hard.HMAC: Plot clusters with different colors.
In Modalclust: Hierarchical Modal Clustering

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

Plot clusters with colors obtained from hard density. Plot one dimensional data with density plot. Plot two dimensional data with scatter plot. Pairwise scatter plot will be provided for data with more than two dimensions.

1	hard.hmac(hmacobj,level=NULL, n.cluster=NULL,plot=TRUE,colors=1:6,...)

`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, hard clustering output is shown for each level.
`plot`	Get the plot of the clusters with different colors. Default value is TRUE, draws a plot on the current graphics device; plot=FALSE indicates do not get the plot and returns the membership of data.
`colors`	Colors used to represent different clusters.
`...`	Further graphical parameters

Returns the membership of each observation of the 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 soft.hmac for soft clustering at specified levels. See plot.hmac.

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

hard.hmac(disc2d.hmac,level=3)

#returns the membership of each observation
disc2d.2clus=hard.hmac(hmacobj=disc2d.hmac,n.cluster=2,plot=FALSE)
table(disc2d.2clus)

#hard.hmac(disc2d.hmac)

iris.hmac=phmac(iris[,-5])
# For more than two dimensions it produces the pairs plot
hard.hmac(iris.hmac,n.cluster=2)