dimdescr | R Documentation |

Identifies the variables and the categories that are the most characteristic according to each dimension obtained
by a MCA. 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.

dimdescr(resmca, vars=NULL, dim = c(1,2), min.cor = NULL, nperm = 100, distrib = "asympt")

`resmca` |
object of class |

`vars` |
data frame of variables to describes the MCA dimensions with. If NULL (default), the active variables of the MCA will be used. |

`dim` |
the axes which are described. Default is c(1,2) |

`min.cor` |
for the relationship between y and a categorical variable, only associations higher or equal to min.cor will be displayed. If NULL (default), they are all displayed. |

`nperm` |
numeric. Number of permutations for the permutation test of independence. If NULL, no permutation test is performed. |

`distrib` |
the null distribution of permutation test of independence can be approximated by its asymptotic distribution ( |

See `condesc`

.

Returns a list of ncp lists including:

`variables` |
associations between y and the variables in x |

`categories` |
a data frame with categorical variables from x and associations measured by correlation coefficients |

Nicolas Robette

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

`condesc`

, `speMCA`

, `csMCA`

, `dimdesc`

## 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)) dimdescr(mca,min.cor=0.1,nperm=10)

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