tools4uplift (version 1.0.0)

qLHS: Qini-based uplift regression

Description

A Qini-based LHS (Latin Hypercube Sampling) uplift model.

Usage

qLHS(data, treat, outcome, predictors, 
     lhs_points = 50, lhs_range = 1, 
     adjusted = TRUE, rank.precision = 2, equal.intervals = FALSE, 
     nb.group = 10, validation = TRUE, p = 0.3)

Arguments

data

a data frame containing the treatment, the outcome and the predictors.

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).

predictors

a vector of names representing the predictors to consider in the model.

lhs_points

number of LHS points to sample for each regularization constant.

lhs_range

a multiplicative scalar that controls the variance of the LHS search - Default is 1, the LHS procedure samples points uniformly with variance equal to the variance of the maximum likelihood estimator.

adjusted

if TRUE, the adjusted Qini coefficient is used instead of the Qini coefficient.

rank.precision

precision for the ranking quantiles to compute the Qini coefficient. Must be 1 or 2. If 1, the ranking quantiles will be rounded to the first decimal. If 2, to the second decimal.

equal.intervals

flag for using equal intervals (with equal number of observations) or the true ranking quantiles which result in an unequal number of observations in each group to compute the Qini coefficient.

nb.group

the number of groups for computing the Qini coefficient if equal.intervals is TRUE - Default is 10.

validation

if TRUE, the best LHS model is selected based on cross-validation - Default is TRUE.

p

if validation is TRUE, the desired proportion for the validation set. p is a value between 0 and 1 expressed as a decimal, it is set to be proportional to the number of observations per group - Default is 0.3.

Value

the models with LHS coefficients of class InterUplift.

Details

The regularization parameter is chosen based on the interaction uplift model that maximizes the Qini coefficient of the LHS search.

References

Belbahri, M., Murua, A., Gandouet, O., and Partovi Nia, V. (2020) Qini-based Uplift Regression, <https://arxiv.org/pdf/1911.12474.pdf>

Examples

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

upliftLHS <- qLHS(data = SimUplift, treat = "treat", outcome = "y", 
                  predictors = colnames(SimUplift[,3:7]), lhs_points = 5,
                  lhs_range = 1, adjusted = TRUE, equal.intervals = TRUE, 
                  nb.group = 5, validation = FALSE)

# }