Perform a posthoc Friedman-Nemenyi test.
Performs a PMCMRplus::frdAllPairsNemenyiTest for a BenchmarkResult and a selected measure.
This means all pairwise comparisons of learners are performed. The null
hypothesis of the post hoc test is that each pair of learners is equal. If
the null hypothesis of the included ad hoc stats::friedman.test can be
rejected an object of class pairwise.htest is returned. If not, the
function returns the corresponding friedman.test.
Note that benchmark results for at least two learners on at least two tasks are required.
friedmanPostHocTestBMR( bmr, measure = NULL, p.value = 0.05, aggregation = "default" )
bmr |
(BenchmarkResult) |
measure |
(Measure) |
p.value |
( |
aggregation |
( |
(pairwise.htest): See PMCMRplus::frdAllPairsNemenyiTest for
details.
Additionally two components are added to the list:
f.rejnull (logical(1)):
Whether the according friedman.test rejects
the Null hypothesis at the selected p.value
crit.difference (list(2)):
Minimal difference the mean ranks of two
learners need to have in order to be significantly different
Other benchmark:
BenchmarkResult,
batchmark(),
benchmark(),
convertBMRToRankMatrix(),
friedmanTestBMR(),
generateCritDifferencesData(),
getBMRAggrPerformances(),
getBMRFeatSelResults(),
getBMRFilteredFeatures(),
getBMRLearnerIds(),
getBMRLearnerShortNames(),
getBMRLearners(),
getBMRMeasureIds(),
getBMRMeasures(),
getBMRModels(),
getBMRPerformances(),
getBMRPredictions(),
getBMRTaskDescs(),
getBMRTaskIds(),
getBMRTuneResults(),
plotBMRBoxplots(),
plotBMRRanksAsBarChart(),
plotBMRSummary(),
plotCritDifferences(),
reduceBatchmarkResults()
# see benchmark
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