GDAtools (version 1.5)

tabcontrib: Displays the categories contributing most to axes for MCA and variants of MCA

Description

Identifies the categories that contribute the most to a given dimension of a Multiple Correspondence Analysis and organizes them into a fancy table. It allows to analyze variants of MCA, such as 'specific' MCA or 'class specific' MCA.

Usage

tabcontrib(resmca, dim = 1)

Arguments

resmca

object of class 'MCA', 'speMCA', or 'csMCA'

dim

dimension to describe (default is 1st dimension)

Value

Returns a data frame with the following columns:

var

the names of the most contributing variables

moda

the names of the most contributing cateories

ctr1

'negative' contributions, i.e. corresponding to categories with coordinates lower than zero

ctr2

'positive' contributions, i.e. corresponding to categories with coordinates higher than zero

weight

weight of the categories

ctrtot

sum of the best contributions for a given variable

cumctrtot

cumulated contributions

Details

Best contributions - i.e. higher than average - are assigned a positive or negative sign according to the corresponding categories' coordinates, so as to facilitate interpretation. Then they are sorted and organized according to the most contributing variables.

References

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).

See Also

dimcontrib, dimdesc, dimdesc.MCA, dimeta2, 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 describes the contributions to axes.
data(Music)
getindexcat(Music[,1:5])
mca <- speMCA(Music[,1:5],excl=c(3,6,9,12,15))
tabcontrib(mca,1)
tabcontrib(mca,2)
# }