\name{predict.pcaRes}
\alias{predict.pcaRes}
\alias{predict,pcaRes-method}
\title{Predict values from PCA.}
\description{This function extracts the predict values from
  a pcaRes object for the PCA methods SVD, Nipals, PPCA and BPCA

  Newdata is first centered if the PCA model was and then scores
  (\eqn{T}) and data (\eqn{X}) is 'predicted' according to :

  \eqn{\hat{T}=X_{new}P}{That=XnewP}

  \eqn{\hat{X}_{new}=\hat{T}P'}{Xhat=ThatP'}

  Missing values are set to zero before matrix multiplication to achieve
  NIPALS like treatment of missing values. 

}

\usage{predict.pcaRes(object, newdata, pcs=nPcs(object), ...)}
\arguments{
  \item{object}{\code{pcaRes} the \code{pcaRes} object of interest.}
  \item{newdata}{\code{matrix} new data with same number of columns as
    the used to compute \code{object}.} 
  \item{pcs}{\code{numeric} The number of PC's to consider}
  \item{...}{Not passed on anywhere, included for S3 consistency.}
}
\value{A list with the following components:
  \item{scores}{The predicted scores}
  \item{x}{The predicted data}
}
\keyword{multivariate}
\examples{
data(iris)
hidden <- sample(nrow(iris), 50)
pcIr <- pca(iris[-hidden,1:4])
pcFull <- pca(iris[,1:4])
irisHat <- predict(pcIr, iris[hidden,1:4])
cor(irisHat$scores[,1], scores(pcFull)[hidden,1])
}
\author{Henning Redestig <henning[at]psc.riken.jp>}