Finds the most likely location for a change point across all current clusters.
e.split(changes, D, min.size, for.sim=FALSE, env=emptyenv())A list with the following components is returned.
The index of the first element of the cluster to be divided.
The index of the last element of the cluster to be divided.
The new set of change points.
The distance between the clusters created by the newly proposed change point.
A vector containing the current set of change points.
An n by n distance matrix.
Minimum number of observations between change points.
Boolean value indicating if the function is to be run on permuted data for significance testing.
Environment that contains information to help reduce computational time.
Nicholas A. James
This method is called by the e.divisive method, and should not be called by the user.
Matteson D.S., James N.A. (2013). A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data.
Nicholas A. James, David S. Matteson (2014). "ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data.", "Journal of Statistical Software, 62(7), 1-25", URL "http://www.jstatsoft.org/v62/i07/"
Rizzo M.L., Szekely G.L. (2005). Hierarchical clustering via joint between-within distances: Extending ward's minimum variance method. Journal of Classification. pp. 151 - 183.
Rizzo M.L., Szekely G.L. (2010). Disco analysis: A nonparametric extension of analysis of variance. The Annals of Applied Statistics. pp. 1034 - 1055.
e.divisive