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)
a list of objects of class
number of dimensions kept in the results (default is 5)
whether RV coefficients should be computed or not (default is FALSE, which makes the function execute faster)
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).
Returns an object of class
multiMCA, i.e. a list:
a list of numeric vector for eigenvalues, percentage of variance and cumulative percentage of variance
a list of matrices with results for input MCAs components (coordinates, correlations between variables and axes, squared cosines, contributions)
a list of matrices with results for individuals (coordinates, squared cosines, contributions)
a list with informations about input data
a list of matrices with results for categories and variables in the input MCAs (coordinates, squared cosines, test-values, variances)
lists the content of the objects in
a matrix of RV coefficients
Escofier, B. and Pages, J. (1994) "Multiple Factor Analysis (AFMULT package)". Computational Statistics and Data Analysis, 18, 121-140.
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)