BinUplift {tools4uplift} | R Documentation |
Univariate optimal partitionning for Uplift Models. The algorithm quantizes a single variable into bins with significantly different observed uplift.
BinUplift(data, treat, outcome, x, n.split = 10, alpha = 0.05, n.min = 30, ylim = NULL, ylab = "Uplift", title = NULL, color = NULL)
data |
a data frame containing the treatment, the outcome and the predictor to quantize. |
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). |
x |
name of the explanatory variable to quantize. |
n.split |
number of splits to test at each node. For continuous explanatory variables only (must be > 0). If n.split = 10, the test will be executed at each decile of the variable. |
alpha |
significance level of the statistical test (must be between 0 and 1). |
n.min |
minimum number of observations per child node. |
ylim |
a range for the y axis. |
ylab |
a title for the y axis. |
title |
an overall title for the plot. |
color |
a color for the plot. If ommitted, the color will be set by default to a custom light blue. |
out.tree |
Descriptive statistics for the different nodes of the tree |
sas.code |
SAS code generated for variable quantization |
Mouloud Belbahri
Belbahri, M., Murua, A., Gandouet, O., and Partovi Nia, V. (2019) Uplift Regression, <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>
BinUpliftEnhanced
library(tools4uplift) data("SimUplift") binX1 <- BinUplift(data = SimUplift, treat = "treat", outcome = "y", x = "X1", alpha = 0.10, n.min = 3, title = "Binning for X1")