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)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.