Segmentor3IsBack (version 1.5)

Segmentor-package: Implementation of the Pruned Dynamic Programming Algorithm for the exact optimal segmentation of profiles

Description

Exact change-point algorithm for the segmentation of profiles according

Details

ll{ Package: Segmentor3IsBack Type: Package Version: 1.4 Date: 2013-03-25 License: GPL LazyLoad: yes }

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

overdispersion parameter: Johnson, N. and Kemps, A. and Kotz, S. (2005) Univariate Discrete Distributions: John Wiley & Sons variance parameter: Hall, P. and Kay, J. and Titterington, D. (1990): Asymptotically optimal difference-based estimation of variance in non-parametric regression Biometrika Selection criterion for counts: Cleynen, A. and Lebarbier, E. Segmentation of the Poisson and negative binomial rate models: a penalized estimator: Submitted http://arxiv.org/abs/1301.2534 Selection criterion for Gaussian distribution: Lebarbier, E. (2005) Detecting multiple change-points in the mean of Gaussian process by model selection: Signal Processing Slope heuristic: Arlot, S. and Bach, F. (2009) Data-driven calibration of penalties for least-squares regression: Journal of Machine Learning Research modified BIC: Zhang, N. and Siegmund, D. (2007) A modified Bayes information criterion with applications to the analysis of comparative genomic hybridization data: Biometrics

Examples

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), rnbinom(N,size=1.3,prob=0.2),rnbinom(N,size=1.3,prob=0.8));
res=Segmentor(data=x,model=3,Kmax=20);  
# Finds the optimal segmentation in up to 20 segments with respect to 
#the negative binomial model.
Cr<-SelectModel(res,penalty='oracle',keep=FALSE)
Cr
#chooses the number of segments in the segmentation of x