soft.HMAC: Plot soft clusters from Modal Clustering output
In Modalclust: Hierarchical Modal Clustering

Description Usage Arguments Value Author(s) References See Also Examples

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

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

`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.

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

Surajit Ray and Yansong Cheng

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.

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

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)