tree(formula=formula(data), data=sys.parent(), weights, subset, na.action=na.pass, control=tree.control(nobs, ...), method="recursive.partition", split=c("deviance", "gini"), model=NULL, x=F, y=T, wts=T, ...)
formula
|
A formula expression. The left-hand-side (response)
should be either a numerical vector when a regression tree will be
fitted or a factor, when a classification tree is produced. The
right-hand-side should be a series of numeric or factor or ordered
variables separated by + ; there should be no interaction terms.
|
data
|
A data frame in which to preferentially interpret
formula , weights and subset .
|
weights
| Vector of non-negative observational weights; fractional weights are allowed. |
subset
| An expression specifying the subset of cases to be used. |
na.action
|
A function to filter missing data from the model
frame. The default is na.pass (to do nothing) as tree
handles missing values (by dropping them down the tree as far as possible).
|
control
|
A list as returned by tree.control .
|
method
|
character string giving the method to use. The only other
useful value is "model.frame" .
|
split
| Splitting criterion to use. |
model
|
If this argument is itself a model frame, then the
formula and data arguments are ignored, and
model is used to define the model.
|
x
| If TRUE, the matrix of variables for each case is returned. |
y
| If TRUE, the response variable is returned. |
wts
| If TRUE, the weights are returned. |
...
|
Additional arguments that are passed to
tree.control . Normally used for mincut , minsize
or mindev .
|
Factor predictor variables can have up to 32 levels. This limit is imposed for ease of labelling, but since their use in a classification tree with three or more levels in a response involves a search over 2^(k-1)-1 groupings for k levels, the practical limit is much less.
Ripley, B.D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge.
tree.control
, prune.tree
,
predict.tree
, snip.tree
library(MASS) data(cpus) cpus.ltr <- tree(log10(perf) ~ syct+mmin+mmax+cach+chmin+chmax, cpus) cpus.ltr summary(cpus.ltr) plot(cpus.ltr); text(cpus.ltr) data(iris) ir.tr <- tree(Species ~., iris) ir.tr summary(ir.tr)