nterms: Highest order interaction terms up to search

In yifansha/highinthunt: High Order Interaction Variable Importance Through Random Forest

Description

This function decides the highest order interaction terms that a random forest object could potentially provide.

Usage

nterms(obj)

Arguments

obj	An object of class (rfsrc, grow). Note that when rfsrc function is used, set statistics = TRUE.

Value

tree.depth	Average tree depth. This number will be returned only when n/p>10 that the distribution of minimal depth statistics is less skewed.
min.depth	Average minimal depth from all variables. This number will be returned only when n/p>10 that the distribution of minimal depth statistics is less skewed.
terms	Highest way of interaction terms to detect. When n/p>10, this equals to the rounded value of tree.depth minus min.depth, which is the average depth of maximal Xi-subtrees. Otherwise, this equals to floor(log2((n)/nodesize)).

Author(s)

Yifan Sha and Min Lu

References

Ishwaran H. (2007). Variable importance in binary regression trees and forests, Electronic J. Statist., 1:519-537.

Ishwaran H., Kogalur U.B., Gorodeski E.Z, Minn A.J. and Lauer M.S. (2010). High-dimensional variable selection for survival data. J. Amer. Statist. Assoc., 105:205-217.

Ishwaran H., Kogalur U.B., Chen X. and Minn A.J. (2011). Random survival forests for high-dimensional data. Statist. Anal. Data Mining, 4:115-132.

Examples

data(express)
obj <- rfsrc(y~., data = express[,1:6], statistics = TRUE)
nterms(obj)

yifansha/highinthunt documentation built on July 2, 2020, 6:29 p.m.