friedmanPostHocTestBMR: Perform a posthoc Friedman-Nemenyi test.
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Value See Also Examples

Performs a 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 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.

1 2	friedmanPostHocTestBMR(bmr, measure = NULL, p.value = 0.05, aggregation = "default")

`bmr`	[`BenchmarkResult`] Benchmark result.
`measure`	[`Measure`] Performance measure. Default is the first measure used in the benchmark experiment.
`p.value`	[`numeric(1)`] p-value for the tests. Default: 0.05
`aggregation`	[`character(1)`] “mean” or “default”. See `getBMRAggrPerformances` for details on “default”.

[pairwise.htest]: See 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