Correlation ratios (aka eta-squared) of supplementary variables

Computes correlation ratios (also known as eta-squared) for a list of supplementary variables of a MCA.

dimeta2(resmca, vars, dim = c(1,2))

Arguments

resmca

object of class MCA, speMCA, csMCA, stMCA or multiMCA

vars

a data frame of supplementary variables

dim

the axes for which eta2 are computed. Default is c(1,2)

Value

Returns a data frame with supplementary variables as rows and MCA axes as columns.

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).

Author

Nicolas Robette

See also

dimdescr, dimcontrib, dimtypicality

Examples

# specific MCA on 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)
# correlation ratios
dimeta2(mca, Music[, c("Gender", "Age")])
#>        dim.1 dim.2
#> Gender   0.0   0.9
#> Age      3.8  22.7