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Tip revision: e7752674737e0cb9f43cd6ca82e39ac428da2a95 authored by David J. Marchette on 27 May 2015, 00:00:00 UTC
version 1.5
Tip revision: e775267
\title{ Cluster Catch Digraphs}
 construct the cluster catch digraph from a data matrix.
ccd(data, m = 1, alpha = 0.05, sequential = TRUE, method = NULL)
  \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}.}
  cluster cover digraph. \code{plot.ccd} is just a call to \code{plot.cccd}.
 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}.}
D.J. Marchette, Random Graphs for Statistical Pattern Recognition,
John Wiley & Sons, 2004.

\author{ David J. Marchette}

\seealso{ \code{\link{cccd}}


x <- matrix(rnorm(100),ncol=2)
G <- ccd(x)
\keyword{ graphs }
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