# Plots 'specific' MCA results

### Description

Plots a 'specific' Multiple Correspondence Analysis (resulting from `speMCA`

function),
i.e. the clouds of individuals or categories.

### Usage

1 2 |

### Arguments

`x` |
object of class |

`type` |
character string: 'v' to plot the categories (default), 'i' to plot individuals' points, 'inames' to plot individuals' names |

`axes` |
numeric vector of length 2, specifying the components (axes) to plot (c(1,2) is default) |

`points` |
character string. If 'all' all points are plotted (default); if 'besth' only those who contribute most to horizontal axis are plotted; if 'bestv' only those who contribute most to vertical axis are plotted; if 'best' only those who contribute most to horizontal or vertical axis are plotted. |

`col` |
color for the points of the individuals or for the labels of the categories (default is 'dodgerblue4') |

`app` |
numerical value. If 0 (default), only the labels of the categories are plotted and their size is constant; if 1, only the labels are plotted and their size is proportional to the weights of the categories; if 2, points (triangles) and labels are plotted, and points size is proportional to the weight of the categories. |

`...` |
further arguments passed to or from other methods, such as cex, cex.main, ... |

### Details

A category is considered to be one of the most contributing to a given axis if its contribution is higher than the average contribution, i.e. 100 divided by the total number of categories.

### Author(s)

Nicolas Robette

### References

Le Roux B. and Rouanet H., *Multiple Correspondence Analysis*, SAGE, Series: Quantitative Applications in the Social Sciences, Volume 163, CA:Thousand Oaks (2010).

Le Roux B. and Rouanet H., *Geometric Data Analysis: From Correspondence Analysis to Stuctured Data Analysis*, Kluwer Academic Publishers, Dordrecht (June 2004).

### See Also

`speMCA`

, `textvarsup`

, `conc.ellipse`

### Examples

1 2 3 4 5 6 7 8 | ```
## Performs a specific MCA on 'Music' example data set
## ignoring every 'NA' (i.e. 'not available') categories,
## and then draws the cloud of categories.
data(Music)
getindexcat(Music[,1:5])
mca <- speMCA(Music[,1:5],excl=c(3,6,9,12,15))
plot(mca)
plot(mca,axes=c(2,3),points='best',col='darkred',app=1)
``` |