R: ENERGY SPLIT

e.split {ecp} | R Documentation

## 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`

[Package ecp version 3.0.0 Index]