BinUplift2d: Bivariate quantization

In tools4uplift: Tools for Uplift Modeling

Description

A non-parametric heat map representing the observed uplift in rectangles that explore a bivariate dimension space. The function also returns the individual uplift based on the heatmap.

Usage

BinUplift2d(data, var1, var2, treat, outcome, valid = NULL, 
            n.split = 3, n.min = 30, plotit = FALSE, nb.col = 20)

Arguments

data	a data frame containing uplift models variables.
var1	x-axis variable name. Represents the first dimension of interest.
var2	y-axis variable name. Represents the second dimension of interest.
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).
valid	a validation data frame containing uplift models variables.
n.split	the number of intervals to consider per explanatory variable. Must be an integer > 1.
n.min	minimum number of observations per group (treatment and control) within each rectangle. Must be an integer > 0.
plotit	if TRUE, a heatmap of observed uplift per rectangle is plotted.
nb.col	number of colors for the heatmap. Default is 20. Must be an integer and should greater than n.split for better visualization.

Value

returns an augmented dataset with Uplift_var1_var2 variable representing a predicted uplift for each observation based on the rectangle it belongs to. The function also plots a heat map of observed uplifts.

Author(s)

Mouloud Belbahri

References

Belbahri, M., Murua, A., Gandouet, O., and Partovi Nia, V. (2021) Uplift Regression : The R Package tools4uplift, <https://arxiv.org/pdf/1901.10867.pdf>

Examples

library(tools4uplift)
data("SimUplift")

heatmap <- BinUplift2d(SimUplift, "X1", "X2", "treat", "y")

tools4uplift documentation built on Jan. 11, 2022, 3 a.m.