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 '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, ... |

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`

## Performs a class specific MCA on 'Music' example data set ## ignoring every 'NA' (i.e. 'not available') categories ## and focusing on the subset of women, ## and then draws the cloud of categories. data(Music) female <- Music$Gender=='Women' getindexcat(Music[,1:5]) mca <- csMCA(Music[,1:5],subcloud=female,excl=c(3,6,9,12,15)) plot(mca) plot(mca,axes=c(2,3),points='best',col='darkred',app=1)

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