SquareUplift {tools4uplift} | R Documentation |
A non-parametric heat map representing the observed uplift in rectangles that explore a bivariate dimension space. The function also predicts the individual uplift based on the heatmap.
SquareUplift(data, var1, var2, treat, outcome, n.split = 10, n.min = 1, categorize = TRUE, nb.group = 3, plotit = TRUE, nb.col = 20)
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). |
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. |
categorize |
if TRUE, the algorithm will augment the data with the categorical variable |
nb.group |
number of categories of equal observations of the variable |
plotit |
if TRUE, a heatmap of observed uplift per rectangle is plotted. |
nb.col |
number of colors for the heatmap. From |
returns an augmented dataset with Uplift_var1_var2
variable representing a predicted uplift for each observation based on the rectangle it belongs to. By default, the function creates also a categorical variable Cat_var1_var2
based on the predicted uplift and plots a heat map of observed uplift.
Mouloud Belbahri
Belbahri, M., Murua, A., Gandouet, O., and Partovi Nia, V. (2019) Uplift Regression, <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>
library(tools4uplift) data("SimUplift") square <- SquareUplift(SimUplift, "X1", "X2", "treat", "y")