The R Project SVN R-packages

\name{sparseMatrix-class}
\docType{class}
\title{Virtual Class "sparseMatrix" --- Mother of Sparse Matrices}
\alias{sparseMatrix-class}
%% Group methods
\alias{Math,sparseMatrix-method}
%\alias{Math2,sparseMatrix,numeric-method}
\alias{Ops,sparseMatrix,sparseMatrix-method}
\alias{Ops,sparseMatrix,numeric-method}
\alias{Ops,numeric,sparseMatrix-method}
\alias{Ops,diagonalMatrix,sparseMatrix-method}
\alias{Ops,sparseMatrix,diagonalMatrix-method}
%
\alias{cbind2,sparseMatrix,sparseMatrix-method}
\alias{cbind2,sparseMatrix,matrix-method}
\alias{cbind2,matrix,sparseMatrix-method}
\alias{cbind2,sparseMatrix,numeric-method}
\alias{cbind2,numeric,sparseMatrix-method}
\alias{rbind2,sparseMatrix,sparseMatrix-method}
\alias{rbind2,sparseMatrix,matrix-method}
\alias{rbind2,matrix,sparseMatrix-method}
\alias{rbind2,sparseMatrix,numeric-method}
\alias{rbind2,numeric,sparseMatrix-method}
%
\alias{coerce,ANY,sparseMatrix-method}
\alias{coerce,factor,sparseMatrix-method}
\alias{coerce,sparseMatrix,generalMatrix-method}
\alias{coerce,sparseMatrix,symmetricMatrix-method}
\alias{coerce,sparseMatrix,triangularMatrix-method}
\alias{-,sparseMatrix,missing-method}
\alias{cov2cor,sparseMatrix-method}
\alias{diag,sparseMatrix-method}
\alias{dim<-,sparseMatrix-method}
\alias{lu,sparseMatrix-method}
\alias{print,sparseMatrix-method}
\alias{show,sparseMatrix-method}
\alias{summary,sparseMatrix-method}
\alias{norm,sparseMatrix,character-method}
\alias{rcond,sparseMatrix,character-method}
%\alias{colMeans,..} etc are now in ./colSums.Rd
% graph stuff
\alias{coerce,graph,sparseMatrix-method}
\alias{coerce,graph,CsparseMatrix-method}
\alias{coerce,graphAM,sparseMatrix-method}
\alias{coerce,graphNEL,CsparseMatrix-method}
\alias{coerce,graphNEL,TsparseMatrix-method}
\alias{coerce,sparseMatrix,graph-method}
\alias{coerce,sparseMatrix,graphNEL-method}
\alias{coerce,TsparseMatrix,graphNEL-method}
\alias{coerce,dgTMatrix,graphNEL-method}
%
\description{Virtual Mother Class of All Sparse Matrices}
\section{Slots}{
  \describe{
    \item{\code{Dim}:}{Object of class \code{"integer"} - the dimensions
     of the matrix - must be an integer vector with exactly two
     non-negative values.}
    \item{\code{Dimnames}:}{a list of length two - inherited from class
      \code{Matrix}, see \code{\linkS4class{Matrix}}.}
    \item{\code{factors}:}{Object of class \code{"list"} - a list
      of factorizations of the matrix. }
  }
}
\section{Extends}{
  Class \code{"Matrix"}, directly.
}
\section{Methods}{
  \describe{
    \item{show}{\code{(object = "sparseMatrix")}: The
      \code{\link{show}} method for sparse matrices prints
      \emph{\dQuote{structural}} zeroes as \code{"."} using
      \code{\link{printSpMatrix}()} which allows further customization.}
    \item{print}{\code{signature(x = "sparseMatrix")}, ....\cr
      The \code{\link{print}} method for sparse matrices by default is the
      same as \code{show()} but can be called with extra optional
      arguments, see \code{\link{printSpMatrix}()}.}
    \item{summary}{\code{(object = "sparseMatrix")}: Returns
      an object of S3 class \code{"sparseSummary"} which is basically a
      \code{\link{data.frame}} with columns \code{(i,j,x)} with the
      non-zero entries.   The \code{\link{print}} method resemble's
      Matlab's way of printing sparse matrices.}
    \item{diag}{\code{(x = "sparseMatrix")}: extracts the diagonal of a
      sparse matrix.}
    \item{dim<-}{\code{signature(x = "sparseMatrix", value = "ANY")}:
      allows to \emph{reshape} a sparse matrix to a sparse matrix with
      the same entries but different dimensions. \code{value} must be of
      length two and fulfill \code{prod(value) == prod(dim(x))}.}
    \item{coerce}{\code{signature(from = "factor", to = "sparseMatrix")}:
      Coercion of a factor to \code{"sparseMatrix"} produces the matrix
      of indicator \bold{rows} stored as an object of class
      \code{"dgCMatrix"}.  To obtain columns representing the interaction
      of the factor and a numeric covariate, replace the \code{"x"} slot
      of the result by the numeric covariate then take the transpose.}
  }
  See also \code{\link{colSums}}, ... %% FIXME
  for methods with separate help pages.
}
\note{
  In method selection for multiplication operations (i.e. \code{\%*\%}
  and the two-argument form of \code{\link[base]{crossprod}})
  the sparseMatrix class takes precedence in the sense that if one
  operand is a sparse matrix and the other is any type of dense matrix
  then the dense matrix is coerced to a \code{dgeMatrix} and the
  appropriate sparse matrix method is used.
}
%\author{Martin}
\examples{
showClass("sparseMatrix") ## and look at the help() of its subclasses
M <- Matrix(0, 10000, 100)
M[1,1] <- M[2,3] <- 3.14
M  ## show(.) method suppresses printing of the majority of rows

## factor -> t( <sparse design matrix> ) :
(fact <- gl(5, 3, 30, labels = LETTERS[1:5]))
(Xt <- as(fact, "sparseMatrix"))  # indicator rows
X <- t(Xt)
}
\keyword{classes}