# example data set according to the simulation study in Lee et al. 2010
# generate artifical data set and a correspondig biclust object
u <- c(10,9,8,7,6,5,4,3,rep(2,17),rep(0,75))
v <- c(10,-10,8,-8,5,-5,rep(3,5),rep(-3,5),rep(0,34))
u <- u/sqrt(sum(u^2))
v <- v/sqrt(sum(v^2))
d <- 50
set.seed(1)
X <- (d*u%*%t(v)) + matrix(rnorm(100*50),100,50)
params <- info <- list()
RowxNumber <- matrix(rep(FALSE,100),ncol=1)
NumberxCol <- matrix(rep(FALSE,50),nrow=1)
RowxNumber[u!=0,1] <- TRUE
NumberxCol[1,v!=0] <- TRUE
Number <- 1
ressim <- BiclustResult(params,RowxNumber,NumberxCol,Number,info)
#perform s4vd biclustering
ress4vd <- biclust(X,method=BCs4vd,pcerv=0.5,
pceru=0.5,ss.thr=c(0.6,0.65),steps=500,
pointwise=FALSE,nbiclust=1,savepath=TRUE)
#perform s4vd biclustering with fast pointwise stability selection
ress4vdpw <- biclust(X,method=BCs4vd,pcerv=0.5,
pceru=0.5,ss.thr=c(0.6,0.65),steps=500,
pointwise=TRUE,nbiclust=1)
#stability paths
stabpath(ress4vd,1)
#selection probabilitys for the pointwise stability selection
stabpath(ress4vdpw,1)
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