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.

`tabcontrib(resmca, dim = 1, best = TRUE, dec = 2, shortlabs = FALSE)`

`resmca` |
object of class |

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

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

`dec` |
integer. The number of decimals for the results (default is 2) |

`shortlabs` |
logical. If TRUE, the data frame will have short column names, so that all columns can be displayed side by side on a laptop screen. Default is FALSE (long explicit column names). |

A data frame with the following contributions.:

`Variable` |
names of the variables |

`Category` |
names of the categories |

`Weight` |
weights of the categories |

`Quality of representation` |
quality of representation (squared cosine) of the categories on the axis |

`Contribution (left)` |
contributions of the categories located on one side of the axis |

`Contribution (right)` |
contributions of the categories located on the other side of the axis |

`Total contribution` |
contributions summed by variable |

`Cumulated contribution` |
cumulated sum of the contributions |

`Contribution of deviation` |
for each variable, contribution of the deviation between the barycenter of the categories located on one side of the axis and the barycenter of those located on the other side |

`Proportion to variable` |
contribution of deviation expressed as a proportion of the contribution of the variable |

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`

, `dimdescr`

, `dimeta2`

, `dimtypicality`

```
# specific MCA of Music example data set
data(Music)
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA")
mca <- speMCA(Music[,1:5], excl = junk)
# main contributions on axis 1
tabcontrib(mca, 1)
# main contributions on axis 2
tabcontrib(mca, 2)
```

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