plot.HCPC {FactoMineR} | R Documentation |

## Plots for Hierarchical Classification on Principle Components (HCPC) results

### Description

Plots graphs from a HCPC result: tree, barplot of inertia gains and first factor map with or without the tree, in 2 or 3 dimensions.

### Usage

```
## S3 method for class 'HCPC'
plot(x, axes=c(1,2), choice="3D.map", rect=TRUE,
draw.tree=TRUE, ind.names=TRUE, t.level="all", title=NULL,
new.plot=FALSE, max.plot=15, tree.barplot=TRUE,
centers.plot=FALSE, ...)
```

### Arguments

`x` |
A HCPC object, see |

`axes` |
a two integers vector.Defines the axes of the factor map to plot. |

`choice` |
A string. "tree" plots the tree. "bar" plots bars of inertia gains. "map" plots a factor map, individuals colored by cluster. "3D.map" plots the same factor map, individuals colored by cluster, the tree above. |

`rect` |
a boolean. If TRUE, rectangles are drawn around clusters if choice ="tree". |

`tree.barplot` |
a boolean. If TRUE, the barplot of intra inertia losses is added on the tree graph. |

`draw.tree` |
A boolean. If TRUE, the tree is projected on the factor map if choice ="map". |

`ind.names` |
A boolean. If TRUE, the individuals names are added on the factor map when choice="3D.map" or choice="map" |

`t.level` |
Either a positive integer or a string. A positive integer indicates the starting level to plot the tree on the map when draw.tree=TRUE. If "all", the whole tree is ploted. If "centers", it draws the tree starting t the centers of the clusters. |

`title` |
a string. Title of the graph. NULL by default and a title is automatically defined |

`centers.plot` |
a boolean. If TRUE, the centers of clusters are drawn on the 3D factor maps. |

`new.plot` |
a boolean. If TRUE, the plot is done in a new window. |

`max.plot` |
The max for the bar plot |

`...` |
Other arguments from other methods. |

### Value

Returns the chosen plot.

### Author(s)

Guillaume Le Ray, Quentin Molto, Francois Husson francois.husson@institut-agro.fr

### See Also

### Examples

```
data(iris)
# Clustering, auto nb of clusters:
res.hcpc=HCPC(iris[1:4], nb.clust=3)
# 3D graph from a different point of view:
plot(res.hcpc, choice="3D.map", angle=60)
```

*FactoMineR*version 2.11 Index]