# EBS-package: Contains funcions that run exact bayesian changepoint methods... In EBS: Exact Bayesian Segmentation

## Description

Implements changepoint method in an exact baysian framework for finding single and multiple changepoints within data. Retrieves each changepoint probabilities for segmentations in 1 to Kmax segments. Chooses the optimal number of segments according to the ICL criterion. Compares change-point location between profiles using credibility intervals or likelihood ratios.

## Details

 Package: EBS Type: Package Version: 2.0 Date: 2012-11-26 License: GPL LazyLoad: yes

## Author(s)

Alice Cleynen

Maintainer: Alice Cleynen <[email protected]>

## References

Rigaill, Lebarbier & Robin (2012): Exact posterior distributions over the segmentation space and model selection for multiple change-point detection problems Statistics and Computing

Cleynen & Robin (2014): Comparing change-point location in independent series Statistics and Computing

Johnson, Kotz & Kemp: Univariate Discrete Distributions

Hall, Kay & Titterington: Asymptotically optimal difference-based estimation of variance in non-parametric regression

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38``` ```# changes for Poisson model set.seed(1) x<-c(rpois(125,1),rpois(100,5),rpois(50,1),rpois(75,5),rpois(50,1)) out <- EBSegmentation(x,Kmax=20) bic <- EBSBIC(out) print(bic\$NbBIC) icl <- EBSICL(out) print(icl\$NbICL) plot(bic\$BIC,type='b',pch=1,col='blue',ylim=c(0,1000)) lines(icl\$ICL,type='b',pch=2,col='red') EBSPlotProba(out, icl\$NbICL, data=TRUE, file="my-segmentation.pdf") # changes for Negative Binomial model, comparison of two profiles set.seed(1) x1<-c(rnbinom(125,size=0.2,prob=0.8),rnbinom(100,size=0.2, prob=0.1), rnbinom(50,size=0.2,prob=0.6),rnbinom(75,size=0.2, prob=0.95), rnbinom(50,size=0.2,prob=0.25)) x2<-c(rnbinom(125,size=0.15,prob=0.75),rnbinom(75,size=0.15,prob=0.2), rnbinom(75,size=0.15,prob=0.9),rnbinom(125,size=0.15,prob=0.1)) M<-rbind(x1,x2) E <- EBSProfiles(M,model=3,K=10,homoscedastic=TRUE) # Computes probabilities for both profile assuming independance but common #overdispersion EBSPlotProbaProfiles(E,K=c(5,4)) # Plots posterior distribution of each change points of the two profiles, #the first into 5 segments, the second into 4. mass<-CompCredibility(E,Conditions=c(1,2),Tau=c(1,1),K=c(5,4)) # Computes the distribution and credibility interval of the difference of #location of the first change point of the two profiles, #the first being devided into 5 segments, the second into 4 mass\$massto0 DecisionStatistic<-EBSStatistic(E,Conditions=c(1,2),Tau=c(1,1)) # Computes the likelihood ratio of the profiles having same first #change-point versus complementary. ```

EBS documentation built on May 29, 2017, 5:49 p.m.