tabcontrib | R Documentation |

Identifies the categories that contribute the most to a given dimension of a Multiple Correspondence Analysis and organizes these informations into a fancy table. It allows to analyze variants of MCA, such as 'specific' MCA or 'class specific' MCA.

tabcontrib(resmca, dim = 1)

`resmca` |
object of class |

`dim` |
dimension to describe (default is 1st dimension) |

Best contributions - i.e. higher than average - are assigned a positive or negative sign according to the corresponding categories' coordinates, so as to facilitate interpretation. Then they are sorted and organized according to the most contributing variables.

Returns a data frame with the following columns:

`var` |
the names of the most contributing variables |

`moda` |
the names of the most contributing cateories |

`ctr1` |
'negative' contributions, i.e. corresponding to categories with coordinates lower than zero |

`ctr2` |
'positive' contributions, i.e. corresponding to categories with coordinates higher than zero |

`weight` |
weight of the categories |

`ctrtot` |
sum of the best contributions for a given variable |

`cumctrtot` |
cumulated contributions |

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).

`dimcontrib`

, `dimdesc`

, `dimdescr`

, `dimeta2`

, `condes`

, `speMCA`

, `csMCA`

## Performs a specific MCA on 'Music' example data set ## ignoring every 'NA' (i.e. 'not available') categories, ## and then describes the contributions to axes. data(Music) getindexcat(Music[,1:5]) mca <- speMCA(Music[,1:5],excl=c(3,6,9,12,15)) tabcontrib(mca,1) tabcontrib(mca,2)

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