\HeaderA{predict.lda}{Classify Multivariate Observations by Linear Discrimination}{predict.lda}
\keyword{multivariate}{predict.lda}
\begin{Description}\relax
Classify multivariate observations in conjunction with \code{lda}, and also
project data onto the linear discriminants.
\end{Description}
\begin{Usage}
\begin{verbatim}
## S3 method for class 'lda':
predict(object, newdata, prior = object$prior, dimen,
        method = c("plug-in", "predictive", "debiased"), ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] object  of class \code{"lda"}

\item[\code{newdata}] data frame of cases to be classified or, if \code{object}
has a formula, a data frame with columns of the same names as the
variables used.  A vector will be interpreted
as a row vector.  If newdata is missing, an attempt will be
made to retrieve the data used to fit the \code{lda} object.

\item[\code{prior}] The prior probabilities of the classes, by default the proportions in the
training set or what was set in the call to \code{lda}.

\item[\code{dimen}] the dimension of the space to be used. If this is less than \code{min(p, ng-1)},
only the first \code{dimen} discriminant components are used (except for
\code{method="predictive"}), and only those dimensions are returned in \code{x}.

\item[\code{method}] This determines how the parameter estimation is handled. With \code{"plug-in"}
(the default) the usual unbiased parameter estimates are used and
assumed to be correct. With \code{"debiased"} an unbiased estimator of
the log posterior probabilities is used, and with \code{"predictive"} the
parameter estimates are integrated out using a vague prior.

\item[\code{...}] arguments based from or to other methods

\end{ldescription}
\end{Arguments}
\begin{Details}\relax
This function is a method for the generic function \code{predict()} for
class \code{"lda"}.  It can be invoked by calling \code{predict(x)} for
an object \code{x} of the appropriate class, or directly by calling
\code{predict.lda(x)} regardless of the class of the object.

Missing values in \code{newdata} are handled by returning \code{NA} if the
linear discriminants cannot be evaluated. If \code{newdata} is omitted and
the \code{na.action} of the fit omitted cases, these will be omitted on the
prediction.

This version centres the linear discriminants so that the
weighted mean (weighted by \code{prior}) of the group centroids is at
the origin.
\end{Details}
\begin{Value}
a list with components

\begin{ldescription}
\item[\code{class}] The MAP classification (a factor)

\item[\code{posterior}] posterior probabilities for the classes

\item[\code{x}] the scores of test cases on up to \code{dimen} discriminant variables

\end{ldescription}
\end{Value}
\begin{References}\relax
Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.} Fourth edition.  Springer.

Ripley, B. D. (1996)
\emph{Pattern Recognition and Neural Networks}. Cambridge University Press.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{lda}{lda}}, \code{\LinkA{qda}{qda}}, \code{\LinkA{predict.qda}{predict.qda}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
tr <- sample(1:50, 25)
train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3])
test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
z <- lda(train, cl)
predict(z, test)$class
\end{ExampleCode}
\end{Examples}

