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).

## 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
```