plotCalibration: Plot calibration data using ggplot2.
In Najah-lshanableh/R-data-mining2: Machine Learning in R

Description Usage Arguments Value See Also Examples

Plots calibration data from generateCalibrationData.

1 2	plotCalibration(obj, smooth = FALSE, reference = TRUE, rag = TRUE, facet.wrap.nrow = NULL, facet.wrap.ncol = NULL)

`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.

ggplot2 plot object.

Other plot: plotBMRBoxplots, plotBMRRanksAsBarChart, plotBMRSummary, plotCritDifferences, plotFilterValuesGGVIS, plotLearningCurveGGVIS, plotLearningCurve, plotPartialDependenceGGVIS, plotPartialDependence, plotROCCurves, plotResiduals, plotThreshVsPerfGGVIS, plotThreshVsPerf

Other calibration: generateCalibrationData

## 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)