GDAtools (version 1.6)

multiMCA: Performs Multiple Factor Analysis

Description

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

Usage

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)

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, correlation between variables and axes, square cosine, contributions)

ind

a list of matrices with results for individuals (coordinates, square cosine, 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, square cosine, test-values, variances)

my.mca

lists the content of the objects in l_mca argument

RV

a matrix of RV coefficients

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).

References

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

See Also

plot.multiMCA, varsup, speMCA, csMCA, MFA, PCA

Examples

# NOT RUN {
## Performs a specific MCA on music variables of 'Taste' example data set,
## another one on movie variables of 'Taste' example data set, 
## and then a Multiple Factor Analysis.
data(Taste)
getindexcat(Taste[,1:5])
mca1 <- speMCA(Taste[,1:5],excl=c(3,6,9,12,15))
getindexcat(Taste[,6:11])
mca2 <- speMCA(Taste[,6:11],excl=c(3,6,9,12,15,18))
mfa <- multiMCA(list(mca1,mca2))
plot.multiMCA(mfa)
# }