auditor (version 0.2.1)

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.

...

Other modelAudit 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)

# }