Usage
rpart(formula, data, weights, subset, na.action = na.rpart, method,
model = FALSE, x = FALSE, y = TRUE, parms, control, cost, ...)Arguments
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 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 intellegent guess.
If y is a survival object, then method="exp"
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 p
control
options that control details of the rpart algorithm.
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
...
arguments to rpart.control may also be specified in the call to
rpart. They are checked against the list of valid arguments.