The problem of comparing a new solution method against existing ones to find statistically significant differences arises very often in sciences and engineering. When the problem instance being solved is defined by several parameters, assessing a number of methods with respect to many problem configurations simultaneously becomes a hard task. Some visualization technique is required for presenting a large number of statistical significance results in an easily interpretable way. Here we review an existing color-based approach called Statistical Ranking Color Scheme (SRCS) for displaying the results of multiple pairwise statistical comparisons between several methods assessed separately on a number of problem configurations. We introduce an R package implementing SRCS, which performs all the pairwise statistical tests from user data and generates customizable plots. We demonstrate its applicability on two examples from the areas of dynamic optimization and machine learning, in which several algorithms are compared on many problem instances, each defined by a combination of parameters.
factorplot, SRCS, e1071, RWeka
MachineLearning, Cluster, Distributions, Environmetrics, Multivariate, NaturalLanguageProcessing, Psychometrics
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For attribution, please cite this work as
Villacorta & Sáez, "The R Journal: SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R", The R Journal, 2015
BibTeX citation
@article{RJ-2015-023,
author = {Villacorta, Pablo J. and Sáez, José A.},
title = {The R Journal: SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R},
journal = {The R Journal},
year = {2015},
note = {https://doi.org/10.32614/RJ-2015-023},
doi = {10.32614/RJ-2015-023},
volume = {7},
issue = {2},
issn = {2073-4859},
pages = {89-104}
}