Identifies the variables and the categories that are the most characteristic according to each dimension obtained
by a Factor Analysis. It is inspired by `dimdesc`

function in `FactoMineR`

package (see Husson et al, 2010),
but allows to analyze variants of MCA, such as 'specific' MCA or 'class specific' MCA.

1 | ```
dimdesc.MCA(resmca, ncp = 3, proba = 0.05)
``` |

`resmca` |
object of class |

`ncp` |
number of dimensions to describe (default is 3) |

`proba` |
the significance threshold considered to characterize the dimension (default is 0.05) |

The statistical indicator used for variables is square correlation ratio (R2) and the one used for categories is test-value (v.test).

Returns a list of ncp lists including:

`quali` |
the description of the dimensions by the categorical variables (the variables are sorted) |

`category` |
the description of the dimensions by each category of all the categorical variables (the categories are sorted) |

Nicolas Robette

Husson, F., Le, S. and Pages, J. (2010). *Exploratory Multivariate Analysis by Example Using R*, Chapman and Hall.

`dimdesc`

, `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 describe the dimensions.
data(Music)
getindexcat(Music[,1:5])
mca <- speMCA(Music[,1:5],excl=c(3,6,9,12,15))
dimdesc.MCA(mca,proba=0.2)
``` |

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