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  (centered and scaled) data matrix, not by using \code{eigen} on the
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  (centered and scaled) data matrix, not by using \code{eigen} on the
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  covariance matrix.  This
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  covariance matrix.  This
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  is generally the preferred method for numerical accuracy.  The
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  is generally the preferred method for numerical accuracy.  The
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  \code{print} method for the these objects prints the results in a nice
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  \code{print} method for the these objects prints the results in a nice
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  format and the \code{plot} method produces a scree plot.
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  format and the \code{plot} method produces a scree plot.
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  If the input has more variables than units then \code{\link{La.svd}}
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  is used, and the results may differ in sign from versions prior to 1.7.0.
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}
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}
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\note{
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\note{
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  The signs of the columns of the rotation matrix are arbitrary, and
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  The signs of the columns of the rotation matrix are arbitrary, and
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  so may differ between different programs for PCA, and even between
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  so may differ between different programs for PCA, and even between
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  different builds of \R.
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  different builds of \R.
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  Venables, W. N. and B. D. Ripley (1997, 9)
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  Venables, W. N. and B. D. Ripley (1997, 9)
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  \emph{Modern Applied Statistics with S-PLUS}, Springer-Verlag.
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  \emph{Modern Applied Statistics with S-PLUS}, Springer-Verlag.
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}
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}
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\seealso{
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\seealso{
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  \code{\link{biplot.prcomp}},
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  \code{\link{princomp}}, \code{\link{cor}}, \code{\link{cov}},
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  \code{\link{princomp}}, \code{\link{cor}}, \code{\link{cov}},
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  \code{\link{svd}}, \code{\link{eigen}}.
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  \code{\link{svd}}, \code{\link{eigen}}.
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}
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}
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\examples{
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\examples{
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## the variances of the variables in the
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## the variances of the variables in the
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data(USArrests)
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data(USArrests)
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prcomp(USArrests)  # inappropriate
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prcomp(USArrests)  # inappropriate
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prcomp(USArrests, scale = TRUE)
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prcomp(USArrests, scale = TRUE)
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plot(prcomp(USArrests))
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plot(prcomp(USArrests))
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summary(prcomp(USArrests, scale = TRUE))
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summary(prcomp(USArrests, scale = TRUE))
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biplot(prcomp(USArrests, scale = TRUE))
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}
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}
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\keyword{multivariate}
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\keyword{multivariate}