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\name{conversions}
\alias{conversions}
\alias{as.SparseSimilarityMatrix}
\alias{as.DenseSimilarityMatrix}
\alias{as.SparseSimilarityMatrix-methods}
\alias{as.DenseSimilarityMatrix-method}
\alias{as.SparseSimilarityMatrix,matrix-method}
\alias{as.DenseSimilarityMatrix,matrix-method}
\alias{as.SparseSimilarityMatrix,sparseMatrix-method}
\alias{as.DenseSimilarityMatrix,sparseMatrix-method}
\alias{as.SparseSimilarityMatrix,Matrix-method}
\alias{as.DenseSimilarityMatrix,Matrix-method}
\title{Conversions Between Dense and Sparse Similarity Matrices}
\description{
Converts a dense similarity matrix into a sparse one or vice versa
}
\usage{
\S4method{as.SparseSimilarityMatrix}{matrix}(s, lower=-Inf)
\S4method{as.SparseSimilarityMatrix}{Matrix}(s, lower=-Inf)
\S4method{as.SparseSimilarityMatrix}{sparseMatrix}(s, lower=-Inf)
\S4method{as.DenseSimilarityMatrix}{matrix}(s, fill=-Inf)
\S4method{as.DenseSimilarityMatrix}{Matrix}(s, fill=-Inf)
\S4method{as.DenseSimilarityMatrix}{sparseMatrix}(s, fill=-Inf)
}
\arguments{
\item{s}{a similarity matrix in sparse or dense format (see details
below)}
\item{lower}{cut-off threshold to apply when converting similarity
matrices into sparse format. All similarities lower than or equal to
\code{lower} will be omitted from the result. The default is
\code{-Inf}), i.e. only \code{-Inf} values are removed.}
\item{fill}{value to fill in for entries that are missing from sparse
similarity matrix 's' (defaults to \code{-Inf}).}
}
\details{
The function \code{as.SparseSimilarityMatrix} takes a matrix argument,
removes all diagonal elements and all values that are lower than or
equal to the cut-off threshold \code{lower} and returns a sparse
matrix of class \code{\linkS4class{dgTMatrix}}.
If the function \code{as.DenseSimilarityMatrix} is called for a
sparse matrix (class \code{\linkS4class{sparseMatrix}} or any
class derived from this class), a dense matrix is returned, where all
values that were missing in the sparse matrix are replaced with
\code{fill}.
\code{as.DenseSimilarityMatrix} can also be called for dense
\code{\link{matrix}} and \code{\linkS4class{Matrix}} objects.
In this case, \code{as.DenseSimilarityMatrix} assumes that the
matrices have three columns that encode for a sparse matrix
in the same way as the Matlab implementation of Frey's and Dueck's
sparse affinity propagation accepts it:
the first column contains 1-based row indices, the second column
contains 1-based column indices, and the third column contains the
similarity values. The same format is also accepted by
\code{as.SparseSimilarityMatrix} to convert a sparse similarity matrix
of this format into a \code{\linkS4class{dgTMatrix}} object.
Note that, for matrices of this format,
\code{as.DenseSimilarityMatrix} replaces the deprectated function
\code{sparseToFull} that was used in older versions of the package.
Note that \code{as.SparseSimilarityMatrix} and
\code{as.DenseSimilarityMatrix} are no S4 coercion methods.
There are no classes named \code{SparseSimilarityMatrix}
or \code{DenseSimilarityMatrix}.
}
\value{returns a square similarity matrix in sparse format (class
\code{\linkS4class{dgTMatrix}} or in dense format (standard class
\code{\link{matrix}}).}
\author{Ulrich Bodenhofer}
\references{\url{https://github.com/UBod/apcluster}
Frey, B. J. and Dueck, D. (2007) Clustering by passing messages
between data points. \emph{Science} \bold{315}, 972-976.
DOI: \doi{10.1126/science.1136800}.
Bodenhofer, U., Kothmeier, A., and Hochreiter, S. (2011)
APCluster: an R package for affinity propagation clustering.
\emph{Bioinformatics} \bold{27}, 2463-2464.
DOI: \doi{10.1093/bioinformatics/btr406}.
}
\examples{
## create similarity matrix in sparse format according to Frey and Dueck
sp <- matrix(c(1, 2, 0.5, 3, 1, 0.2, 5, 4, -0.2, 3, 4, 1.2), 4, 3, byrow=TRUE)
sp
## perform conversions
as.DenseSimilarityMatrix(sp, fill=0)
as.SparseSimilarityMatrix(sp)
## create dense similarity matrix
cl1 <- cbind(rnorm(20, 0.2, 0.05), rnorm(20, 0.8, 0.06))
cl2 <- cbind(rnorm(20, 0.7, 0.08), rnorm(20, 0.3, 0.05))
x <- rbind(cl1, cl2)
sim <- negDistMat(x, r=2)
ssim <- as.SparseSimilarityMatrix(sim, lower=-0.2)
## run apcluster() on the sparse similarity matrix
apres <- apcluster(ssim, q=0)
apres
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{cluster}
|
