Performs Multiple Factor Analysis, drawing on the work of Escofier and Pages (1994). It allows the use of
MCA variants (e.g. specific MCA or class specific MCA) as inputs.

`multiMCA(l_mca, ncp = 5, compute.rv = FALSE)`

## Arguments

- l_mca
a list of objects of class `MCA`

, `speMCA`

or `csMCA`

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

- compute.rv
whether RV coefficients should be computed or not (default is FALSE, which makes the function execute faster)

## Details

This function binds individual coordinates from every MCA in `l_mca`

argument, weights them by the first eigenvalue, and the resulting data frame is used as input for Principal Component Analysis (PCA).

## Value

Returns an object of class `multiMCA`

, i.e. a list:

- eig
a list of numeric vector for eigenvalues, percentage of variance and cumulative percentage of variance

- var
a list of matrices with results for input MCAs components (coordinates, correlations between variables and axes, squared cosines, contributions)

- ind
a list of matrices with results for individuals (coordinates, squared cosines, contributions)

- call
a list with informations about input data

- VAR
a list of matrices with results for categories and variables in the input MCAs (coordinates, squared cosines, test-values, variances)

- my.mca
lists the content of the objects in `l_mca`

argument

- RV
a matrix of RV coefficients

## References

Escofier, B. and Pages, J. (1994) "Multiple Factor Analysis (AFMULT package)". *Computational Statistics and Data Analysis*, 18, 121-140.

## Examples

```
data(Taste)
# specific MCA on music variables of Taste example data set
mca1 <- speMCA(Taste[,1:5], excl = c(3,6,9,12,15))
# specific MCA on movie variables of Taste example data set
mca2 <- speMCA(Taste[,6:11], excl = c(3,6,9,12,15,18))
# Multiple Factor Analysis of the two sets of variables
mfa <- multiMCA(list(mca1,mca2))
plot.multiMCA(mfa)
```