Recursive Partitioning and Regression Trees

Fit a rpart model

rpart(formula, data, weights, subset, na.action = na.rpart, method,
      model = FALSE, x = FALSE, y = TRUE, parms, control, cost,
      ncand, seed, redmem = FALSE, ...)

Arguments

formula	a formula, with a response but no interaction terms. If this a a data frame, that is taken as the model frame (see `model.frame).`
data	an optional data frame in which to interpret the variables named in the formula.
weights	optional case weights.
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"`, `"exp"`, `"gamma"` or `"lognormal"`. If `method` is missing then the routine tries to make an intelligent 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 specify the method directly, especially as more criteria may added to the function in future. Alternatively, `method` can be a list of functions named `init`, `split` and `eval`. Examples are given in the file `tests/usersplits.R` in the sources, and in the vignettes ‘User Written Split Functions’.
model	if logical: 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` function), then this frame is used rather than constructing new data.
x	keep a copy of the `x` matrix in the result.
y	keep a copy of the dependent variable in the result. If missing and `model` is supplied this defaults to `FALSE`.
parms	optional parameters for the splitting function. Anova splitting has no parameters. Poisson splitting 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 diagonal and positive off-diagonal elements. The splitting index can be `gini` or `information`. The default priors are proportional to the data counts, the losses default to 1, and the split defaults to `gini`.
control	a list of options that control details of the `rpart` algorithm. See `rpart.control`.
cost	a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.
ncand	integer specifying the number of randomly chosen variable candidates to consider at each node to find the optimal split.
seed	seed for the random number generator which chooses the `ncand` variable split candidates.
redmem	boolean whether to reduce the memory footprint of the fitted `rpart` tree by eliminating non-essential elements from the fit (mainly needed in a random forest).
...	arguments to `rpart.control` may also be specified in the call to `rpart`. They are checked against the list of valid arguments.

Details

This differs from the tree function in S mainly in its handling of surrogate variables. In most details it follows Breiman et. al (1984) quite closely. R package tree provides a re-implementation of tree.

Value

An object of class rpart. See rpart.object.

References

Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.

Examples