mlr (version 2.10)

plotCalibration: 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: plotBMRBoxplots, plotBMRRanksAsBarChart, plotBMRSummary, plotCritDifferences, plotFilterValuesGGVIS, plotLearningCurveGGVIS, plotLearningCurve, plotPartialDependenceGGVIS, plotPartialDependence, plotROCCurves, plotResiduals, plotThreshVsPerfGGVIS, 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)
## ---------------------------------------------