\HeaderA{mca}{Multiple Correspondence Analysis}{mca}
\aliasA{print.mca}{mca}{print.mca}
\keyword{category}{mca}
\keyword{multivariate}{mca}
\begin{Description}\relax
Computes a multiple correspondence analysis of a set of factors.
\end{Description}
\begin{Usage}
\begin{verbatim}
mca(df, nf = 2, abbrev = FALSE)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{df}] A data frame containing only factors

\item[\code{nf}] The number of dimensions for the MCA. Rarely 3 might be useful.

\item[\code{abbrev}] Should the vertex names be abbreviated?  By default these are of the
form \dQuote{factor.level} but if \code{abbrev = TRUE} they are just
\dQuote{level} which will suffice if the factors have distinct levels.

\end{ldescription}
\end{Arguments}
\begin{Value}
An object of class \code{"mca"}, with components

\begin{ldescription}
\item[\code{rs}] The coordinates of the rows, in \code{nf} dimensions.

\item[\code{cs}] The coordinates of the column vertices, one for each level of each factor.

\item[\code{fs}] Weights for each row, used to interpolate additional factors in \code{predict.mca}.

\item[\code{p}] The number of factors

\item[\code{d}] The singular values for the \code{nf} dimensions.

\item[\code{call}] The matched call.

\end{ldescription}
\end{Value}
\begin{References}\relax
Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.} Fourth edition.  Springer.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{predict.mca}{predict.mca}}, \code{\LinkA{plot.mca}{plot.mca}}, \code{\LinkA{corresp}{corresp}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
farms.mca <- mca(farms, abbrev=TRUE)
farms.mca
plot(farms.mca)
\end{ExampleCode}
\end{Examples}

