tree: tree – R documentation

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Fit a Classification or Regression Tree

Description

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.

Usage

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, ...)

Arguments

`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 `+`; there should be no interaction terms. Both `.` and `-` are allowed: regression trees can have `offset` 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. If the argument is logical and true, the model frame is stored as component `model` in the result.
`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 `tree.control`. Normally used for `mincut`, `minsize` or `mindev`.

Details

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.

Value

The value is an object of class "tree" which has components

`frame`	A data frame with a row for each node, and `row.names` giving the node numbers. The columns include `var`, the variable used at the split (or `"<leaf>"` for a terminal node), `n`, the (weighted) number of cases reaching that node, `dev` the deviance of the node, `yval`, the fitted value at the node (the mean for regression trees, a majority class for classification trees) and `split`, a two-column matrix of the labels for the left and right splits at the node. Classification trees also have `yprob`, a matrix of fitted probabilities for each response level.
`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 `Tree`.
`model`	If `model = TRUE`, the model frame.
`x`	If `x = TRUE`, the model matrix.
`y`	If `y = TRUE`, the response.
`wts`	If `wts = TRUE`, the weights.

and attributes xlevels and, for classification trees, ylevels.

A tree with no splits is of class "singlenode" which inherits from class "tree".

Author(s)

B. D. Ripley

References

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.

Examples

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)

tree

Classification and Regression Trees

v1.0-40

GPL-2 | GPL-3

Authors

Brian Ripley [aut, cre]

Initial release

2019-03-01

tree

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

tree

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