moreparty (version 0.4)

EasyTreeVarImp: Variable importance for conditional inference trees.

Description

Variable importance for partykit conditional inference trees, using various performance measures.

Usage

EasyTreeVarImp(ct, nsim = 1)

Value

A data frame of variable importances, with variables as rows and performance measures as columns.

Arguments

ct

A tree of class constparty (as returned by ctree from partykit package).

nsim

Integer specifying the number of Monte Carlo replications to perform. Default is 1. If nsim > 1, the results from each replication are simply averaged together.

Author

Nicolas Robette

Details

If the response variable is a factor, AUC (if response is binary), accuracy, balanced accuracy and true predictions by class are used. If the response is numeric, r-squared and Kendall's tau are used.

References

Hothorn T, Hornik K, Van De Wiel MA, Zeileis A. "A lego system for conditional inference". The American Statistician. 60:257–263, 2006.

Hothorn T, Hornik K, Zeileis A. "Unbiased Recursive Partitioning: A Conditional Inference Framework". Journal of Computational and Graphical Statistics, 15(3):651-674, 2006.

See Also

ctree

Examples

  data(iris)
  iris2 = iris
  iris2$Species = factor(iris$Species == "versicolor")
  iris.ct = partykit::ctree(Species ~ ., data = iris2)
  EasyTreeVarImp(iris.ct, nsim = 1)