adjustRom: Rom correction of p-values

In scmamp: Statistical Comparison of Multiple Algorithms in Multiple Problems

Description

This function takes the particular list of possible hypthesis to correct for multiple testing, as defined in Rom (1990)

Usage

adjustRom(pvalues, alpha = 0.05)

Arguments

pvalues	Raw p-values in either a vector or a matrix. Note that in case the p-values are in a matrix, all the values are used for the correction. Therefore, if the matrix contains repeated values (as those output by some methods in this package), the repetitions have to be removed.
alpha	value for the averall test

Details

The test has been implemented according to the version in Garciaet al. (2010), page 2680-2682.

Value

A vector or matrix with the corrected p-values

References

S. Garcia, A. Fernandez, J. Luengo and F. Herrera (2010) Advanced nonparametric tests for multiple comparison in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences, 180, 2044-2064.

D. M. Rom (1990) A sequentially rejective test procedure based on a modified Bonferroni inequality. Biometrika, 77, 663-665.

Examples

data(data_gh_2008)
raw.pvalues <- friedmanPost(data.gh.2008)
raw.pvalues
adjustRom(raw.pvalues, alpha=0.05)

Example output

                   C4.5   k-NN(k=1)   NaiveBayes       Kernel         CN2
C4.5                 NA 0.004848763 8.064959e-01 4.486991e-08 0.012763008
k-NN(k=1)  4.848763e-03          NA 1.011233e-02 7.963489e-03 0.743971478
NaiveBayes 8.064959e-01 0.010112334           NA 1.736118e-07 0.024744672
Kernel     4.486991e-08 0.007963489 1.736118e-07           NA 0.002880485
CN2        1.276301e-02 0.743971478 2.474467e-02 2.880485e-03          NA
                   C4.5  k-NN(k=1)   NaiveBayes       Kernel        CN2
C4.5                 NA 0.06630175 1.000000e+00 8.759529e-07 0.09989494
k-NN(k=1)  6.630175e-02         NA 9.885489e-02 9.336980e-02 1.00000000
NaiveBayes 1.000000e+00 0.09885489           NA 3.050818e-06 0.14546923
Kernel     8.759529e-07 0.09336980 3.050818e-06           NA 0.04500258
CN2        9.989494e-02 1.00000000 1.454692e-01 4.500258e-02         NA

scmamp documentation built on May 1, 2019, 10:10 p.m.