Segmentor3IsBack (version 1.5)

BestSegmentation: BestSegmentation

Description

This function is used to compute the cost of the best segmentation in K segments with change-point t.

Usage

BestSegmentation(x,K,t=numeric())

Arguments

x

An object of class Segmentor returned by function Segmentor

K

The number of segments of the segmentation for which the cost or best segmentation is wanted

t

The position for which the best segmentation with t as change-point is wanted

Value

• BestSegmentation
  A matrix of size n*K: the cost of the optimal segmentation with jth change-point i

Details

ll{ Package: Segmentor3IsBack Type: Package Version: 1.4 Date: 2013-03-25 License: GPL (>= 2) }

References

PDPA: Rigaill, G. Pruned dynamic programming for optimal multiple change-point detection: Submitted http://arxiv.org/abs/1004.0887

PDPA: Cleynen, A. and Koskas, M. and Rigaill, G. A Generic Implementation of the Pruned Dynamic Programing Algorithm: Submitted http://arxiv.org/abs/1204.5564

Examples

require(Segmentor3IsBack);
N=2000 
x=c(rnbinom(N,size=1.3,prob=0.7),rnbinom(N,size=1.3,prob=0.2),
rnbinom(N,size=1.3,prob=0.01));
res=Segmentor(data=x,model=3,Kmax=10, keep=TRUE);  
# Finds the optimal segmentation in up to 10 segments with respect to 
#the negative binomial model.
K<-3
Best<-BestSegmentation(res,K=3,t=3000)
matplot(Best$bestCost, type='l', lty=2)
points(apply(Best$bestCost, 2, which.min), apply(Best$bestCost, 2, min), pch=20, col=1:(K-1))
apply(Best$bestCost, 2, which.min)
getBreaks(res)[K,1:(K-1)]
#computes and plots cost of best segmentation in 3 segments with 
#change-point t, and compares result with change-point estimates.
Best$bestSeg
#returns the optimal segmentation in 3 segments with 3000 as a
#change-point