sig.test: SIGNIFICANCE TEST

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

SIGNIFICANCE TEST

Description

Performs a permutation test.

Usage

sig.test(D, R, changes, min.size, obs, env=emptyenv())

Arguments

D	A n by n distnace matrix.
R	The number of permutations to use in the permutation test.
changes	The set of current change points.
min.size	Minimum number of observations between change points.
obs	Test statistic value for non-permuted data.
env	Environment with information used to reduce computational time.

Details

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

Value

The returned value is the approximate p-value obtained by the permutation test. The permutaiton test is performed using the method outlined in Gandy (2009).

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. 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.