new parameters

man/acp.Rd

+\name{acp}
+\alias{acp}
+\alias{pca}
+\alias{print.acp}
+
+\title{Principal component analysis}
+\description{Principal component analysis}
+\usage{
+acp(x,center=TRUE,reduce=TRUE,wI=rep(1,nrow(x)),wV=rep(1,ncol(x)))
+pca(x,center=TRUE,reduce=TRUE,wI=rep(1,nrow(x)),wV=rep(1,ncol(x)))
+print.acp(x, ...) 
+}
+
+
+\arguments{
+\item{x}{Matrix  / data frame}
+\item{center}{a logical value indicating whether we center data}
+\item{reduce}{a logical value indicating whether we "reduce" data i.e. 
+  divide each column by standard deviation}
+\item{wI,wV}{weigth vector for individuals / variables}
+\item{\dots}{arguments to be passed to or from other methods.}
+}
+
+
+
+
+\value{
+  An object of class \bold{acp} 
+  The object is a list with components:
+
+  \item{sdev}{the standard deviations of the principal components.}
+  \item{loadings}{the matrix of variable loadings (i.e., a matrix
+    whose columns contain the eigenvectors).  This is of class
+    \code{"loadings"}: see \code{\link[stats]{loadings}} for its \code{print}
+    method.}
+  \item{scores}{if \code{scores = TRUE}, the scores of the supplied
+    data on the principal components.}
+  \item{eig}{Eigen values}
+}
+
+\details{
+  This function offer a variant of \code{\link[stats]{princomp}} and
+  \code{\link[stats]{prcomp}} functions, with a slightly different
+  graphic representation (see \code{\link{plot.acp}}).
+  }
+
+
+\references{
+  A. Carlier
+  Analyse des données Multidimensionnelles
+  \url{http://www.lsp.ups-tlse.fr/Carlier/enseignement.html}
+}
+
+
+\examples{
+data(lubisch)
+lubisch <- lubisch[,-c(1,8)]
+p <- acp(lubisch)
+plot(p)
+}
+\keyword{multivariate}
+
+\author{Antoine Lucas, \url{http://mulcyber.toulouse.inra.fr/projects/amap/}}
+\seealso{\link{plot.acp},\link{acpgen}, \link[stats]{princomp} }