dimdesc.MCA: Describes the dimensions of MCA and variants of MCA In GDAtools: A Toolbox for the Analysis of Categorical Data in Social Sciences, and Especially Geometric Data Analysis

Description

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.

Usage

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

Arguments

 `resmca` object of class `'MCA'`, `'speMCA'`, `'csMCA'` or `'stMCA'` `ncp` number of dimensions to describe (default is 3) `proba` the significance threshold considered to characterize the dimension (default is 0.05)

Details

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

Value

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

References

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

`dimdesc`, `condes`, `speMCA`, `csMCA`

Examples

 ```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) ```

GDAtools documentation built on May 29, 2017, 9:48 p.m.