auditor (version 0.3.0)

plotRROC: Regression Receiver Operating Characteristic (RROC)

Description

The basic idea of the ROC curves for regression is to show model asymmetry. The RROC is a plot where on the x-axis we depict total over-estimation and on the y-axis total under-estimation.

Usage

plotRROC(object, ...)

Arguments

object

An object of class modelAudit or modelResiduals.

...

Other modelAudit or model Residuals objects to be plotted together.

Value

ggplot object

Details

For RROC curves we use a shift, which is an equivalent to the threshold for ROC curves. For each observation we calculate new prediction: \(\hat{y}'=\hat{y}+s\) where s is the shift. Therefore, there are different error values for each shift: \(e_i = \hat{y_i}' - y_i\)

Over-estimation is calculated as: \(OVER= \sum(e_i|e_i>0)\).

Under-estimation is calculated as: \(UNDER = \sum(e_i|e_i<0)\).

The shift equals 0 is represented by a dot.

The Area Over the RROC Curve (AOC) equals to the variance of the errors multiplied by \(frac{n^2}{2}\).

References

Hern<U+00E1>ndez-Orallo, Jos<U+00E9>. 2013. <U+2018>ROC Curves for Regression<U+2019>. Pattern Recognition 46 (12): 3395<U+2013>3411.

See Also

plot.modelAudit, plotROC, plotREC

Examples

# NOT RUN {
library(car)
lm_model <- lm(prestige~education + women + income, data = Prestige)
lm_au <- audit(lm_model, data = Prestige, y = Prestige$prestige)
plotRROC(lm_au)

library(randomForest)
rf_model <- randomForest(prestige~education + women + income, data = Prestige)
rf_au <- audit(rf_model, data = Prestige, y = Prestige$prestige)
plotRROC(lm_au, rf_au)

# }