Nothing
GenieClustering=function(DataOrDistances,ClusterNo=0,DistanceMethod="euclidean",ColorTreshold=0,...){
# INPUT
# DataOrDistances[1:n,1:d] Dataset with n observations and d features or distance matrix with size n
#
# OPTIONAL
# ClusterNo Number of clusters to search for
# DistanceMethod String. 'euclidean','mahalanobis','manhatten' (cityblock),'fJaccard','binary', 'canberra', 'maximum'
# ColorTreshold Number. Draws cutline w.r.t. dendogram y-axis (height), height of line as scalar should be given
#
# OUTPUT
# Cls[1:n] Clustering of data
# hc Object of hclust2 algorithm
#
# Author: MT
if (!requireNamespace('genie',quietly = TRUE)) {
message(
'Subordinate clustering package (genie) is missing. No computations are performed.
Please install the package which is defined in "Suggests".'
)
return(
list(
Cls = rep(1, nrow(DataOrDistances)),
Object = "Subordinate clustering package (genie) is missing.
Please install the package which is defined in 'Suggests'."
)
)
}
if (!isSymmetric(unname(DataOrDistances))) {
if(requireNamespace("parallelDist",quietly = TRUE)){
pDist=as.dist(parallelDist::parDist(DataOrDistances,method=DistanceMethod))
}
else{
stop('parallelDist package not loaded or installed.')
}
}else if(!inherits(DataOrDistances,'dist')){
pDist=as.dist(DataOrDistances)
}else{
pDist=DataOrDistances
}
#requireNamespace('genie')
hc <- genie::hclust2(pDist,...)
m=paste("Genie Clustering/ "," N=",nrow(as.matrix(pDist)))
# Classification or dendrogram
if (ClusterNo>0){
Cls=cutree(hc,ClusterNo)
Cls=ClusterRename(Cls,DataOrDistances)
return(list(Cls=Cls,Dendrogram=as.dendrogram(hc),Object=hc))
}
else{
x=as.dendrogram(hc);plot(x, main=m,xlab="Number of data points N", ylab="Distance",sub=" ",leaflab ="none")
axis(1,col="black",las=1)
if (ColorTreshold!=0){
rect.hclust(hc, h=ColorTreshold,border="red")}
else{
}
return(list(Cls=NULL,Dendrogram=x,Object=hc))
}
}
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