supvars: Statistics for categorical supplementary variables

In GDAtools: Geometric Data Analysis

Statistics for categorical supplementary variables

Description

From MCA results, computes statistics (weights, coordinates, squared cosines, contributions, test-values, variances) for categorical supplementary variables.

Usage

supvars(resmca, vars)

varsups(resmca, vars)

Arguments

resmca	object of class MCA, speMCA, csMCA, stMCA or multiMCA
vars	A data frame of categorical supplementary variables. All these variables should be factors.

Value

Returns a list with the following items :

weight	numeric vector of categories weights
coord	data frame of categories coordinates
cos2	data frame of categories squared cosines
var	a list of data frames of categories within variances, variance between and within categories and variable square correlation ratio (eta2)
typic	data frame of categories typicality test statistics
pval	data frame of categories p-values from typicality test statistics
cor	data frame of categories correlation coefficients

Note

varsups is softly deprecated. Please use supvars instead.

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

supvar, ggadd_supvar, ggadd_supvars, textvarsup, supind

Examples

# specific MCA of Music example data set
data(Music)
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA")
mca <- speMCA(Music[,1:5], excl = junk)
# computes statistics for Gender and Age supplementary variables
supvars(mca, Music[, c("Gender","Age")])

GDAtools documentation built on May 29, 2024, 11:06 a.m.