DualPredict: Predictions from a two-model estimator

Description

Predictions from the two-model uplift model estimator with associated model performance.

Usage

DualPredict(data, treat, outcome, model, nb.group = 10, plotit = FALSE)

Arguments

data

a data frame containing the treatment, the outcome and the predictors.

treat

name of a binary (numeric) vector representing the treatment assignment (coded as 0/1).

outcome

name of a binary response (numeric) vector (coded as 0/1).

model

a model that must be the output of DualUplift function.

nb.group

number of groups of equal observations in which to partition the data in order to compute model performance.

plotit

if TRUE, a QiniCurve and QiniBarPlot are returned.

Value

data

a data frame augmented with the predicted uplift

qini

a Qini Coefficient

References

Hansotia, B., J., and Rukstales B. (2001) Direct marketing for multichannel retailers: Issues, challenges and solutions. Journal of Database Marketing and Customer Strategy Management, Vol. 9(3), 259-266.

Belbahri, M., Murua, A., Gandouet, O., and Partovi Nia, V. (2019) Uplift Regression, <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>

Examples

Run this code

# NOT RUN {
library(tools4uplift)
data("SimUplift")

fit <- DualUplift(SimUplift, "treat", "y", predictors = colnames(SimUplift[, 3:12]))

pred <- DualPredict(SimUplift, "treat", "y", model = fit, nb.group = 5)[[1]]

# }