auditor (version 0.3.0)

plotREC: Regression Error Characteristic Curves (REC)

Description

Error Characteristic curves are a generalization of ROC curves. On the x axis of the plot there is an error tolerance and on the y axis there is a percentage of observations predicted within the given tolerance.

Usage

plotREC(object, ...)

Arguments

object

An object of class ModelAudit or modelResiduals.

...

Other modelAudit or model Residuals objects to be plotted together.

Value

ggplot object

Details

REC curve estimates the Cumulative Distribution Function (CDF) of the error

Area Over the REC Curve (REC) is a biased estimate of the expected error

References

Bi J., Bennett K.P. (2003). Regression error characteristic curves, in: Twentieth International Conference on Machine Learning (ICML-2003), Washington, DC.

See Also

plot.modelAudit, plotROC, plotRROC

Examples

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

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


# }