

   RReeccuurrssiivvee PPaarrttiittiioonniinngg aanndd RReeggrreessssiioonn TTrreeeess

        rpart(formula, data, weights, subset, na.action=na.rpart, method,
                  model=F, x=F, y=T, parms, control=rpart.control(...), ...)

   AArrgguummeennttss::

    formula: a formula, as in the `lm' function.

       data: an optional data frame in which to interpret the
             variables named in the formula

    weights: optional weights (currently ignored).

     subset: optional expression saying that only a subset of
             the rows of the data should be used in the fit.

   na.action: The default action deletes all observations for
             which `y' is missing, but keeps those in which one
             or more predictors are missing.

     method: one of `"anova"', `"poisson"', `"class"' or
             `"exp"'.  If `method' is missing then the routine
             tries to make an intellegent guess.  If `y' is a
             survival object, then `method="exp"' is assumed,
             if `y' has 2 columns then `method="poisson"' is
             assumed, if `y' is a factor then `method="class"'
             is assumed, otherwise `method="anova"' is assumed.
             It is wisest to specifiy the method directly,
             especially as more criteria are added to the func-
             tion.

      model: keep a copy of the model frame in the result.  If
             the input value for `model' is a model frame
             (likely from an earlier call to the `rpart' func-
             tion), then this frame is used rather than con-
             structing new data.

          x: keep a copy of the `x' matrix in the result.

          y: keep a copy of the dependent variable in the
             result.

      parms: optional parameters for the splitting function.
             Anova splitting has no parameters.  Poisson split-
             ting has a single parameter, the coefficient of
             variation of the prior distribution on the rates.
             The default value is 1.  Exponential splitting has
             the same parameter as Poisson.  For classification
             splitting, the list can contain any of: the vector
             of prior probabilities (component `prior'), the
             loss matrix (component `loss') or the splitting
             index (component `split').  The priors must be
             positive and sum to 1.  The loss matrix must have
             zeros on the diagnoal and positive off-diagonal
             elements.  The splitting index can be `gini' or
             `information'.  The default priors are propor-
             tional to the data counts, the losses default to
             1, and the split defaults to gini.

    control: options that control details of the `rpart' algo-
             rithm.

        ...: arguments to `rpart.control' may also be specified
             in the call to `rpart'.

   DDeessccrriippttiioonn::

        Fit a `rpart' model

   DDeettaaiillss::

        This differs from the `tree' function mainly in its
        handling of surrogate variables.

   VVaalluuee::

        an object of class `rpart', a superset of class `tree'.

   RReeffeerreenncceess::

        Breiman, Friedman, Olshen, and Stone. (1984) Classifi-
        cation and Regression Trees.  Wadsworth.

   SSeeee AAllssoo::

        `rpart.control', `rpart.object', `tree', `sum-
        mary.rpart', `print.rpart'

   EExxaammpplleess::

        > fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis)
        > fit2 <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis,
                        parms=list(prior=c(.65,.35), split='information'))
        > fit3 <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis,
                        control=rpart.control(cp=.05))
        > par(mfrow=c(1,2))
        > plot(fit)
        > text(fit,use.n=T)
        > plot(fit2)
        > text(fit2,use.n=T)

