segclustout {segclust}R Documentation

segclustout

Description

Extraction of parameters for a segmentation/clustering model

Usage

        out <- segclustout(x,param,P,K,draw)

Arguments

x data vector (without missing values)
param list of parameters estimated by hybrid for a given P
P number of clusters
K number of segments (must be smaller than P)
draw TRUE for plotting

Value

output dataframe containing results of the estimation procedure
output$signal input signal x
output$mean estimated mean according to the model, for each position
output$sd estimated standard deviation according to the model, for each position
output$cluster cluster for each point
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)
        x3         <- rnorm(10,0,1)
        x4         <- rnorm(40,2,1)
        x          <- c(x1,x2,x3,x4)

        Pmin       <- 1
        Pmax       <- 4
        Kmax       <- 20
        Linc       <- matrix(-Inf, ncol=Pmax,nrow= Kmax)
        param.list <- list()

        for (P in (Pmin:Pmax)){   
            out.hybrid      <- hybrid(x,P,Kmax)
            param.list[[P]] <- out.hybrid$param
            Linc[,P]        <- out.hybrid$Linc    
        }
        out.select <- segclustselect(x,param,Pmin,Pmax,Kmax,Linc, method = "sequential")
        output     <- segclustout(x,param.list[[out.select$Pselect]],out.select$Pselect,out.select$Kselect,draw=TRUE)


[Package segclust version 0.74 Index]