GDAtools (version 1.5)

dimdesc.MCA: Describes the dimensions of MCA and variants of MCA

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

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)

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)

Details

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

References

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

See Also

dimdesc, condes, speMCA, csMCA

Examples

# NOT RUN {
## 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)
# }