RateDrivenCurves: Plotting the lower envelope of Cost Curves using the Rate...
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 Cost Curves using Rate Driven threshold Choice

RateDrivenCurves(predictions, classes, uniquec=FALSE, loss2skew=FALSE, hold=FALSE,
                 plotOFF=FALSE, gridOFF=TRUE, pointsOFF=TRUE, legendOFF=FALSE,
                 main, xlab, ylab, namesClassifiers, col, lwd, lty, 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 view is maintained to plot a new curve above the current curve.
`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 rate-driven threshold choice method is a natural way of choosing the thresholds, especially when we only have a ranking or a poorly calibrated probabilistic caddifier.

The rate driven cost curves by cost is defined as a plot of:

2(c*pi0(1-F0((R^-1(c))))+(1-c)pi1*F1((R^-1(c))))

on the axis y against cost c

The rate driven cost curves by skew is defined as a plot of:

z(1-F0(R^-1(c)))+(1-z)F1(R^-1(c)))

on the axis y against skew z

and

R(t)=pi0*F0(t)+pi1*F1(t), by c

R(t)=(F0(t)+F1(t))/2, by z

Where:

	c:	cost values of x_axis between [0, 1].
	z:	skew values of x_axis between [0, 1].
	t:	threshold t=R^-1(c) or t=R^-1(c) as appropriate, and c=R(t)
	pi0:	negative class proportion (Y==0)/length(Y).
	pi1:	positive class proportion (Y==1)/length(Y).
	F1(t):	false positive rate of specific threshold.
	1-F0(t):	true positive rate of specific threshold.
	R(c):	recall that the predicted positive rate.

An array with AUKC (Area Under Kendall Curve) for each test.

Paulina Morillo: paumoal@inf.upv.es

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

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

#Load data
data(predictions)

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

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