# Plots Multiple Factor Analysis

### Description

Plots Multiple Factor Analysis data, resulting from `multiMCA`

function.

### Usage

1 2 3 |

### 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 are the most correlated to horizontal axis are plotted; if 'bestv' only those who are the most correlated to vertical axis are plotted; if 'best' only those who are the most coorelated to horizontal or vertical axis are plotted. |

`groups` |
numeric vector specifying the groups of categories to plot. By default, every groups of categories will be plotted |

`col` |
a color for the points of the individuals or a vector of colors for the labels of the groups of categories (by default, rainbow palette is used) |

`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 correlated to a given axis if its test-value is higher then 2.58 (which corresponds to a 0.05 threshold).

### Author(s)

Nicolas Robette

### References

Escofier, B. and Pages, J. (1994) "Multiple Factor Analysis (AFMULT package)". *Computational Statistics and Data Analysis*, 18, 121-140.

### See Also

`multiMCA`

, `textvarsup`

, `speMCA`

, `csMCA`

, `MFA`

### Examples

1 2 3 4 5 6 7 8 9 | ```
## Performs a specific MCA on music variables of 'Taste' example data set,
## another one on movie variables of 'Taste' example data set,
## and then a Multiple Factor Analysis and plots the results.
data(Taste)
mca1 <- speMCA(Taste[,1:5],excl=c(3,6,9,12,15))
mca2 <- speMCA(Taste[,6:11],excl=c(3,6,9,12,15,18))
mfa <- multiMCA(list(mca1,mca2))
plot.multiMCA(mfa,col=c('darkred','darkblue'))
plot.multiMCA(mfa,groups=2,app=1)
``` |