similar to: setGeneric?

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,

I've installed R-devel R Under development (unstable) (2014-07-09 r66111) Platform: x86_64-apple-darwin13.1.0 (64-bit) and am trying to resolve some problems that I am seeing with my SparseM package. In prior versions I explicitly had: setGeneric("image", function(x, ...) standardGeneric("image")) and then used setMethod to define a method for the class matrix.csr but

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

I would like to add a class to the SparseM package. I have a class "matrix.csr" that describes a matrix in compressed sparse row format, now I would like a class matrix.diag.csr that describes such objects when they happen to be diagonal. The idea is that matrix.diag.csr objects should behave (later in life) exactly like matrix.csr objects, the distinction is only needed in order to

I'm still having difficulties with methods...is the following behavior expected? (This is without loading any libraries) R : Copyright 2003, The R Development Core Team Version 1.7.0 Under development (unstable) (2003-03-31) > ls() character(0) > > diag(2) [,1] [,2] [1,] 1 0 [2,] 0 1 > setGeneric("diag") [1] "diag" > diag(2) Error in

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:

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

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 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

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")

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, 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,

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 #

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 =

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 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

I am trying to use the lnam autocorrelation model from the SNA package. I have it running for smaller adjacency matrices (<1,500) it works just fine but when my matrices are bigger 4000+. I get the error: > lnam1_01.adj<- lnam(data01$adopt,x01,ec2001.csr) Error in optim(rho, n2ll.rho, method = method, control = control, beta = parm$beta, : initial value in 'vmmin' is not

Dear all, I can't see how setGeneric() works. I know perfectly well how to define library(methods); setClass("Square", representation(side="numeric")); # Will become the default method. getArea <- function(object) { stop(paste("Method getArea() is not defined for this class:", data.class(object))); } setMethod("getArea",

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