dimcontrib: Describes the contributions to axes for MCA and variants of...

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Identifies the categories and individuals that contribute the most to each dimension obtained by a Multiple Correspondence Analysis. It allows to analyze variants of MCA, such as 'specific' MCA or 'class specific' MCA.

Usage

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dimcontrib(resmca, dim = c(1,2), best = TRUE)

Arguments

resmca

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

dim

dimensions to describe (default is c(1,2))

best

if FALSE, displays all the categories; if TRUE (default), displays only categories and individuals with contributions higher than average

Details

Contributions are sorted and assigned a positive or negative sign according to the corresponding categories or individuals' coordinates, so as to facilitate interpretation.

Value

Returns a list:

var

a list of categories' contributions to axes

ind

a list of individuals' contributions to axes

Author(s)

Nicolas Robette

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

dimdesc, dimdesc.MCA, dimeta2, condes, speMCA, csMCA

Examples

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## 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))
dimcontrib(mca)


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