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, ...)
a formula, with a response but no interaction terms. If this a a data frome, that is taken as the model frame (see model.frame).
an optional data frame in which to interpret the variables named in the formula.
optional case weights.
optional expression saying that only a subset of the rows of the data should be used in the fit.
the default action deletes all observations for which y is missing, but keeps those in which one or more predictors are missing.
one of "anova", "poisson", "class" or "exp". 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’.
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.
keep a copy of the x matrix in the result.
keep a copy of the dependent variable in the result. If missing and model is supplied this defaults to FALSE.
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.
a list of options that control details of the rpart algorithm. See rpart.control.
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.
arguments to rpart.control may also be specified in the call to rpart. They are checked against the list of valid arguments.

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.


An object of class rpart. See rpart.object.


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

See Also

rpart.control, rpart.object, summary.rpart, print.rpart

  • rpart
library(rpart) 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 = 0.05)) par(mfrow = c(1,2), xpd = NA) # otherwise on some devices the text is clipped plot(fit) text(fit, use.n = TRUE) plot(fit2) text(fit2, use.n = TRUE)
Documentation reproduced from package rpart, version 4.1-10, License: GPL-2 | GPL-3

Community examples

sayandutta13@gmail.com at Feb 28, 2017 rpart v4.1-10

# Set random seed. Don't remove this line. set.seed(1) # Take a look at the iris dataset str(iris) summary(iris) # A decision tree model has been built for you tree <- rpart(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris, method = "class") # A dataframe containing unseen observations unseen <- data.frame(Sepal.Length = c(5.3, 7.2), Sepal.Width = c(2.9, 3.9), Petal.Length = c(1.7, 5.4), Petal.Width = c(0.8, 2.3)) # Predict the label of the unseen observations. Print out the result. predict(tree, unseen, type = "class")