similar to: Problem installing SparseM on Debian stable

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

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

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

Hello, I'm working with a very large, very sparse X matrix. Let csr.X <- * as.matrix.csr*(X) as described by the SparseM package. The documentation says that "Indexing .... work just like they do on dense matrices". To me this says that I should be able to perform operations on the rows of csr.X in the same way I would on X itself. E.g. f <- function(x){ for (i in 1:n){

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"),

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

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

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

I have recently started using R and want to use the quantreg (and sparseM) packages. I downloaded the .tar files for each, and placed the subsequent folders into the library folder in the frameworks/R.framework/ resources/library folder with all the other packages. When I try to load either package from the package manager window I get: Loading required package: SparseM Error in

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

Hello, I am trying to load the package SparseM. It seems that I have successfully installed SparseM (version 0.78), but I did not succeed in loading the SparseM package into R 2.7. Does anybody know a trick for loading SparseM? Thanks in advance, Heike > library(SparseM,lib.loc=my.lib.loc) Error in packageDescription(pkg)$Version : $ operator is invalid for atomic vectors In addition:

I'm having trouble loading the package SparseM in R 1.8.1, OS = Windows 2000. Installing appeared to go well; I saw no error messages, html documentation was installed, and "installed.packages()" lists SparseM among the installed packages. When I try to load the library, however, I get the following: > library(SparseM) Error in slot(mlist, "argument") : Can't get

Hi, I am trying out the SparseM package and had the a question. The following piece of code works fine: ... fit = slm(model, data = trainData, weights = weight) ... But how do I use the fit object to predict the values on say a reserved testDataSet? In the regular lm function I would do something like this: predict.lm(fit,testDataSet) Thanks -Bala

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

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.

Hi, I have just installed R 2.4.0 and when I try to load SpareseM, I get the following error message library(SparseM) Package SparseM (0.71) loaded. To cite, see citation("SparseM") Error in loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'SparseM' methods specified for export, but none defined: as.matrix.csr, as.matrix.csc,

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

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: