Performs a PMCMR::posthoc.friedman.nemenyi.test 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"
)
(BenchmarkResult) Benchmark result.
(Measure) Performance measure. Default is the first measure used in the benchmark experiment.
(numeric(1)
)
p-value for the tests. Default: 0.05
(character(1)
)
“mean” or “default”. See getBMRAggrPerformances
for details on “default”.
(pairwise.htest
): See PMCMR::posthoc.friedman.nemenyi.test 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()
# NOT RUN {
# see benchmark
# }
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