similar to: SparseM and Stepwise Problem

Could someone advise me about how to react to the message: * checking R code for possible problems ... NOTE slm: no visible binding for global variable 'response' from R CMD check SparseM with * using R version 2.6.0 Under development (unstable) (2007-09-03 r42749) The offending code looks like this: "slm" <- function (formula, data, weights, na.action, method =

I am an R user trying to get around the 2Gig memory limit in Windows, so here I am days later with a working Ubuntu, and R under Ubuntu. But - the memory problems seem worse than ever. R code that worked under windows fails, unable to allocate memory. Searching around the web, it appears that the problem may be the ability to find contguous memory for my big vectors, but a fresh boot of

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

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! 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

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 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

iterations <- 100 nvars <- 4 combined <- rbind(scaleMiceTrain, scaleMiceTest) reducedSample <- combined reducedSample <- subset(reducedSample, select = -pID50) reducedSample <- subset(reducedSample, select = -id) for (i in 1:iterations) { miceSample <- sample(combined[,-c(1,2)],nvars, replace=FALSE) miceSample$pID50 <- combined$pID50 miceTestSample <-

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 #

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'm hoping that someone with deeper insight into S4 than I, that is to say virtually everyone reading this list, could help resolve the following problem in SparseM. We have setGeneric("backsolve", function(r, x, k = NULL, upper.tri = NULL, transpose = NULL, twice = TRUE, ...) standardGeneric("backsolve"), useAsDefault= function(r, x,

Hello Andy, Thank you for your offer to work together on implementing the your new scheduler on X86. I can start working on this right away. In case you were unaware, the new Silvermont micro-architecture is only out of order on the integer side. The SSE instructions are still in order, so the current postRA scheduler is very beneficial for code with lots of SSE instructions, such as the ISPC

Hi All, I created a two variable lm() model slm<-lm(y[1:3000,8]~y[1:3000,12]+y[1:3000,15]) I made two predictions predict(slm,newdata=y[201:3200,]) predict(slm,newdata=y[601:3600,]) there is no error message for either of these. the results are identical, and identical to slm$fitted as well. if this is not the right way to apply the model coefficients to a new set of inputs, what is

Dear R list, members of my course have encountered the following error message: > slm <- lm(price ~ engsize, autoframe) Error in get(x, envir, mode, inherits) : variable "FUN" was not found [more context is given in the fuller listing below]. Once the error is encountered it seems to persist; for example early in one session: > summary(blin.fit) Call: lm(formula = Response

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

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 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,

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.