  Kiers (1991) considered the orthogonal rotation in PCAMIX, a principal
component method for a mixture of qualitative and quantitative variables.
PCAMIX includes the ordinary principal component analysis (PCA) and multiple
correspondence analysis (MCA) as special cases. In this paper, we give a new
presentation of PCAMIX where the principal components and the squared loadings
are obtained from a Singular Value Decomposition. The loadings of the
quantitative variables and the principal coordinates of the categories of the
qualitative variables are also obtained directly. In this context, we propose a
computationaly efficient procedure for varimax rotation in PCAMIX and a direct
solution for the optimal angle of rotation. A simulation study shows the good
computational behavior of the proposed algorithm. An application on a real data
set illustrates the interest of using rotation in MCA. All source codes are
available in the R package "PCAmixdata".
