plot.hmac {Modalclust}

Plots of heierarchical tree for a 'hmac' object

Description

Plots the dendrogram of the entire heierarchical tree for a 'hmac' object starting from any specified smoothing level.

Usage

## S3 method for class 'hmac'
plot(x,mycol=1:6,level=1,n.cluster=NULL,userclus=NULL,sep=.1,...)

Arguments

x

The output of HMAC analysis. An object of class 'hmac'.

mycol

Colors used to represent different clusters.

level

The specified level that dendrogram starts. Default value is 1.

n.cluster

The specified number of clusters. If neither level nor n.cluster is specified, the full tree is plotted.

userclus

If user provides membership, the tree colors the node according to this membership and the tree can be used for validation.

sep

It provides the distance between the lowest layer of nodes of the clusters.

...

further arguments passed to or from other methods.

Author(s)

Surajit Ray and Yansong Cheng

References

Li. J, Ray. S, Lindsay. B. G, "A nonparametric statistical approach to clustering via mode identification," Journal of Machine Learning Research , 8(8):1687-1723, 2007.

Lindsay, B.G., Markatou M., Ray, S., Yang, K., Chen, S.C. "Quadratic distances on probabilities: the foundations," The Annals of Statistics Vol. 36, No. 2, page 983–1006, 2008.

See Also

phmac for front end of using modal clustering and also for parallel implementation of modal clustering. hard.hmac for hard clustering at specified levels. soft.hmac for soft clustering at specified levels.

Examples

data(disc2d.hmac)
# disc2d.hmac is the output of phmac(disc2d,npart=1)
plot(disc2d.hmac)

set.seed(20)
mix4=data.frame(rbind(rmvnorm(20,rep(0,4)), rmvnorm(20,rep(2,4)),
               rmvnorm(20,rep(10,4)),rmvnorm(20,rep(13,4))))
mix4.hmac=phmac(mix4,npart=1)
plot(mix4.hmac,col=1:6)

# Verifying with user provided groups
plot(mix4.hmac,userclus=rep(c(1,2,3,4),each=20),col=1:6)