segclustout {segclust} | R Documentation |
Extraction of parameters for a segmentation/clustering model
out <- segclustout(x,param,P,K,draw)
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 |
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$bp |
breakpoint coordinates, equals 1 for a breakpoint (corresponding to the end of the segments) |
F. Picard, M. Hoebecke
Picard, F., Robin, S., Lebarbier, E., & Daudin, J. -J. (2007). A segmentation/clustering model for the analysis of array CGH data. Biometrics, 63(3) 758-766
x1 <- rnorm(20,0,1) x2 <- rnorm(30,2,1) x3 <- rnorm(10,0,1) x4 <- rnorm(40,2,1) x <- c(x1,x2,x3,x4) Pmin <- 1 Pmax <- 4 Kmax <- 20 Linc <- matrix(-Inf, ncol=Pmax,nrow= Kmax) param.list <- list() for (P in (Pmin:Pmax)){ out.hybrid <- hybrid(x,P,Kmax) param.list[[P]] <- out.hybrid$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)