SelectModel: SelectModel
In Segmentor3IsBack: A Fast Segmentation Algorithm

Description Usage Arguments Details Value Author(s) References Examples

This function is used to select the number of segments in the segmentation of the data according to several criterion including BIC, AIC, mBIC and oracle penalties.

1	SelectModel(x,penalty="oracle",seuil=n/log(n),keep=FALSE,greatjump=FALSE)

`x`	An object of class Segmentor returned by function Segmentor
`penalty`	An object of class string stating which penalty to use in the model selection criteria. Accepted penalties are BIC, AIC, Zhang's modified BIC: mBIC and oracle penalties: 'oracle'. In the case of Normal segmentation, criterion developed by Lebarbier, in the case of Poisson and Negative Binomial segmentation, criterion developed by Cleynen and Lebarbier. Default is oracle
`seuil`	If penalty='oracle', an Integer for the threshold to use for the slope heuristic. Default value is n/log(n)
`keep`	A Boolean stating whether or not to keep the values of the criterion. Default is FALSE.
`greatjump`	If penalty is "oracle", a boolean stating whether to use the greatest jump (TRUE) or the threshold for the slope heuristic. Default is FALSE.

Package:	Segmentor3IsBack
Type:	Package
Version:	1.5
Date:	2013-03-25
License:	GPL (>= 2)

`K`	The number of segments selected.
`crit`	If keep=TRUE, a vector of criterion value for each possible K.

Alice Cleynen, Michel Koskas and Guillem Rigaill

Maintainer: Who to complain to <alice.cleynen@agroparistech.fr>

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 Lebarbier, E. and Rigaill, G. and Robin, S. Segmentor3IsBack (2014): an R package for the fast and exact segmentation of Seq-data Algorithms for Molecular Biology

overdispersion parameter: Johnson, N. and Kemps, A. and Kotz, S. (2005) Univariate Discrete Distributions: John Wiley & Sons

Selection criterion for counts: Cleynen, A. and Lebarbier, E. (2014) Segmentation of the Poisson and negative binomial rate models: a penalized estimator: ESAIM: Probability and Statistics

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

require(Segmentor3IsBack);
N=2000 
x=rnbinom(5*N, size=1.3, prob=rep(c(0.7,0.2,0.01,0.2,0.8),each=N))
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 using
# an oracle-inequality approach

N=250 
x=rpois(10*N, rep(c(8,1,5,3,16,33,2,12,7,1),each=N))
res=Segmentor(data=x,model=3,Kmax=40);  
# Finds the optimal segmentation in up to 40 segments with respect to 
#the poisson model.
Cr<-SelectModel(res,penalty='BIC',keep=FALSE)
Cr
#chooses the number of segments in the segmentation of x using
# the BIC approach