tools4uplift (version 0.1-0)

BinUplift: Univariate categorization

Description

Univariate optimal partitionning for Uplift Models. The algorithm categorizes a single variable into bins with significantly different observed uplift.

Usage

BinUplift(data, treat, outcome, x, n.split = 10, alpha = 0.05, 
          n.min = 30, ylim = NULL, ylab = "Uplift",
          title = "Binning Results", color = NULL)

Arguments

data

a data frame containing the treatment, the outcome and the predictor to categorize.

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 categorize.

n.split

number of splits to test at each node. For continuous explanatory variables only (must be > 0).

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.

Value

out.tree

Descriptive statistics for the different nodes of the tree

sas.code

SAS code generated for variable categorization

See Also

BinUpliftEnhanced

Examples

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

binX1 <- BinUplift(data = SimUplift, treat = "treat", outcome = "y", x = "X1", 
                  alpha = 0.10, n.min = 3, title = "Binning for X1")

# }