Generates data that can be used to plot a
critical differences plot. Computes the critical differences according
to either the
"Bonferroni-Dunn" test or the "Nemenyi" test.
"Bonferroni-Dunn" usually yields higher power as it does not
compare all algorithms to each other, but all algorithms to a
baseline instead.
Learners are drawn on the y-axis according to their average rank.
For test = "nemenyi" a bar is drawn, connecting all groups of not
significantly different learners.
For test = "bd" an interval is drawn arround the algorithm selected
as a baseline. All learners within this interval are not signifcantly different
from the baseline.
Calculation:
$$CD = q_{\alpha} \sqrt{\left(\frac{k(k+1)}{6N}\right)}$$
Where \(q_\alpha\) is based on the studentized range statistic.
See references for details.
generateCritDifferencesData(
bmr,
measure = NULL,
p.value = 0.05,
baseline = NULL,
test = "bd"
)(BenchmarkResult) Benchmark result.
(Measure) Performance measure. Default is the first measure used in the benchmark experiment.
(numeric(1))
P-value for the critical difference. Default: 0.05
(character(1)): (learner.id)
Select a learner.id as baseline for the test = "bd"
("Bonferroni-Dunn") critical differences
diagram. The critical difference interval will then be positioned arround this learner.
Defaults to best performing algorithm.
For test = "nemenyi", no baseline is needed as it performs all pairwise
comparisons.
(character(1))
Test for which the critical differences are computed.
“bd” for the Bonferroni-Dunn Test, which is comparing all
classifiers to a baseline, thus performing a comparison
of one classifier to all others.
Algorithms not connected by a single line are statistically different
from the baseline.
“nemenyi” for the PMCMR::posthoc.friedman.nemenyi.test
which is comparing all classifiers to each other. The null hypothesis that
there is a difference between the classifiers can not be rejected for all
classifiers that have a single grey bar connecting them.
(critDifferencesData). List containing:
(data.frame) containing the info for the descriptive part of the plot
(list) of class pairwise.htest
contains the calculated
PMCMR::posthoc.friedman.nemenyi.test
(list) containing info on the critical difference and its positioning
baseline chosen for plotting
p.value used for the PMCMR::posthoc.friedman.nemenyi.test and for computation of the critical difference
Other generate_plot_data:
generateCalibrationData(),
generateFeatureImportanceData(),
generateFilterValuesData(),
generateLearningCurveData(),
generatePartialDependenceData(),
generateThreshVsPerfData(),
plotFilterValues()
Other benchmark:
BenchmarkResult,
batchmark(),
benchmark(),
convertBMRToRankMatrix(),
friedmanPostHocTestBMR(),
friedmanTestBMR(),
getBMRAggrPerformances(),
getBMRFeatSelResults(),
getBMRFilteredFeatures(),
getBMRLearnerIds(),
getBMRLearnerShortNames(),
getBMRLearners(),
getBMRMeasureIds(),
getBMRMeasures(),
getBMRModels(),
getBMRPerformances(),
getBMRPredictions(),
getBMRTaskDescs(),
getBMRTaskIds(),
getBMRTuneResults(),
plotBMRBoxplots(),
plotBMRRanksAsBarChart(),
plotBMRSummary(),
plotCritDifferences(),
reduceBatchmarkResults()