# Plots of heierarchical tree for a 'hmac' object

### Description

Plots the dendrogram of the entire heierarchical tree for a 'hmac' object starting from any specified smoothing level.

### Usage

1 2 |

### Arguments

`x` |
The output of HMAC analysis. An object of class 'hmac'. |

`mycol` |
Colors used to represent different clusters. |

`level` |
The specified level that dendrogram starts. Default value is 1. |

`n.cluster` |
The specified number of clusters. If neither |

`userclus` |
If user provides membership, the tree colors the node according to this membership and the tree can be used for validation. |

`sep` |
It provides the distance between the lowest layer of nodes of the clusters. |

`...` |
further arguments passed to or from other methods. |

### 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.

### See Also

`phmac`

for front end of using modal clustering and also for parallel implementation of modal clustering.
`hard.hmac`

for hard clustering at specified levels.
`soft.hmac`

for soft clustering at specified levels.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 | ```
data(disc2d.hmac)
# disc2d.hmac is the output of phmac(disc2d,npart=1)
plot(disc2d.hmac)
set.seed(20)
mix4=data.frame(rbind(rmvnorm(20,rep(0,4)), rmvnorm(20,rep(2,4)),
rmvnorm(20,rep(10,4)),rmvnorm(20,rep(13,4))))
mix4.hmac=phmac(mix4,npart=1)
plot(mix4.hmac,col=1:6)
# Verifying with user provided groups
plot(mix4.hmac,userclus=rep(c(1,2,3,4),each=20),col=1:6)
``` |