Nothing
UpliftPerCat <- function(data, treat, outcome, x, ...){
# Compute uplift per category and plot it.
#
# Args:
# data: a data frame containing the treatment, the outcome and the variable
# 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).
# x: name of the explanatory variable to study.
# ... extra parameters for barplot.
#
# Returns:
# A barplot representing the uplift per category.
# Create descriptive statistics associated with the binning
x.cat.stats <- matrix(data = NA, nrow = 6, ncol = length(levels(data[[x]])))
rownames(x.cat.stats) <- c("n", "n.t", "n.c", "p1.t", "p1.c", "uplift")
colnames(x.cat.stats) <- levels(data[[x]])
for(i in 1:ncol(x.cat.stats)){
x.cat.stats[1,] <- as.integer(table(data[[x]]))
x.cat.stats[2, i] <- as.integer(sum(data[[treat]]==1 & data[[x]] == levels(data[[x]])[i] )) # n.t
x.cat.stats[3, i] <- as.integer(sum(data[[treat]]==0 & data[[x]] == levels(data[[x]])[i] )) # n.c
x.cat.stats[4, i] <- sum(data[[outcome]]==1 & data[[treat]]==1 & data[[x]] == levels(data[[x]])[i] )/ x.cat.stats[2, i] # p1.t
x.cat.stats[5, i] <- sum(data[[outcome]]==1 & data[[treat]]==0 & data[[x]] == levels(data[[x]])[i] )/ x.cat.stats[3, i] # p1.c
x.cat.stats[6, i] <- x.cat.stats[4, i] - x.cat.stats[5, i] # uplift
}
bar.plot <- barplot(x.cat.stats[6,], ...)
abline(h = 0)
}
# END FUN
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