Description Usage Arguments Author(s) References See Also Examples
Plot clusters for two dimensional data with contours of the original data
1 2 |
x |
The output of HMAC analysis. An object of class 'hmac'. |
level |
The specified level |
n.cluster |
The specified number of clusters. Either |
prob |
The specified level of the contour plot. Default value is NULL, plot all levels of the contour plot. Must be between 0 and 1 |
smoothplot |
Get the smooth scatter plot of the original data set. Default value is FALSE, which does not provide the smooth scatter plot. |
... |
Further arguments passed to or from other methods. |
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.
hard.hmac
for hard clustering at specified levels.
See plot
for plotting the whole dendrogram.
1 2 3 4 5 6 7 8 9 10 | data(disc2d.hmac)
# disc2d.hmac is the output of phmac(disc2d,npart=1)
contour.hmac(disc2d.hmac,level=3,col=gray(0.7))
# Provide contour line at probability density 0.05.
contour(disc2d.hmac,n.cluster=2,prob=0.05)
# Plot using smooth scatter plot.
contour.hmac(disc2d.hmac,n.cluster=2,smoothplot=TRUE)
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