plot.csMCA | R Documentation |

Plots a class specific Multiple Correspondence Analysis (resulting from `csMCA`

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

```
## S3 method for class 'csMCA'
plot(x, type = "v", axes = 1:2, points = "all",
col = "dodgerblue4", app = 0, ...)
```

`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 'besthv' 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, ... |

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.

Nicolas Robette

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).

`csMCA`

, `textvarsup`

, `conc.ellipse`

```
# class specific MCA on Music example data set
# ignoring every NA values categories
# and focusing on the subset of women,
data(Music)
female <- Music$Gender=="Women"
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA")
mca <- csMCA(Music[,1:5], subcloud = female, excl = junk)
# cloud of categories
plot(mca)
# cloud of most contributing categories
plot(mca,axes=c(2,3), points = "besthv", col = "darkred", app = 1)
```

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