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\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{mean,sparseMatrix-method}
\alias{print,sparseMatrix-method}
\alias{show,sparseMatrix-method}
\alias{summary,sparseMatrix-method}
\alias{norm,sparseMatrix,character-method}
\alias{determinant,dgCMatrix,logical-method}
\alias{determinant,dsparseMatrix,logical-method}
\alias{determinant,dtCMatrix,logical-method}
\alias{determinant,sparseMatrix,missing-method}
\alias{determinant,sparseMatrix,logical-method}
% "[" are in ./Xtrct-methods.Rd
%\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}}.}
}
}
\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)} (or just
\code{(i,j)} for \code{\linkS4class{nsparseMatrix}} class objects)
with the stored (typically non-zero) entries. The
\code{\link{print}} method resembles Matlab's way of printing
sparse matrices, and also the MatrixMarket format, see
\code{\link{writeMM}}.}
\item{determinant}{\code{(x = "sparseMatrix", logarithm=TRUE)}:
\code{\link{determinant}()} methods for sparse matrices typically
work via \code{\link{Cholesky}} or \code{\link{lu}} decompositions.}
\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.
Missing values (\code{\link{NA}}) from the factor are translated
to columns of all \code{0}s.}
}
See also \code{\link{colSums}}, \code{\link{norm}},
... %% 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
data(CAex); dim(CAex) # 72 x 72 matrix
determinant(CAex) # works via sparse lu(.)
## factor -> t( <sparse design matrix> ) :
(fact <- gl(5, 3, 30, labels = LETTERS[1:5]))
(Xt <- as(fact, "sparseMatrix")) # indicator rows
## missing values --> all-0 columns:
f.mis <- fact
i.mis <- c(3:5, 17)
is.na(f.mis) <- i.mis
Xt != (X. <- as(f.mis, "sparseMatrix")) # differ only in columns 3:5,17
stopifnot(all(X.[,i.mis] == 0), all(Xt[,-i.mis] == X.[,-i.mis]))
}
\keyword{classes}
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