The R Project SVN R-packages

\name{lsparseMatrix-classes}

\docType{class}

\alias{lsparseMatrix-class}

\alias{lgCMatrix-class}

\alias{ltCMatrix-class}

\alias{lsCMatrix-class}

\alias{lgRMatrix-class}

\alias{ltRMatrix-class}

\alias{lsRMatrix-class}

\alias{lgTMatrix-class}

\alias{ltTMatrix-class}

\alias{lsTMatrix-class}

%

\alias{-,lsparseMatrix,missing-method}

\alias{!,lsparseMatrix-method}

\alias{\%*\%,lgCMatrix,lgCMatrix-method}

\alias{\%*\%,lsparseMatrix,lsparseMatrix-method}

\alias{\%*\%,lsparseMatrix,ldenseMatrix-method}

\alias{\%*\%,ldenseMatrix,lsparseMatrix-method}

\alias{chol,lsCMatrix,logical-method}

\alias{chol,lsCMatrix,missing-method}

\alias{coerce,lgCMatrix,dgCMatrix-method}

\alias{coerce,lgCMatrix,lgTMatrix-method}

\alias{coerce,lgCMatrix,lgeMatrix-method}

\alias{coerce,lgCMatrix,matrix-method}

\alias{coerce,lgTMatrix,dgTMatrix-method}

\alias{coerce,lgTMatrix,lgCMatrix-method}

\alias{coerce,lgTMatrix,matrix-method}

\alias{coerce,lsCMatrix,dgTMatrix-method}

\alias{coerce,lsCMatrix,dsCMatrix-method}

\alias{coerce,lsCMatrix,lgCMatrix-method}

\alias{coerce,lsCMatrix,lsTMatrix-method}

\alias{coerce,lsCMatrix,matrix-method}

\alias{coerce,ltCMatrix,dtCMatrix-method}

\alias{coerce,ltCMatrix,dMatrix-method}

\alias{coerce,ltCMatrix,lgCMatrix-method}

\alias{coerce,ltCMatrix,matrix-method}

\alias{coerce,matrix,lgCMatrix-method}

\alias{coerce,matrix,lgTMatrix-method}

\alias{coerce,matrix,ltCMatrix-method}

%

\alias{coerce,ltTMatrix,dtTMatrix-method}

\alias{coerce,ltTMatrix,lgTMatrix-method}

\alias{coerce,ltTMatrix,matrix-method}

%

\alias{crossprod,lgCMatrix,missing-method}

\alias{crossprod,lgTMatrix,missing-method}

\alias{crossprod,lsparseMatrix-method}

\alias{crossprod,lsparseMatrix,missing-method}

\alias{crossprod,lsparseMatrix,lsparseMatrix-method}

\alias{crossprod,lsparseMatrix,ldenseMatrix-method}

\alias{crossprod,ldenseMatrix,lsparseMatrix-method}

\alias{diag,lgCMatrix-method}

\alias{diag,lsparseMatrix-method}

\alias{image,lgCMatrix-method}

\alias{image,lsCMatrix-method}

\alias{image,ltCMatrix-method}

\alias{image,ltTMatrix-method}

\alias{t,lgCMatrix-method}

\alias{t,lgTMatrix-method}

\alias{t,lsCMatrix-method}

\alias{t,ltCMatrix-method}

\alias{tcrossprod,lgCMatrix,missing-method}

\alias{tcrossprod,lgTMatrix,missing-method}

\alias{tcrossprod,lsparseMatrix,missing-method}

\title{Sparse logical matrices}

\description{The \code{lsparseMatrix} class is a virtual class of sparse

  matrices with \code{TRUE}/\code{FALSE} entries.  Only the positions of the

  elements that are \code{TRUE} are stored.  These can be stored in the

  ``triplet'' form (classes \code{lgTMatrix}, \code{lsTMatrix}, and

  \code{ltTMatrix} which really contain pairs, not triplets) or in

  compressed column-oriented form (classes \code{lgCMatrix},

  \code{lsCMatrix}, and \code{ltCMatrix}) or in compressed row-oriented

  form (classes \code{lgRMatrix}, \code{lsRMatrix}, and

  \code{ltRMatrix}).  The second letter in the name of these non-virtual

  classes indicates \code{g}eneral, \code{s}ymmetric, or \code{t}riangular.

}

\section{Objects from the Class}{

  Objects can be created by calls of the form \code{new("lgCMatrix",

    ...)} and so on.  More frequently objects are created by coercion of

  a numeric sparse matrix to the logical form for use in

  the symbolic analysis phase

  of an algorithm involving sparse matrices.  Such algorithms often

  involve two phases: a symbolic phase wherein the positions of the

  non-zeros in the result are determined and a numeric phase wherein the

  actual results are calculated.  During the symbolic phase only the

  positions of the non-zero elements in any operands are of interest,

  hence any numeric sparse matrices can be treated as logical sparse

  matrices.

}

\section{Slots}{

  \describe{

    \item{\code{uplo}:}{Object of class \code{"character"}. Must be

      either "U", for upper triangular, and "L", for lower

      triangular. Present in the triangular and symmetric classes but not

      in the general class.}

    \item{\code{diag}:}{Object of class \code{"character"}. Must be

      either \code{"U"}, for unit triangular (diagonal is all ones), or

      \code{"N"} for non-unit.  The implicit diagonal elements are not

      explicitly stored when \code{diag} is \code{"U"}.  Present in the

      triangular classes only.}

    \item{\code{p}:}{Object of class \code{"integer"} of pointers, one

      for each column (row), to the initial (zero-based) index of elements in

      the column.  Present in compressed column-oriented and compressed

      row-oriented forms only.}

    \item{\code{i}:}{Object of class \code{"integer"} of length nnzero

      (number of non-zero elements).  These are the row numbers for

      each TRUE element in the matrix.  All other elements are FALSE.

      Present in triplet and compressed column-oriented forms only.}

    \item{\code{j}:}{Object of class \code{"integer"} of length nnzero

      (number of non-zero elements).  These are the column numbers for

      each TRUE element in the matrix.  All other elements are FALSE.

      Present in triplet and compressed column-oriented forms only.}

    \item{\code{Dim}:}{Object of class \code{"integer"} - the dimensions

      of the matrix.}

  }

}

\section{Methods}{

  \describe{

    \item{coerce}{\code{signature(from = "dgCMatrix", to = "lgCMatrix")}}

    \item{t}{\code{signature(x = "lgCMatrix")}: returns the transpose

      of \code{x}}

  }

}

%\references{}

%\author{}

%\note{}

\seealso{

  the class \code{\linkS4class{dgCMatrix}}

}

\examples{

m <- Matrix(c(0,0,2:0), 3,5, dimnames=list(LETTERS[1:3],NULL))

(dm <- as(m, "dgCMatrix"))# no dimnames for sparse here

(lm <- as(dm, "lgCMatrix"))

str(lm) # no 'x' slot

!lm     # no longer sparse

data(KNex)

lmm <- as(KNex $ mm, "lgCMatrix")

xlx <- crossprod(lmm)

image(xlx, main=paste("crossprod(lmm) : Sparse", class(xlx)))

}

\keyword{classes}

\keyword{algebra}