clustersegments {segclust}R Documentation

clustersegments

Description

Cluster segments using the EM algorithm when the number of clusters and the segmentation are given. The segmentation is not revised after clustering (contrary to hybrid)

Usage

        out <- clustersegments(x,P,bp,vh=TRUE)

Arguments

x data vector (without missing values)
P number of clusters
bp vector (size length(x)), such that bp[t]=1 if t is a breakpoint and 0 otherwise. (t corresponds to the end of a segment). bp[length(x)] is always equal to 1.
vh TRUE for homogeneous variances (default), FALSE otherwise

Value

out dataframe
output$signal input signal x
output$mean estimated mean using a mixture model with P cluster, AFTER a segmentation
output$sd estimated standard deviation using a mixture model with P cluster, AFTER a segmentation
output$cluster cluster for each point AFTER segmentation
output$bp breakpoint coordinates, equals 1 for a breakpoint (corresponding to the end of the 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

Examples

        x1           <- rnorm(20,0,1)
        x2           <- rnorm(30,2,1)
        x            <- c(x1,x2)
        bp           <- rep(0,length(x))
        bp[c(20,50)] <- 1
        P            <- 2
        out          <- clustersegments(x,P,bp)

[Package segclust version 0.74 Index]