# permuteRelief: Permutation Statistics for the Relief Algorithm In topepo/AppliedPredictiveModeling: Functions and Data Sets for 'Applied Predictive Modeling'

 permuteRelief R Documentation

## Permutation Statistics for the Relief Algorithm

### Description

This function uses a permutation approach to determining the relative magnitude of Relief scores (Kira and Rendell, 1992 and Kononenko, 1994).

### Usage

``````permuteRelief(x, y, nperm = 100, ...)
``````

### Arguments

 `x` a data frame of predictor data `y` a vector of outcomes `nperm` the number of random permutations of the data `...` options to pass to `attrEval`, such as the exact Relief algorithm, to use

### Details

The scores for each predictor are computed using the original data and after outcome data are randomly scrambled (`nprem` times). The mean and standard deviation of the permuted values are determined and a standardized version of the observed scores are determined by subtracting the permuted means from the original values, then dividing each by the corresponding standard deviation.

### Value

a list with elements

 `standardized ` a vector of standardized predictor scores `permutations ` the values of the permuted scores, for plotting to assess the permutation distribution `observed` the observed scores `options` a list of options passed using ...

Max Kuhn

### References

Kira, K., & Rendell, L. (1992). The feature selection problem: Traditional methods and a new algorithm. Proceedings of the Eleventh International Conference on Machine Learning, 129-129.

Kononenko, I. (1994). Estimating attributes: analysis and extensions of RELIEF. Machine Learning: ECML-94, 171-182.

`attrEval`

### Examples

``````
set.seed(874)
reliefEx3 <- easyBoundaryFunc(500)
reliefEx3\$X1 <- scale(reliefEx3\$X1)
reliefEx3\$X2 <- scale(reliefEx3\$X2)
reliefEx3\$prob <- NULL

standardized <- permuteRelief(reliefEx3[, 1:2], reliefEx3\$class,
## For efficiency, a small number of
## permutations are used here.
nperm = 50,
estimator="ReliefFequalK",
ReliefIterations= 50)

``````

topepo/AppliedPredictiveModeling documentation built on Aug. 25, 2023, 11:12 a.m.