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Last data update: 2014.03.03

R: Plots of heierarchical tree for a 'hmac' object

plot.hmac

R Documentation

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.

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(Modalclust)
Loading required package: mvtnorm
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: class
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Modalclust/plot.hmac.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot.hmac
> ### Title: Plots of heierarchical tree for a 'hmac' object
> ### Aliases: plot.hmac plot
> ### Keywords: cluster, hierarchical, nested, modal
> 
> ### ** 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)
Performing initial Modal clusteringLoading required package: parallel
..........
Building hierarchical Modal clusters at 
level  1 ...level  2 ...level  3 ...level  4 ...level  5 ...
level  6 ...level  7 ...level  8 ...level  9 ...level  10 ...

> 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)

> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>