\name{predict.psych}
\alias{predict.psych}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Prediction function for factor analysis or principal components
}
\description{Finds predicted factor/component scores from a factor analysis or components analysis of data set A predicted to data set B.  Predicted factor scores use the weights matrix used to find estimated factor scores, predicted components use the loadings matrix.
}
\usage{
\method{predict}{psych}(object, data,old.data,...)
}

\arguments{
  \item{object}{the result of a factor analysis or principal components analysis of data set A}
  \item{data}{Data set B, of the same number of variables as data set A.}
  \item{old.data}{if specified, the data set B will be standardized in terms of values from the old data}
  \item{...}{More options to pass to predictions }
}

\value{
Predicted factor/components scores.
}

\author{William Revelle
}
\note{Thanks to Reinhold Hatzinger for the suggestion and request}
\seealso{ \code{\link{fa}}, \code{\link{principal}}
}
\examples{
set.seed(42)
x <- sim.item(12,500)
f2 <- fa(x[1:250,],2,scores="regression")  # a two factor solution
p2 <- principal(x[1:250,],2,scores=TRUE)  # a two component solution
round(cor(f2$scores,p2$scores),2)  #correlate the components and factors from the A set
#find the predicted scores (The B set)
pf2 <- predict(f2,x[251:500,])
pp2 <- predict(p2,x[251:500,])
round(cor(pf2,pp2),2)   #find the correlations in the B set
#test how well these predicted scores match the factor scores from the second set
fp2 <- fa(x[251:500,],2,scores=TRUE)
round(cor(fp2$scores,pf2),2)
#note that the signs of the factors in the second set are arbitrary
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{multivariate}
\keyword{ models }