The ade4 Package - NexTag Supports Open Source Initiatives

dudi.mix 89
Details
If df contains only quantitative variables, this is equivalent to a normed PCA.
If df contains only factors, this is equivalent to a MCA.
Ordered factors are replaced by poly(x,deg=2).
This analysis generalizes the Hill and Smith method.
The principal components of this analysis are centered and normed vectors maximizing the sum of
the:
squared correlation coefficients with quantitative variables
squared multiple correlation coefficients with polynoms
correlation ratios with factors.
Value
Returns a list of class mix and dudi (see dudi) containing also
index
assign
cr
a factor giving the type of each variable : f = factor, o = ordered, q = quantitative
a factor indicating the initial variable for each column of the transformed table
a data frame giving for each variable and each score:
the squared correlation coefficients if it is a quantitative variable
the correlation ratios if it is a factor
the squared multiple correlation coefficients if it is ordered
Author(s)
Daniel Chessel
Anne B Dufour 〈dufour@biomserv.univ-lyon1.fr〉
References
Hill, M. O., and A. J. E. Smith. 1976. Principal component analysis of taxonomic data with multistate
discrete characters. Taxon, 25, 249-255.
De Leeuw, J., J. van Rijckevorsel, and . 1980. HOMALS and PRINCALS - Some generalizations
of principal components analysis. Pages 231-242 in E. Diday and Coll., editors. Data Analysis and
Informatics II. Elsevier Science Publisher, North Holland, Amsterdam.
Kiers, H. A. L. 1994. Simple structure in component analysis techniques for mixtures of qualitative
ans quantitative variables. Psychometrika, 56, 197-212.
Examples
data(dunedata)
dd1