tools4uplift (version 0.1-0)

SquareUplift: Bivariate categorization

Description

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

Usage

SquareUplift(data, var1, var2, treat, outcome, n.split = 10, 
             n.min = 1, categorize = TRUE, nb.group = 3, 
             plotit = TRUE, 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).

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 square. Must be an integer > 0.

categorize

if TRUE, the algorithm will augment the data with the categorical variable Cat_var1_var2 with nb.group categories sorted from the highest to the lowest predicted uplift.

nb.group

number of categories of equal observations of the variable Cat_var1_var2. Must be an integer > 1.

plotit

if TRUE, a heatmap of observed uplift per square is plotted.

nb.col

number of colors for the heatmap. From royalblue to red. 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 square 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.

Examples

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

square <- SquareUplift(SimUplift, "X1", "X2", "treat", "y")

# }