Contains funcions that run exact bayesian changepoint methods and return changepoint probabilities and ICL criteria for model selection

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 <alice.cleynen@agroparistech.fr>

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

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# 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.

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