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\name{NNclean}
\alias{NNclean}
\alias{print.nnclean}
%- Also NEED an `\alias' for EACH other topic documented here.
\title{Nearest neighbor based clutter/noise detection}
\description{
Detects if data points are noise or part of a cluster,
based on a Poisson process model.
}
\usage{
NNclean(data, k, distances = NULL, edge.correct = FALSE, wrap = 0.1,
convergence = 0.001, plot=FALSE, quiet=TRUE)
\method{print}{nnclean}(x, ...)
}
%- maybe also `usage' for other objects documented here.
\arguments{
\item{data}{numerical matrix or data frame.}
\item{k}{integer. Number of considered nearest neighbors per point.}
\item{distances}{distance matrix object of class \code{dist}. If
specified, it is used instead of computing distances from the data.}
\item{edge.correct}{logical. If \code{TRUE} and the data is
two-dimensional, neighbors for points at the edges of the parent
region of the noise Poisson process are determined after wrapping
the region onto a toroid.}
\item{wrap}{numerical. If \code{edge.correct=TRUE}, points in a
strip of size \code{wrap*range} along the edge for each variable
are candidates for
being neighbors of points from the opposite.}
\item{convergence}{numerical. Convergence criterion for EM-algorithm.}
\item{plot}{logical. If \code{TRUE}, a histogram of the distance to
kth nearest neighbor and fit is plotted.}
\item{quiet}{logical. If \code{FALSE}, the likelihood is printed
during the iterations.}
\item{x}{object of class \code{nnclean}.}
\item{...}{necessary for print methods.}
}
\details{
The assumption is that the noise is distributed as a homogeneous
Poisson process on a certain region and the clusters are distributed
as a homogeneous Poisson process with larger intensity on a
subregion (disconnected in case of more than one cluster).
The distances are then distributed according to a mixture of two
transformed Gamma distributions, and this mixture is estimated via the
EM-algorithm. The points are assigned to noise or cluster component
by use of the estimated a posteriori probabilities.
}
\value{
\code{NNclean} returns a list of class \code{nnclean} with components
\item{z}{0-1-vector of length of the number of data points. 1 means
cluster, 0 means noise.}
\item{probs}{vector of estimated a priori probabilities for each point
to belong to the cluster component.}
\item{k}{see above.}
\item{lambda1}{intensity parameter of cluster component.}
\item{lambda2}{intensity parameter of noise component.}
\item{p}{estimated probability of cluster component.}
\item{kthNND}{distance to kth nearest neighbor.}
}
\references{
Byers, S. and Raftery, A. E. (1998) Nearest-Neighbor Clutter
Removal for Estimating Features in Spatial Point Processes,
\emph{Journal of the American Statistical Association}, 93, 577-584.
}
\author{R-port by Christian Hennig
\email{christian.hennig@unibo.it}
\url{https://www.unibo.it/sitoweb/christian.hennig/en},\cr
original Splus package by S. Byers and A. E. Raftery.
}
\note{The software can be freely used for non-commercial purposes, and can
be freely distributed for non-commercial purposes only.}
\examples{
library(mclust)
data(chevron)
nnc <- NNclean(chevron[,2:3],15,plot=TRUE)
plot(chevron[,2:3],col=1+nnc$z)
}
\keyword{multivariate}% at least one, from doc/KEYWORDS
\keyword{cluster}% __ONLY ONE__ keyword per line
|
