urf_test: urf_test computes UNAIR variable importances and uses them...

In imbs-hl/pranger: Proximities with ranger

urf_test computes UNAIR variable importances and uses them for the Janitza et al. (2018) high-dimensional testing procedure.

Description

urf_test computes UNAIR variable importances and uses them for the Janitza et al. (2018) high-dimensional testing procedure.

Usage

urf_test(data, target = "target", resampling_seed, ...)

Arguments

data	[data.frame] original data
target	[character] name of the artificial target variable
resampling_seed	[integer] resampling seed
...	more parameters to pass to ranger

Value

[data.table] with variable importances and p-values

Author(s)

Cesaire J. K. Fouodo

References

Janitza, S, Celik, E, Boulesteix, AL. (2018). A computationally fast variable importance test for random forests for high-dimensional data. Adv Data Anal Classif.; doi.org: 10.1007/s11634-016-0276-4 Cesaire J. K. Fouodo, Inke R. König Silke Szymczak (2022) Computing variable importance with unsupervised random forests. In review process.

imbs-hl/pranger documentation built on May 15, 2022, 5:27 p.m.