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Last data update: 2014.03.03

R: Plot soft clusters from Modal Clustering output

soft.hmac

R Documentation

Plot soft clusters from Modal Clustering output

Description

Plot clusters for two dimensional data with colors representing the posterior probability of belonging to clusters. Additionally boundary points between the clusters, with specified thresholds are also

Usage

soft.hmac(hmacobj,n.cluster=NULL,level=NULL,boundlevel=0.4,plot=TRUE)

Arguments

`hmacobj`	The output of HMAC analysis. An object of class 'hmac'.
`level`	The specified level of HMAC output
`n.cluster`	The specified number of clusters. If neither `level` nor `n.cluster` is specified, soft clustering output is shown for each level.
`boundlevel`	Posterior probability threshold. Points having posterior probability below `boundlevel` are assigned as boundary points and colored in gray. Default value is 0.4.
`plot`	Get the two dimensional plot of the clusters with different colors. Default value is TRUE, which returns the two dimensional plot on the current graphics device; plot=FALSE returns the posterior probability of each observation.

Value

Returns the list that contains the posterior probability of each observation and boundary points at specified level if plot=FALSE

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(logcta20.hmac)
#logcta20.hmac is the output of phmac(logcta20,npart=1)

soft.hmac(logcta20.hmac,n.cluster=3)

#return the posterior probability of each observation and boundary points.
postprob=soft.hmac(hmacobj=logcta20.hmac,n.cluster=3,plot=FALSE)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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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/soft.HMAC.Rd_%03d_medium.png", width=480, height=480)
> ### Name: soft.hmac
> ### Title: Plot soft clusters from Modal Clustering output
> ### Aliases: soft.hmac
> ### Keywords: cluster, hierarchical, nested, modal
> 
> ### ** Examples
> 
> data(logcta20.hmac)
> #logcta20.hmac is the output of phmac(logcta20,npart=1)
> 
> soft.hmac(logcta20.hmac,n.cluster=3)
The level at which there are 3 clusters is 4 
> 
> #return the posterior probability of each observation and boundary points.
> postprob=soft.hmac(hmacobj=logcta20.hmac,n.cluster=3,plot=FALSE) 
The level at which there are 3 clusters is 4 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>