\name{ccd} \alias{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) } \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} } \details{ cluster cover digraph. } \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{ plotCCD(G) } } \keyword{ math } \keyword{ multivariate }