# rpart

##### Recursive Partitioning and Regression Trees

Fit a `rpart`

model

- Keywords
- tree

##### Usage

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

##### 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"`

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’.- 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.

- …
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.

##### See Also

##### Examples

```
# NOT RUN {
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 4.1-15, License: GPL-2 | GPL-3*

### Community examples

**bestkurisu@outlook.com**at Sep 18, 2020 rpart v4.1-15

tree <- rpart(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris, method = "class") 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(tree, unseen, type = "class")

**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")