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\name{APResult-class}
\docType{class}
\alias{APResult-class}
\alias{APResult}
\alias{apresult}
\alias{similarity}
\alias{[,APResult,index,missing,missing-method}
\alias{[[,APResult,index,missing-method}
\alias{length,APResult-method}
\alias{similarity,APResult-method}
\title{Class "APResult"}
\description{S4 class for storing results of affinity propagation
clustering. It extends the class \code{\linkS4class{ExClust}}.}
\section{Objects}{
Objects of this class can be created by calling \code{\link{apcluster}}
or \code{\link{apclusterL}} for a given similarity matrix or calling
one of these procedures with a data set and a similarity measure.
}
\section{Slots}{
The following slots are defined for \link{APResult} objects. Most names
are taken from Frey's and Dueck's original Matlab package:
\describe{
\item{\code{sweeps}:}{number of times leveraged clustering ran with
different subsets of samples}
\item{\code{it}:}{number of iterations the algorithm ran}
\item{\code{p}:}{input preference (either set by user or
computed by \code{\link{apcluster}} or
\code{\link{apclusterL}})}
\item{\code{netsim}:}{final total net similarity, defined as the
sum of \code{expref} and \code{dpsim}
(see below)}
\item{\code{dpsim}:}{final sum of similarities of data points to
exemplars}
\item{\code{expref}:}{final sum of preferences of the identified
exemplars}
\item{\code{netsimLev}:}{total net similarity of the individual
sweeps for leveraged clustering; only
available for leveraged clustering}
\item{\code{netsimAll}:}{vector containing the total net similarity
for each iteration; only available if
\code{\link{apcluster}} was called with
\code{details=TRUE}}
\item{\code{exprefAll}:}{vector containing the sum of preferences
of the identified exemplars
for each iteration; only available if
\code{\link{apcluster}} was called with
\code{details=TRUE}}
\item{\code{dpsimAll}:}{vector containing the sum of similarities
of data points to exemplars
for each iteration; only available if
\code{\link{apcluster}} was called with
\code{details=TRUE}}
\item{\code{idxAll}:}{matrix with sample-to-exemplar indices
for each iteration; only available if
\code{\link{apcluster}} was called with
\code{details=TRUE}}
}
}
\section{Extends}{
Class \code{"ExClust"}, directly.
}
\section{Methods}{
\describe{
\item{plot}{\code{signature(x="APResult")}: see
\code{\link{plot-methods}}}
\item{plot}{\code{signature(x="ExClust", y="matrix")}: see
\code{\link{plot-methods}}}
\item{heatmap}{\code{signature(x="ExClust")}: see
\code{\link{heatmap-methods}}}
\item{heatmap}{\code{signature(x="ExClust", y="matrix")}: see
\code{\link{heatmap-methods}}}
\item{show}{\code{signature(object="APResult")}: see
\code{\link{show-methods}}}
\item{labels}{\code{signature(object="APResult")}: see
\code{\link{labels-methods}}}
\item{cutree}{\code{signature(object="APResult")}: see
\code{\link{cutree-methods}}}
\item{length}{\code{signature(x="APResult")}: gives the number of
clusters.}
\item{sort}{\code{signature(x="ExClust")}: see
\code{\link{sort-methods}}}
\item{as.hclust}{\code{signature(x="ExClust")}: see
\code{\link{coerce-methods}}}
\item{as.dendrogram}{\code{signature(object="ExClust")}: see
\code{\link{coerce-methods}}}
}
}
\section{Accessors}{
In the following code snippets, \code{x} is an \code{APResult} object.
\describe{
\item{[[}{\code{signature(x="APResult", i="index", j="missing")}:
\code{x[[i]]} returns the i-th cluster as a list of indices
of samples belonging to the i-th cluster.
}
\item{[}{\code{signature(x="APResult", i="index", j="missing",
drop="missing")}: \code{x[i]} returns a list of integer vectors with the
indices of samples belonging to this cluster. The list has as
many components as the argument \code{i} has elements. A list is
returned even if \code{i} is a single integer.
}
\item{similarity}{\code{signature(x="APResult")}: gives the similarity
matrix.
}
}
}
\author{Ulrich Bodenhofer, Andreas Kothmeier, Johannes Palme}
\references{\url{https://github.com/UBod/apcluster}
APCluster: an R package for affinity propagation clustering.
\emph{Bioinformatics} \bold{27}, 2463-2464.
DOI: \doi{10.1093/bioinformatics/btr406}.
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}.
}
\seealso{\code{\link{apcluster}}, \code{\link{apclusterL}},
\code{\link{show-methods}}, \code{\link{plot-methods}},
\code{\link{labels-methods}}, \code{\link{cutree-methods}}}
\examples{
## create two Gaussian clouds
cl1 <- cbind(rnorm(100, 0.2, 0.05), rnorm(100, 0.8, 0.06))
cl2 <- cbind(rnorm(50, 0.7, 0.08), rnorm(50, 0.3, 0.05))
x <- rbind(cl1, cl2)
## compute similarity matrix (negative squared Euclidean)
sim <- negDistMat(x, r=2)
## run affinity propagation
apres <- apcluster(sim, details=TRUE)
## show details of clustering results
show(apres)
## plot information about clustering run
plot(apres)
## plot clustering result
plot(apres, x)
## plot heatmap
heatmap(apres, sim)
}
\keyword{classes}
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