\name{biplot.pcaRes}
\alias{biplot,pcaRes-method}
\alias{biplot.pcaRes}

\title{Plot a overlaid scores and loadings plot}

\description{Visualize two-components simultaneously}
\usage{biplot.pcaRes(x, choices=1:2, scale=1, pc.biplot=FALSE, ...)}
\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 inner products between variables approximate
    covariances and distances between observations approximate
    Mahalanobis distance. }

  \item{...}{optional arguments to be passed to \code{biplot.default}.}
}

\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.
}
\author{Kevin Wright, Adapted from \code{biplot.prcomp}}
\seealso{\code{prcomp}, \code{pca}, \code{princomp}}
\examples{
data(iris)
pcIr <- pca(iris[,1:4])
biplot(pcIr)
}
\keyword{multivariate}