SelectModel {Segmentor3IsBack} | R Documentation |
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.
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