R: Plot calibration data using ggplot2.

plotCalibration {mlr} | R Documentation

## Plot calibration data using ggplot2.

### Description

Plots calibration data from generateCalibrationData.

### Usage

    plotCalibration(
      obj,
      smooth = FALSE,
      reference = TRUE,
      rag = TRUE,
      facet.wrap.nrow = NULL,
      facet.wrap.ncol = NULL
    )

### Arguments

`obj` | (CalibrationData) Result of generateCalibrationData.
`smooth` | (`logical(1)`) Whether to use a loess smoother. Default is `FALSE`.
`reference` | (`logical(1)`) Whether to plot a reference line showing perfect calibration. Default is `TRUE`.
`rag` | (`logical(1)`) Whether to include a rag plot which shows a rug plot on the top which pertains to positive cases and on the bottom which pertains to negative cases. Default is `TRUE`.
`facet.wrap.nrow, facet.wrap.ncol` | (integer) Number of rows and columns for facetting. Default for both is `NULL`. In this case ggplot's `facet_wrap` will choose the layout itself.

### Value

ggplot2 plot object.

### See Also

Other plot: `createSpatialResamplingPlots()`, `plotBMRBoxplots()`, `plotBMRRanksAsBarChart()`, `plotBMRSummary()`, `plotCritDifferences()`, `plotLearningCurve()`, `plotPartialDependence()`, `plotROCCurves()`, `plotResiduals()`, `plotThreshVsPerf()`

Other calibration: `generateCalibrationData()`

### Examples

    ## Not run:
    lrns = list(makeLearner("classif.rpart", predict.type = "prob"),
      makeLearner("classif.nnet", predict.type = "prob"))
    fit = lapply(lrns, train, task = iris.task)
    pred = lapply(fit, predict, task = iris.task)
    names(pred) = c("rpart", "nnet")
    out = generateCalibrationData(pred, groups = 3)
    plotCalibration(out)

    fit = lapply(lrns, train, task = sonar.task)
    pred = lapply(fit, predict, task = sonar.task)
    names(pred) = c("rpart", "lda")
    out = generateCalibrationData(pred)
    plotCalibration(out)

    ## End(Not run)

[Package mlr version 2.17.1 Index]