Recursive Partitioning and Regression Trees
Description
Fit a rpart
model
Usage
1 2 
Arguments
formula 
a formula, with a response but no interaction
terms. If this a a data frome, that is taken as the model frame
(see 
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

method 
one of Alternatively, 
model 
if logical: keep a copy of the model frame in the result?
If the input value for 
x 
keep a copy of the 
y 
keep a copy of the dependent variable in the result. If
missing and 
parms 
optional parameters for the splitting function. 
control 
a list of options that control details of the

cost 
a vector of nonnegative 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 
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
reimplementation 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
rpart.control
, rpart.object
,
summary.rpart
, print.rpart
Examples
1 2 3 4 5 6 7 8 9 10  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)
