\name{biplot.pcaRes}
\title{Plot a overlaid scores and loadings plot}
\usage{biplot.pcaRes(x, choices=1:2, scale=1, pc.biplot=FALSE, ...)}
\description{Visualize two-components simultaneously}
\details{This is a method for the generic function 'biplot'.  There is
considerable confusion over the precise definitions: those of the
original paper, Gabriel (1971), are followed here.  Gabriel and
Odoroff (1990) use the same definitions, but their plots actually
correspond to \code{pc.biplot = TRUE}.}
\value{a plot is produced on the current graphics device.}
\alias{biplot.pcaRes}
\seealso{\code{prcomp}, \code{pca}, \code{princomp}}
\author{Kevin Wright, Adapted from \code{biplot.prcomp}}
\keyword{multivariate}
\arguments{\item{x}{a pcaRes object}
\item{choices}{which two pcs to plot}
\item{scale}{The variables are scaled by
\eqn{\lambda^{scale}}{lambda^scale} and the observations are
scaled    by \eqn{\lambda^{scale}}{lambda ^ (1-scale)} where
\code{lambda} are  the singular values as computed by
\code{princomp}.  Normally  \eqn{0\le{}scale\le{}1}{0 <= scale <=
1}, and a warning will be issued if the specified 'scale' is
outside this range.}
\item{pc.biplot}{If true, use what Gabriel (1971) refers to as a
"principal component biplot", with \eqn{\lambda=1}{lambda = 1} and
observations scaled up by sqrt(n) and variables scaled down by
sqrt(n). Then the inner products between variables approximate
covariances and distances between observations approximate
Mahalanobis distance.}
\item{...}{optional arguments to be passed to
\code{biplot.default}.}}
\examples{data(iris)
pcIr <- pca(iris[,1:4])
biplot(pcIr)}