https://github.com/cran/cccd
Tip revision: de4ce40da7a3b119ee4d19055a910ee9a4ad2a63 authored by David J. Marchette on 08 April 2022, 11:22:29 UTC
version 1.6
version 1.6
Tip revision: de4ce40
ccd.Rd
\name{ccd}
\alias{ccd}
\alias{plot.ccd}
\title{ Cluster Catch Digraphs}
\description{
construct the cluster catch digraph from a data matrix.
}
\usage{
ccd(data, m = 1, alpha = 0.05, sequential = TRUE, method = NULL)
\method{plot}{ccd}(x,...)
}
\arguments{
\item{data}{ a matrix of observations.}
\item{m}{ slope of the null hypothesis curve.}
\item{alpha}{ alpha for the K-S test if \code{sequential=T}.}
\item{sequential}{ use the sequential or non-sequential version.}
\item{method}{ the method used for the distance.
See \code{\link[proxy]{dist}}.}
\item{x}{an object of class ccd.}
\item{\dots}{arguments passed to \code{plot.cccd}.}
}
\details{
cluster cover digraph. \code{plot.ccd} is just a call to \code{plot.cccd}.
}
\value{
an object of class igraph. In addition, this contains the attributes:
\item{R}{the radii.}
\item{stats}{ the K-S statistics.}
\item{layout}{the data vectors.}
\item{walks}{the y-values of the random walks.}
\item{fs}{the null hypothesis curve.}
\item{A}{ the adjacency matrix.}
\item{m,alpha}{arguments passed to \code{ccd}.}
}
\references{
D.J. Marchette, Random Graphs for Statistical Pattern Recognition,
John Wiley & Sons, 2004.
}
\author{ David J. Marchette david.marchette@navy.mil}
\seealso{ \code{\link{cccd}}
}
\examples{
x <- matrix(rnorm(100),ncol=2)
G <- ccd(x)
\dontrun{
plot(G)
}
}
\keyword{ graphs }