\HeaderA{predict.rpart}{Predictions from a Fitted Rpart Object}{predict.rpart}
\keyword{tree}{predict.rpart}
\begin{Description}\relax
Returns a vector of predicted responses from a fitted \code{rpart} object.
\end{Description}
\begin{Usage}
\begin{verbatim}
## S3 method for class 'rpart':
predict(object, newdata = list(),
       type = c("vector", "prob", "class", "matrix"), ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] fitted model object of class \code{rpart}.  This is assumed to be
the result of some function that produces an object with the same named 
components as that returned by the \code{rpart} function. 

\item[\code{newdata}] data frame containing the values at which predictions are required. 
The predictors referred to in the right side of
\code{formula(object)} must be present by name in \code{newdata}.
If missing, the fitted values are returned. 

\item[\code{type}] character string denoting the type of predicted value returned.  If
the \code{rpart} object is a classification tree, then the default is to
return \code{prob} predictions, a matrix whose columns are the
probability of the first, second, etc. class.  (This agrees with the
default behavior of \code{\LinkA{tree}{tree}}).  Otherwise, a vector
result is returned.

\item[\code{...}] further arguments passed to or from other methods.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
This function is a method for the generic function predict for class
\code{rpart}. It can be invoked by calling \code{predict} for an object
of the appropriate class, or directly by calling \code{predict.rpart}
regardless of the class of the object.
\end{Details}
\begin{Value}
A new object is obtained by
dropping \code{newdata} down the object.  For factor predictors, if an
observation contains a level not used to grow the tree, it is left at
the deepest possible node and \code{frame\$yval} at the node is the
prediction.

If \code{type="vector"}:\\
vector of predicted responses.
For regression trees this is the mean response at the node, for Poisson
trees it is the estimated response rate, and for classification trees
it is the predicted class.

If \code{type="prob"}:\\
(for a classification tree) a matrix of class probabilities.

If \code{type="matrix"}:\\
a matrix of the full responses (\code{frame\$yval2} if this exists,
otherwise \code{frame\$yval}).
For regression trees, this is the mean response, for Poisson trees it
is the response rate and the number of events at that node in the fitted
tree, and for classification trees it is the concatonation of the
predicted class, the class counts at that node in the fitted tree, and
the class probabilities.

If \code{type="class"}:\\
(for a classification tree) a factor of classifications based on the
responses.
\end{Value}
\begin{SeeAlso}\relax
\code{\LinkA{predict}{predict}},  \code{\LinkA{rpart.object}{rpart.object}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
z.auto <- rpart(Mileage ~ Weight, car.test.frame)
predict(z.auto)

fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis)
predict(fit, type="prob")   # class probabilities (default)
predict(fit, type="vector") # level numbers
predict(fit, type="class")  # factor
predict(fit, type="matrix") # level number, class frequencies, probabilities

sub <- c(sample(1:50, 25), sample(51:100, 25), sample(101:150, 25))
fit <- rpart(Species ~ ., data=iris, subset=sub)
fit
table(predict(fit, iris[-sub,], type="class"), iris[-sub, "Species"])
\end{ExampleCode}
\end{Examples}

