We give a code for drawing parallel level plots of the clusters. The clustering algorithms are provided by R. First generate the data and list the clustering algorithms.
dendat<-sim.data(n=300,seed=5,type="mulmod") algos<-c("kmeans","single","complete","average", "ward","mcquitty","median","centroid")
Draw the parallel level plot.
algo<-algos[3] # choose the algorithm k<-4 # choose the number of clusters paraclus(dendat,algo=algo,k=k) # draw the plot
We give a code for drawing graphical matrices with the permutation given by the clusters. First we generate the data.
dendat<-sim.data(n=300,seed=5,type="cross")
We use hierarchical clustering.
dis<-dist(dendat) hc<-hclust(dis, method=algos[3]) classes=cutree(hc,k=4) permu<-order(classes) graph.matrix(dendat,permu=permu,col=classes) plot(dendat,col=classes) # scatter plot
We use the k-means algorithm.
cl<-kmeans(dendat,centers=4) classes<-cl$cluster permu<-order(classes) graph.matrix(dendat,permu=permu,col=classes) plot(dendat,col=classes) # scatter plot
Graphical matrix of tail clusters.
rho<-0.55 tt<-leafsfirst(dendat=dendat,rho=rho) graph.matrix(dendat,tt=tt) colo<-tree.segme(tt,paletti=seq(1,1000)) plot(dendat,col=colo)
First generate the data.
dendat<-sim.data(n=300,seed=5,type="cross")
Perform hierarchical clustering.
dis<-dist(dendat) hc<-hclust(dis, method=algos[3])
Transform the herarchical clustering tree to a tree generating a branching curve, and then make a parallel level plot of the tree.
tr<-dend2parent(hc,dendat) plotbary(tr,coordi=2,colometh="cluster",colothre=3) plot(hc, hang = -1) # plot also the dendrogram
We give the code for reproducing this figure. First generate the data.
dendat<-sim.data(n=n,type="nested",seed=1)
Calculate a kernel estimate, calculate a level set tree of the kernel estimate, and calculate the likelhood tree.
ke<-pcf.kern(dendat,h=1,N=c(64,64)) # kernel estimate lst<-leafsfirst(ke) # level set tree lt<-liketree(dendat,ke,lst) # likelihood tree
We visualize the likelihood tree with a parallel plot.
plotbary(lt,coordi=1) # coordi=1 or coordi=2
In the 2D case one may also draw the colored scatter plot
dendat.sub<-lt$denda # subsetting paletti<-c("red","blue","green", "orange","navy","darkgreen", "orchid","aquamarine","turquoise", "pink","violet","magenta","chocolate","cyan", colors()[50:657],colors()[50:657]) col<-colobary(lt$parent,paletti) plot(dendat.sub,col=col)