Segmentor3IsBack (version 2.0)

Segmentor: Segmentor

Description

The functions are used for change-point problems. Given a loss function (Poisson, Normal homoscedastic, Negative Binomial, Normal Heteroscedastic (with given constant mean) or Exponential), the function Segmentor estimates the optimal segmentation with respect to the log-likelihood. The Segmentor gives estimates of the breakpoint locations as well as the loss function parameter of each segment.

Usage

Segmentor(data=numeric(), model=1, Kmax = 15, phi = numeric(), 
m = numeric(), keep=FALSE, bounds=c(0,0), compress = TRUE)

Arguments

data

A vector of observations to be segmented. Must have no missing values.

model

Integer between 1 and 4 giving the modelisation of the observed data, 1: poisson (default), 2: normal-homoscedastic, 3: negative binomial, 4: normal-heteroscedastic or 5: exponential

Kmax

The maximum number oe segments wanted for the data. The Segmentor will find all optimal segmentations in 1 to Kmax segments.

phi

Needed only for the Negative Binomial distribution: the value of the inverse of the overdispersion parameter. If the user does not enter a value, the package uses a modified version of Johnson and Kotz's estimator where the mean is replaced by the median.

m

Needed only for Normal Heteroscedastic distribution: the value of the constant mean. If not entered, the function uses the empirical mean of the data.

keep

a boolean stating whether or not to keep Cost and Position matrices

bounds

optional lower and upper bounds on the parameter to segment : will fasten the pruning and hence the algorithm if some values are not allowed (e.g. non negative numbers for Poisson distribution)

compress

A boolean stating whether data should be compressed prior to segmentation

Value

data

The vector of observations to be segmented.

model

Emission distribution (Poisson, Normal Homoscedastic, Negative Binomial or Normal Heteroscedastic or exponential)

breaks

Matrix of size Kmax*Kmax of estimated change-point locations for each optimal segmentation in 1 to Kmax segments.

parameters

Matrix of size Kmax*Kmax which elements are the estimated parameters for each segment of the optimal segmentation. If model is Poisson or Normal, the parameter corresponds to the mean of the signal in each segment. If model is Negative binomial, the parameter corresponds to the success-probability of the signal in each segment. If model is normal heteroscedastic, the parameter is the variance assuming known mean.

likelihood

Vector of size Kmax of resulting negative log-likelihood for each optimal segmentation.

Cost

Matrix of size Kmax x n containing the cost of the segmentation of signal up to point j in i segments

Pos

Matrix of size Kmax x n containing the last change-point location of the segmentation of signal up to point j in i segments

overdispersion

only if model = Negative Binomial, the value of the inverse of overdispersion used for the segmentation

mean

only if model = Normal Heteroscedastic, the value of the mean used for the segmentation

compression

The value of the compression factor used (compression>=1)

Details

Package:

Segmentor3IsBack

Type:

Package

Version:

1.5

Date:

2013-03-25

License:

GPL (>= 2)

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 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 variance parameter: Hall, P. and Kay, J. and Titterington, D. (1990): Asymptotically optimal difference-based estimation of variance in non-parametric regression Biometrika

Examples

require(Segmentor3IsBack);
N=2000 
x=c(rpois(N,2.0),rpois(2*N,2.2),rpois(N,1.9));
res=Segmentor(data=x,Kmax=3);  
# Finds the optimal segmentation in 1, 2 and 3 segments with respect to 
#the Poisson model.

y=c(rnbinom(N,prob=0.3,size=0.15),rnbinom(2*N,prob=0.1,size=0.15),
rnbinom(N,prob=0.6,size=0.15),compress=FALSE)
res2=Segmentor(y, model=3,Kmax=10); 
#Finds the optimal segmentation in 1 to 10 segments with respect to 
#the Negative Binomial model, without compression of data.