R: Create a bar chart for ranks in a BenchmarkResult.

plotBMRRanksAsBarChart {mlr} | R Documentation

## Create a bar chart for ranks in a BenchmarkResult.

### Description

Plots a bar chart from the ranks of algorithms. Alternatively, tiles can be plotted for every rank-task combination, see `pos` for details. In all plot variants the ranks of the learning algorithms are displayed on the x-axis. Areas are always colored according to the `learner.id`.

### Usage

    plotBMRRanksAsBarChart(bmr, measure = NULL, ties.method = "average",
      aggregation = "default", pos = "stack", order.lrns = NULL,
      order.tsks = NULL, pretty.names = TRUE)

### Arguments

`bmr` | (BenchmarkResult) Benchmark result.
`measure` | (Measure) Performance measure. Default is the first measure used in the benchmark experiment.
`ties.method` | (`character(1)`) See rank for details.
`aggregation` | (`character(1)`) “mean” or “default”. See getBMRAggrPerformances for details on “default”.
`pos` | (`character(1)`) Optionally set how the bars are positioned in ggplot2. Ranks are plotted on the x-axis. “tile” plots a heat map with `task` as the y-axis. Allows identification of the performance in a special task. “stack” plots a stacked bar plot. Allows for comparison of learners within and and across ranks. “dodge” plots a bar plot with bars next to each other instead of stacked bars.
`order.lrns` | (`character(n.learners)`) Character vector with `learner.ids` in new order.
`order.tsks` | (`character(n.tasks)`) Character vector with `task.ids` in new order.
`pretty.names` | (`logical(1)`) Whether to use the short name of the learner instead of its ID in labels. Defaults to `TRUE`.

### Value

ggplot2 plot object.

### See Also

Other plot: `createSpatialResamplingPlots`, `plotBMRBoxplots`, `plotBMRSummary`, `plotCalibration`, `plotCritDifferences`, `plotLearningCurve`, `plotPartialDependence`, `plotROCCurves`, `plotResiduals`, `plotThreshVsPerf`

Other benchmark: `BenchmarkResult`, `batchmark`, `benchmark`, `convertBMRToRankMatrix`, `friedmanPostHocTestBMR`, `friedmanTestBMR`, `generateCritDifferencesData`, `getBMRAggrPerformances`, `getBMRFeatSelResults`, `getBMRFilteredFeatures`, `getBMRLearnerIds`, `getBMRLearnerShortNames`, `getBMRLearners`, `getBMRMeasureIds`, `getBMRMeasures`, `getBMRModels`, `getBMRPerformances`, `getBMRPredictions`, `getBMRTaskDescs`, `getBMRTaskIds`, `getBMRTuneResults`, `plotBMRBoxplots`, `plotBMRSummary`, `plotCritDifferences`, `reduceBatchmarkResults`

### Examples

    # see benchmark

[Package mlr version 2.15.0 Index]