lines.PerformanceUplift {tools4uplift} | R Documentation |
Curve of the function Qini, the incremental observed uplift with respect to predicted uplift sorted from the highest to the lowest.
## S3 method for class 'PerformanceUplift' lines(x, ...)
x |
a table that must be the output of |
... |
additional plot arguments. |
a Qini curve and the associated Qini coefficient
Mouloud Belbahri
Radcliffe, N. (2007). Using control groups to target on predicted lift: Building and assessing uplift models. Direct Marketing Analytics Journal, An Annual Publication from the Direct Marketing Association Analytics Council, pages 14-21.
Belbahri, M., Murua, A., Gandouet, O., and Partovi Nia, V. (2019) Uplift Regression, <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>
PerformanceUplift
library(tools4uplift) data("SimUplift") model1 <- BinUplift2d(SimUplift, "X1", "X2", "treat", "y") perf1 <- PerformanceUplift(data = model1, treat = "treat", outcome = "y", prediction = "Uplift_X1_X2", equal.intervals = TRUE, nb.group = 3) model2 <- BinUplift2d(SimUplift, "X3", "X4", "treat", "y") perf2 <- PerformanceUplift(data = model2, treat = "treat", outcome = "y", prediction = "Uplift_X3_X4", equal.intervals = TRUE, nb.group = 3) plot(perf1, type='b') lines(perf2, type='b', col='red')