R: ranger variable importance p-values

importance_pvalues {ranger}

R Documentation

ranger variable importance p-values

Description

Compute variable importance with p-values.

Usage

importance_pvalues(x, method = c("janitza", "altmann"),
  num.permutations = 100, formula = NULL, data = NULL, ...)

Arguments

`x`	ranger or holdoutRF object.
`method`	Method to compute p-values. Use "janitza" for the method by Janitza et al. (2015) or "altmann" for the non-parametric method by Altmann et al. (2010).
`num.permutations`	Number of permutations. Used in the "altmann" method only.
`formula`	Object of class formula or character describing the model to fit. Used in the "altmann" method only.
`data`	Training data of class data.frame or matrix. Used in the "altmann" method only.
`...`	Further arguments passed to ranger(). Used in the "altmann" method only.

Value

Variable importance and p-values.

Author(s)

Marvin N. Wright

References

Janitza, S., Celik, E. & Boulesteix, A.-L., (2015). A computationally fast variable importance test for random forests for high-dimensional data. Adv Data Anal Classif http://dx.doi.org/10.1007/s11634-016-0276-4.
Altmann, A., Tolosi, L., Sander, O. & Lengauer, T. (2010). Permutation importance: a corrected feature importance measure, Bioinformatics 26(10):1340-1347.