CostLines: Plotting Cost Curves based on Cost or Skew
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 lines based on cost or skew.

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CostLines(predictions,classes,uniquec=FALSE, loss2skew=FALSE, hold=FALSE,
          gridOFF=TRUE, legendOFF=FALSE, main, xlab, ylab, namesClassifiers,
           lwd, lty, col, xPosLegend,yPosLegend, cexL)

`predictions`	A list with predicted scores.
`classes`	A list with labels, (only binary classes).
`uniquec`	If TRUE, the same array classes is used for each array in a list predictions.
`loss2skew`	If 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.
`gridOFF`	Disable/enable grid 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.
`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 loss which is produced at a decision threshold t and a cost proportion c is given by formula:

Loss by Cost:

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

The loss which is produced at a decision threshold t and a skew z is given by formula:

Loss by Skew:

z(1-F0(t))+(1-z)F1(t))

Where:

	c:	cost values of x_axis between [0, 1].
	z:	skew values of x_axis between [0, 1].
	t:	threshold.
	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.

No return value.

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, KendallCurves, predictions, RateDrivenCurves, TestOptimal, TP_FP.rates, TrainOptimal

data(predictions)
#Loss by cost
CostLines(list(predictions$A, predictions$B), list(predictions$classes),
			uniquec=TRUE)

#Loss by skew
CostLines(list(predictions$A, predictions$B), list(predictions$classes),
			uniquec=TRUE, loss2skew = TRUE)

#names legend
CostLines(list(predictions$A, predictions$B), list(predictions$classes,
			predictions$classes), loss2skew = TRUE, col=c("blue", "red"), lty=c(1, 2),
            namesClassifiers = c("A","B"))

#LegendOFF
CostLines(list(predictions$A, predictions$B), list(predictions$classes),
			uniquec=TRUE, loss2skew = TRUE, legendOFF=TRUE, lty=5)