TestOptimal: Plotting lower envelope of Cost Curves
In paumoal/costcurvespkg: Display and analyze Cost Curves and treshold choice

Description Usage Arguments Details Value Author(s) References See Also Examples

Function to plot of lower envelope of Cost Curves

TestOptimal(predictions,classes,uniquec=FALSE, loss2skew=FALSE, hold=FALSE,
            plotOFF=FALSE, gridOFF=TRUE, pointsOFF=TRUE, legendOFF=FALSE,
            main, xlab, ylab, namesClassifiers, lwd, lty, col, pch, cex,
            xPosLegend,yPosLegend, cexL)

`predictions`	A list with predicted scores.
`classes`	A list with labels, (only binary classes).
`uniquec`	If it is TRUE, the same array classes is used for each array in a list predictions.
`loss2skew`	If it is TRUE, loss by Skew is plotted otherwise loss by cost is plotted.
`hold`	If it is TRUE, the plot is maintained open allowing to insert new curves on the same plot.
`plotOFF`	Disable/enable plot visualization, only return AUC values.
`gridOFF`	Disable/enable grid visualization.
`pointsOFF`	Disable/enable point marks visualization.
`legendOFF`	Disable/enable legend visualization.
`main`	title.
`xlab`	x label.
`ylab`	y label.
`namesClassifiers`	An array with names of each classifier.
`lwd`	Line width.
`lty`	Line type.
`col`	Line color.
`pch`	Point type.
`cex`	Size point.
`xPosLegend`	x coordinate to be used to position the legend.
`yPosLegend`	y coordinate to be used to position the legend.
`cexL`	size of box legend.

The lower envelope at any given c (cost) or z (skew) value is defined as the lowest loss value on any of the given cost curves at that c or z. Each segment of lower envelope or test optimal correspond to points on a ROC covex hull.

A list of lists, for each classifier is returned: extreme points of each segment of lower envelope, thresholds and AUCC (area under curve cost).

Paulina Morillo: paumoal@inf.upv.es

Drummond, C., & Holte, R. C. (2006). Cost curves: An improved method for visualizing classifier performance.

Hernandez-Orallo, J., Flach, P., & Ferri, C. (2013). ROC curves in cost space. Machine learning, 93(1), 71-91.

BrierCurves, CostCurves, CostLines, KendallCurves, predictions, RateDrivenCurves, TP_FP.rates, TrainOptimal

#Load data
data(predictions)

#Loss by cost
R<-TestOptimal(list(predictions$A, predictions$B), list(predictions$classes), uniquec=TRUE)

#Loss by skew
R<-TestOptimal(list(predictions$A, predictions$B), list(predictions$classes), uniquec=TRUE,
	loss2skew = TRUE)

#names legend
R<-TestOptimal(list(predictions$A, predictions$B), list(predictions$classes, predictions$classes),
	plotOFF=TRUE)

#LegendOFF
R<-TestOptimal(list(predictions$A, predictions$B), list(predictions$classes), uniquec=TRUE,
	loss2skew = TRUE, pointsOFF=FALSE, legendOFF=TRUE, gridOFF=FALSE)