tree | R Documentation |
A tree is grown by binary recursive partitioning using the response in the specified formula and choosing splits from the terms of the right-hand-side.
tree(formula, data, weights, subset, na.action = na.pass, control = tree.control(nobs, ...), method = "recursive.partition", split = c("deviance", "gini"), model = FALSE, x = FALSE, y = TRUE, wts = TRUE, ...)
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
variables separated by |
data |
A data frame in which to preferentially interpret
|
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 |
control |
A list as returned by |
method |
character string giving the method to use. The only other
useful value is |
split |
Splitting criterion to use. |
model |
If this argument is itself a model frame, then the
|
x |
logical. If true, the matrix of variables for each case is returned. |
y |
logical. If true, the response variable is returned. |
wts |
logical. If true, the weights are returned. |
... |
Additional arguments that are passed to
|
A tree is grown by binary recursive partitioning using the response in the specified formula and choosing splits from the terms of the right-hand-side. Numeric variables are divided into X < a and X > a; the levels of an unordered factor are divided into two non-empty groups. The split which maximizes the reduction in impurity is chosen, the data set split and the process repeated. Splitting continues until the terminal nodes are too small or too few to be split.
Tree growth is limited to a depth of 31 by the use of integers to label nodes.
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.
The value is an object of class "tree"
which has components
frame |
A data frame with a row for each node, and
|
where |
An integer vector giving the row number of the frame detailing the node to which each case is assigned. |
terms |
The terms of the formula. |
call |
The matched call to |
model |
If |
x |
If |
y |
If |
wts |
If |
and attributes xlevels
and, for classification trees,
ylevels
.
A tree with no splits is of class "singlenode"
which inherits
from class "tree"
.
B. D. Ripley
Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge. Chapter 7.
tree.control
, prune.tree
,
predict.tree
, snip.tree
data(cpus, package="MASS") cpus.ltr <- tree(log10(perf) ~ syct+mmin+mmax+cach+chmin+chmax, cpus) cpus.ltr summary(cpus.ltr) plot(cpus.ltr); text(cpus.ltr) ir.tr <- tree(Species ~., iris) ir.tr summary(ir.tr)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.