permuteRelief {AppliedPredictiveModeling} | R Documentation |
This function uses a permutation approach to determining the relative magnitude of Relief scores (Kira and Rendell, 1992 and Kononenko, 1994).
permuteRelief(x, y, nperm = 100, ...)
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 |
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.
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
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.
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)