R: Perform a posthoc Friedman-Nemenyi test.

friedmanPostHocTestBMR {mlr} | R Documentation

## Perform a posthoc Friedman-Nemenyi test.

### Description

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.

### 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 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

### 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 2.19.0 Index]