Fit a `rpart`

model

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

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.

An object of class `rpart`

. See `rpart.object`

.

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`

.

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

# 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) # }