 1 123 245 367 489 611 733 855 977 1099 ... ..@ dimension: int [1:2] 699776 122

Dear List: I'm working to construct a very large sparse matrix and have found relief using the SparseM package. I have encountered an issue that is confusing to me and wonder if anyone may be able to suggest a smarter solution. The matrix I'm creating is a covariance matrix for a larger research problem that is subsequently used in a simulation. Below is the latex form of the matrix if