Coinertia analysis between two groups of categorical variables

```
coiMCA(Xa, Xb,
excl.a = NULL, excl.b = NULL,
row.w = NULL, ncp = 5)
```

## Arguments

- Xa
data frame with the first group of categorical variables

- Xb
data frame with the second group of categorical variables

- excl.a
numeric vector indicating the indexes of the "junk" categories in `Xa`

(default is NULL). See `getindexcat`

or use `ijunk`

interactive function to identify these indexes. It may also be a character vector of junk categories, specified in the form "namevariable.namecategory" (for instance "gender.male").

- excl.b
numeric vector indicating the indexes of the "junk" categories in `Xb`

(default is NULL). See `excl.a`

argument.

- row.w
numeric vector of row weights. If NULL (default), a vector of 1 for uniform row weights is used.

- ncp
number of dimensions kept in the results (by default 5)

## Details

Coinertia analysis aims at capturing the structure common to two groups of variables. With groups of numerical variables, it is equivalent to Tucker's inter-battery analysis.
With categorical data, it consists in the following steps :
1. Transformation of `Xa`

and `Xb`

into indicator matrices (i.e. disjunctive tables) `Xad`

and `Xbd`

2. Computation of the covariance matrix t(Xad).Xbd
3. CA of the matrix

## Value

An object of class `CA`

from `FactoMineR`

package, with an additional item :

- RV
the RV coefficient between the two groups of variabels

## References

Tucker, L.R.. (1958) An inter-battery method of factor analysis. *Psychometrika*, 23-2, 111-136.

Dolédec, S. and Chessel, D. (1994) Co-inertia analysis: an alternative method for studying species-environment relationships. *Freshwater Biology*, 31, 277–294.

## Examples

```
data(Music)
# music tastes
Xa <- Music[,1:5]
# gender and age
Xb <- Music[,6:7]
# coinertia analysis
res <- coiMCA(Xa, Xb)
plot(res)
# RV coefficient
res$RV
#> [1] 0.06064611
```