e.divisive {ecp} | R Documentation |
A divisive hierarchical estimation algorithm for multiple change point analysis.
e.divisive(X, sig.lvl=.05, R=199, k=NULL, min.size=30, alpha=1)
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 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. |
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.
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. |
Nicholas A. James
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.
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)