Nothing
checkBranchLocalMO <- function(tree,data,i,maxsplit,asw.tol,local.const,orness,
type, ah, verbose, ...){
if (is.vector(data)){
proposal <- list(reject = TRUE,tree = -1)
}else{
if(ncol(data) <= 2){
proposal <- list(reject = TRUE,tree = -1)
}else{
if(sum(tree$clustering == i) <= 2){ #First stopping criteria.
proposal <- list(tree=tree,reject=TRUE)
}else{
which.x <- (tree$clustering == i)
xi <- data[which.x,]
xi.ps <- getBestPamsamMO(xi,maxsplit,orness,type,ah,verbose,...)
if(is.null(local.const)){
n.sub.clust <- xi.ps$num.of.clusters
asw.vec <- rep(NA, n.sub.clust)
for(j in 1:n.sub.clust){
if(sum(xi.ps$clustering == j) <= 2){
asw.vec[j] <- 0
}else{
xij <- xi[xi.ps$clustering == j,]
asw.vec[j] <- getBestPamsamMO(xij,maxsplit,orness,type,ah,verbose,...)$asw
}
}
if(xi.ps$asw > mean(asw.vec) - asw.tol){ #Second stopping criteria.
tree <- update.tree.local(object = tree, xi.ps, which.x, i)
proposal <- list(tree = tree,reject = FALSE)
}else{
proposal <- list(tree = tree,reject = TRUE)
}
}else{
if(xi.ps$asw > local.const){
tree <- update.tree.local(object = tree, xi.ps, which.x, i)
proposal <- list(tree = tree, reject = FALSE)
}else{
proposal <- list(tree=tree,reject=TRUE)
}
}
}
}
}
proposal
}
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