segmean {segclust}R Documentation

segmean

Description

segmentation of a signal when considering changes in the mean

Usage

        out <- segmean(x,Kmax,lmin=1,lmax=length(x),vh=TRUE)
 

Arguments

x data vector
Kmax maximum number of segments
lmin minimal segment length, default value lmin = 1
lmax maximal segment length, default value lmax = length(x)
vh TRUE for an homogeneous variance (default), FALSE otherwise

Details

This function can be used for a segmentation model such that:

for t in I_k, Y_t = μ_k + epsilon_t,

and the variance of $ε$ can be either homoscedastic (vh=TRUE) or heteroscedastic (vh=FALSE). It uses dynamic programming to find the best breakpoints, and is based on the calculus of the Residual Sum of Squares:

J.est_K = sum_{k=1}^{K} sum_{t in I_k} (y_t-hat{μ}_k)^2.

Value

J.est Residual Sum of Squares for segmentation models up to Kmax segments
t.est estimated positions for breakpoints for segmentation models up to Kmax segments

References

Picard, F., Robin, S., Lavielle, M., Vaisse, C., & Daudin, J. -J. (2005). A statistical approach for array CGH data analysis. BMC Bioinformatics, 6(1), 1-14.

Examples

        x1      <- rnorm(20,0,1)
        x2      <- rnorm(30,2,1)
        x       <- c(x1,x2)
        Kmax    <- 20
        out     <- segmean(x,Kmax)
        Kselect <- segselect(out$J.est,Kmax)
        output  <- segout(x,K=Kselect,th = out$t.est,draw=TRUE)


[Package segclust version 0.74 Index]