R: Perform a posthoc Friedman-Nemenyi test.

friedmanPostHocTestBMR {mlr} | R Documentation

## Perform a posthoc Friedman-Nemenyi test.

### Description

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.

### Usage

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

### Arguments

`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”.

### Value

([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

### See Also

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`

### Examples

    # see benchmark

[Package mlr version 2.12.1 Index]