# tree.control

0th

Percentile

##### Select Parameters for Tree

A utility function for use with the control argument of tree.

Keywords
tree
##### Usage
tree.control(nobs, mincut = 5, minsize = 10, mindev = 0.01)
##### Arguments
nobs

The number of observations in the training set.

mincut

The minimum number of observations to include in either child node. This is a weighted quantity; the observational weights are used to compute the ‘number’. The default is 5.

minsize

The smallest allowed node size: a weighted quantity. The default is 10.

mindev

The within-node deviance must be at least this times that of the root node for the node to be split.

##### Details

This function produces default values of mincut and minsize, and ensures that mincut is at most half minsize.

To produce a tree that fits the data perfectly, set mindev = 0 and minsize = 2, if the limit on tree depth allows such a tree.

##### Value

A list:

mincut

The maximum of the input or default mincut and 1

minsize

The maximum of the input or default minsize and 2.

nmax

A estimate of the maximum number of nodes that might be grown.

nobs

The input nobs.

##### Note

The interpretation of mindev given here is that of Chambers and Hastie (1992, p. 415), and apparently not what is actually implemented in S. It seems S uses an absolute bound.

##### References

Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S. Wadsworth & Brooks/Cole.

tree