e.split: ENERGY SPLIT

In ecp: Non-Parametric Multiple Change-Point Analysis of Multivariate Data

ENERGY SPLIT

Description

Finds the most likely location for a change point across all current clusters.

Usage

	e.split(changes, D, min.size, for.sim=FALSE, env=emptyenv())

Arguments

changes	A vector containing the current set of change points.
D	An n by n distance matrix.
min.size	Minimum number of observations between change points.
for.sim	Boolean value indicating if the function is to be run on permuted data for significance testing.
env	Environment that contains information to help reduce computational time.

Details

This method is called by the e.divisive method, and should not be called by the user.

Value

A list with the following components is returned.

first	The index of the first element of the cluster to be divided.
second	The index of the last element of the cluster to be divided.
third	The new set of change points.
fourth	The distance between the clusters created by the newly proposed change point.

Author(s)

Nicholas A. James

References

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.

See Also

e.divisive

ecp documentation built on Sept. 12, 2024, 7:40 a.m.