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.

### See Also

`ranger`

[Package ranger version 0.7.0 Index]