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\name{lsparseMatrix-class}
\title{Sparse logical matrices}
%
\docType{class}
\keyword{array}
\keyword{classes}
%
\alias{lsparseMatrix-class}
\alias{lsparseMatrix-classes} % used by package 'enhancerHomologSearch'
\alias{lgCMatrix-class}
\alias{lgRMatrix-class}
\alias{lgTMatrix-class}
\alias{ltCMatrix-class}
\alias{ltRMatrix-class}
\alias{ltTMatrix-class}
\alias{lsCMatrix-class}
\alias{lsRMatrix-class}
\alias{lsTMatrix-class}
% lsparse
\alias{!,lsparseMatrix-method}
\alias{-,lsparseMatrix,missing-method}
\alias{Arith,lsparseMatrix,Matrix-method}
\alias{Logic,lsparseMatrix,ldenseMatrix-method}
\alias{Logic,lsparseMatrix,lsparseMatrix-method}
\alias{Ops,lsparseMatrix,lsparseMatrix-method}
\alias{Ops,lsparseMatrix,nsparseMatrix-method}
\alias{coerce,matrix,lsparseMatrix-method}
\alias{coerce,vector,lsparseMatrix-method}
\alias{which,lsparseMatrix-method}
% lgC
\alias{Arith,lgCMatrix,lgCMatrix-method}
\alias{Logic,lgCMatrix,lgCMatrix-method}
% lgR
% lgT
\alias{Arith,lgTMatrix,lgTMatrix-method}
\alias{Logic,lgTMatrix,lgTMatrix-method}
% ltC
\alias{Logic,ltCMatrix,ltCMatrix-method}
% ltR
% ltT
% lsC
\alias{Logic,lsCMatrix,lsCMatrix-method}
% lsR
% lsT
%
\description{The \code{lsparseMatrix} class is a virtual class
of logical sparse matrices, i.e., sparse matrices with entries
\code{TRUE}, \code{FALSE}, or \code{NA}.
These can be stored in the \dQuote{triplet} form (class
\code{\linkS4class{TsparseMatrix}}, subclasses \code{lgTMatrix},
\code{lsTMatrix}, and \code{ltTMatrix}) or in compressed
column-oriented form (class \code{\linkS4class{CsparseMatrix}},
subclasses \code{lgCMatrix}, \code{lsCMatrix}, and \code{ltCMatrix})
or--\emph{rarely}--in compressed row-oriented form (class
\code{\linkS4class{RsparseMatrix}}, subclasses \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, e.g. in an expression
\code{x != 0}.
The logical form is also used 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.
}
\details{
Note that triplet stored (\code{\linkS4class{TsparseMatrix}}) matrices
such as \code{lgTMatrix} may contain duplicated pairs of indices
\eqn{(i,j)} as for the corresponding numeric class
\code{\linkS4class{dgTMatrix}} where for such pairs, the corresponding
\code{x} slot entries are added. For logical matrices, the \code{x}
entries corresponding to duplicated index pairs \eqn{(i,j)} are
\dQuote{added} as well if the addition is defined as logical \eqn{or},
i.e., \dQuote{\code{TRUE + TRUE |-> TRUE}} and
\dQuote{\code{TRUE + FALSE |-> TRUE}}.
Note the use of \code{\link{asUniqueT}()} for getting an internally
unique representation without duplicated \eqn{(i,j)} entries.
}
\section{Slots}{
\describe{
\item{\code{x}:}{Object of class \code{"logical"}, i.e., either
\code{TRUE}, \code{\link{NA}}, or \code{FALSE}.}
\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 row-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}}
\item{which}{\code{signature(x = "lsparseMatrix")}, semantically
equivalent to \pkg{base} function \code{\link{which}(x, arr.ind)};
for details, see the \code{\linkS4class{lMatrix}} class documentation.}
}
}
\seealso{
the class \code{\linkS4class{dgCMatrix}} and \code{\linkS4class{dgTMatrix}}
}
\examples{
\dontshow{ % for R_DEFAULT_PACKAGES=NULL
library(utils, pos = "package:base", verbose = FALSE)
}
(m <- Matrix(c(0,0,2:0), 3,5, dimnames=list(LETTERS[1:3],NULL)))
(lm <- (m > 1)) # lgC
!lm # no longer sparse
stopifnot(is(lm,"lsparseMatrix"),
identical(!lm, m <= 1))
data(KNex, package = "Matrix")
str(mmG.1 <- (KNex $ mm) > 0.1)# "lgC..."
table(mmG.1@x)# however with many ``non-structural zeros''
## from logical to nz_pattern -- okay when there are no NA's :
nmG.1 <- as(mmG.1, "nMatrix") # <<< has "TRUE" also where mmG.1 had FALSE
## from logical to "double"
dmG.1 <- as(mmG.1, "dMatrix") # has '0' and back:
lmG.1 <- as(dmG.1, "lMatrix")
stopifnot(identical(nmG.1, as((KNex $ mm) != 0,"nMatrix")),
validObject(lmG.1),
identical(lmG.1, mmG.1))
class(xnx <- crossprod(nmG.1))# "nsC.."
class(xlx <- crossprod(mmG.1))# "dsC.." : numeric
is0 <- (xlx == 0)
mean(as.vector(is0))# 99.3\% zeros: quite sparse, but
table(xlx@x == 0)# more than half of the entries are (non-structural!) 0
stopifnot(isSymmetric(xlx), isSymmetric(xnx),
## compare xnx and xlx : have the *same* non-structural 0s :
sapply(slotNames(xnx),
function(n) identical(slot(xnx, n), slot(xlx, n))))
}
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