multiMCA | R Documentation |
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)
l_mca |
a list of objects of class |
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) |
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:
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 |
RV |
a matrix of RV coefficients |
Nicolas Robette
Escofier, B. and Pages, J. (1994) "Multiple Factor Analysis (AFMULT package)". Computational Statistics and Data Analysis, 18, 121-140.
plot.multiMCA
, speMCA
, csMCA
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)
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