\name{fitted.pcaRes}
\title{Extract fitted values from PCA.}
\usage{fitted.pcaRes(object, data, nPcs=nP(object), pre=TRUE, post=TRUE, ...)}
\description{Fitted values of a PCA model}
\details{This function extracts the fitted values from a pcaResobject. For
PCA methods like SVD, Nipals, PPCA etc this is basically just the
scores multipled by the loadings and adjusted for pre-processing.
for non-linear PCA the original data is propagated through the
network to obtain the approximated data.}
\value{A matrix representing the fitted data}
\keyword{multivariate}
\alias{fitted.pcaRes}
\author{Henning Redestig}
\arguments{\item{object}{the \code{pcaRes} object of interest.}
\item{data}{For standard PCA methods this can safely be left null
to get scores x loadings but if set, then the scores are obtained
by projecting provided data onto the loadings.  If data contains
missing values the result will be all NA. Non-linear PCA is an
exception, here if data is NULL then data is set to the
completeObs and propaged through the network.}
\item{nPcs}{The number of PC's to consider}
\item{pre}{pre-process \code{data} based on the pre-processing
chosen for the PCA model}
\item{post}{unpre-process the final data (add the center back etc
to get the final estimate)}
\item{...}{Not used}}
\examples{pc <- pca(iris[,1:4], nPcs=4, center=TRUE, scale="uv")
sum( (fitted(pc) - iris[,1:4])^2 )}