Identifies the categories and individuals that contribute the most characteristic according to each dimension obtained by a Factor Analysis. It allows to analyze variants of MCA, such as 'specific' MCA or 'class specific' MCA.

1 | ```
dimcontrib(resmca, dim = c(1,2), best = TRUE)
``` |

`resmca` |
object of class |

`dim` |
dimensions to describe (default is c(1,2)) |

`best` |
if FALSE, displays all the categories; if TRUE (default), displays only categories and individuals with contributions higher than average |

Contributions are sorted and assigned a positive or negative sign according to the corresponding categories or individuals' coordinates, so as to facilitate interpretation.

Returns a list:

`var` |
a list of categories' contributions to axes |

`ind` |
a list of individuals' contributions to axes |

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).

`dimdesc`

, `dimdesc.MCA`

, `dimeta2`

, `condes`

, `speMCA`

, `csMCA`

1 2 3 4 5 6 7 | ```
## 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))
dimcontrib(mca)
``` |

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