segmixt {segclust}R Documentation

segmixt

Description

segmentation of a signal when considering changes in a mixture of Gaussian vectors

Usage

        out <- segmixt(x,P,Kmax,phi,lmin=1,lmax=length(x))
 

Arguments

x data vector
P number of clusters
Kmax maximum number of segments
phi parameters of the mixture
lmin minimal segment length, default value lmin = 1
lmax maximal segment length, default value lmax = length(x)

Details

This function can be used for a segmentation/clustering model with P clusters and up to Kmax segments. phi gives the parameters of the mixture (P means, P standard deviations, P mixture proportions). It uses dynamic programming to find the best breakpoints, and is based on the calculus of the incomplete-data log-likelihood:

J.est_{K,P} = sum_{k=1}^{K} log [ sum_{p=1}^{P} (pi_p f(y^k;theta_p)) ]

where f(y^k;theta_p) is the density of a Gaussian vector of length n_k.

Value

J.est incomplete-data log-likelihood for a segmentation/clustering model with P clusters and up to Kmax segments
t.est estimated positions of breakpoints for a segmentation/clustering model up to Kmax segments

Author(s)

F. Picard, M. Hoebecke

References

Picard, F., Robin, S., Lebarbier, E., & Daudin, J. -J. (2007). A segmentation/clustering model for the analysis of array CGH data. Biometrics, 63(3) 758-766


[Package segclust version 0.74 Index]