\name{svdPca}
\alias{svdPca}
\title{Perform principal component analysis using singular value
decomposition}
\usage{svdPca(Matrix, nPcs=2, varLimit=1, verbose=interactive(), ...)}
\description{A wrapper function for \code{prcomp} to deliver the result as a
\code{pcaRes} method. Supplied for  compatibility with  the rest
of the pcaMethods package.  It is not recommended to use this
function directely but rather to use  the \code{pca()} wrapper
function.}
\value{A \code{pcaRes} object.}
\seealso{\code{prcomp}, \code{princomp}, \code{pca}}
\keyword{multivariate}
\author{Henning Redestig}
\arguments{\item{Matrix}{Pre-processed (centered and possibly scaled)
numerical matrix samples in rows and variables as columns. No
missing values allowed.}
\item{nPcs}{Number of components that should be extracted.}
\item{varLimit}{Optionally the ratio of variance that should be
explained. \code{nPcs} is ignored if varLimit < 1}
\item{verbose}{Verbose complaints to matrix structure}
\item{...}{Only used for passing through arguments.}}
\examples{data(metaboliteDataComplete)
mat <- prep(t(metaboliteDataComplete))
pc <- svdPca(mat, nPcs=2)
## better use pca()
pc <- pca(t(metaboliteDataComplete), method="svd", nPcs=2)
\dontshow{stopifnot(sum((fitted(pc) - t(metaboliteDataComplete))^2, na.rm=TRUE) < 200)}}