R: ENERGY DIVISIVE

e.divisive {ecp} | R Documentation

## ENERGY DIVISIVE

### Description

A divisive hierarchical estimation algorithm for multiple change point analysis.

### Usage

    e.divisive(X, sig.lvl=.05, R=199, k=NULL, min.size=30, alpha=1)

### Arguments

`X` | A T x d matrix containing the length T time series with d-dimensional observations.
`sig.lvl` | The level at which to sequentially test if a proposed change point is statistically significant.
`R` | The maximum number of random permutations to use in each iteration of the permutation test. The permutation test p-value is calculated using the method outlined in Gandy (2009).
`k` | Number of change point locations to estimate, suppressing the permutation based testing. If k=NULL then only the statistically significant estimated change points are returned.
`min.size` | Minimum number of observations between change points.
`alpha` | The moment index used for determining the distance between and within segments.

### Details

Segments are found through the use of a binary bisection method and a permutation test. The computational complexity of this method is O(kT^2), where k is the number of estimated change points, and T is the number of observations.

### Value

The returned value is a list with the following components.

`k.hat` | The number of clusters within the data created by the change points.
`order.found` | The order in which the change points were estimated.
`estimates` | Locations of the statistically significant change points.
`considered.last` | Location of the last change point, that was not found to be statistically significant at the given significance level.
`permutations` | The number of permutations performed by each of the sequential permutation test.
`cluster` | The estimated cluster membership vector.
`p.values` | Approximate p-values estimated from each permutation test.

### 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/"

Gandy, A. (2009) "Sequential implementation of Monte Carlo tests with uniformly bounded resampling risk." Journal of the American Statistical Association.

Rizzo M.L., Szekely G.L (2005). Hierarchical clustering via joint between-within distances: Extending ward's minimum variance method. Journal of Classification.

Rizzo M.L., Szekely G.L. (2010). Disco analysis: A nonparametric extension of analysis of variance. The Annals of Applied Statistics.

### See Also

`e.agglo`

### Examples

    set.seed(100)
    x1 = matrix(c(rnorm(100),rnorm(100,3),rnorm(100,0,2)))
    y1 = e.divisive(X=x1,sig.lvl=0.05,R=199,k=NULL,min.size=30,alpha=1)
    x2 = rbind(MASS::mvrnorm(100,c(0,0),diag(2)),MASS::mvrnorm(100,c(2,2),diag(2)))
    y2 = e.divisive(X=x2,sig.lvl=0.05,R=499,k=NULL,min.size=30,alpha=1)

[Package ecp version 3.0.0 Index]