import_lift_method: Import lift_method for lift modeling with rpart

In IyarLin/segmenTree: Find sub groups (segments) with heterogenuus treatment effect in Randomised Controlled Trial data.

Description

import_lift_method is a function that imports a list of functions to serve as a user defined method with the rpart function. see example below for more details on it's usage.

Usage

import_lift_method(f_n = NULL)

Arguments

f_n	a function that takes as input the split child node sample size and returns a scalar/vector of the same length. this number is used to weight the competing sub populations when making the split. see example below for a use case. if NULL the sample size is ignored when comparing splits.

Details

the rpart function accepts in the method argument a user defined list. This function imports the list that implements a segment tree. The y variable in the input data.frame must be a 2 column matrix who's first column contains the response variable values (either binary or numeric, categorical isn't supported) and the second column is a binary only treatment indicator.

Value

a list containing eval, split and init functions.

See Also

extract_segments, prune_tree

Examples

## ----generate example file, eval=F, echo=F------------------------------
## knitr::purl("README.Rmd", output = "examples/segmenTree_example.R")




## ---- echo=FALSE--------------------------------------------------------
library(segmenTree)


## ----generate a dataset-------------------------------------------------
set.seed(1) # vary seed, n and effect_size below to get a sense of the model performance sensetivity
effect_size <- 0.25
p_x <- function(Tr, X1, X2, X3){
  lp <- 2*X1 + 0.2*X2 + as.numeric(X3)/6
  effect_size + (effect_size/0.125)*Tr*X1^3 + 
    (1 - 2*effect_size)*exp(lp)/(1+exp(lp))
}

n <- 10000
Tr <- rbinom(n, 1, 0.3)
X1 <- runif(n, -0.5, 0.5)
X2 <- rnorm(n)
X3 <- factor(sample(LETTERS[1:3], size = n, replace = T))
p <- p_x(Tr, X1, X2, X3)
y <- sapply(p, function(x) rbinom(1, 1, x))
y_mat <- cbind(y, Tr)
dat <- data.frame(y = I(y_mat), X1, X2, X3)


## ----fit a segment tree-------------------------------------------------
lift_method <- import_lift_method()
segment_tree <- rpart(y ~ ., data = dat,
                      method = lift_method, 
                      control = rpart.control(cp = 0, minbucket = 1000),
                      x = T)


## ----explore resulting tree---------------------------------------------
segment_tree


## ---- warning=F, message=F----------------------------------------------
segments <- extract_segments(segment_tree, alpha = 0.15)
print(segments)


## ----predict treatment effect and compare with actual treatment effect----
tau <- predict(segment_tree, dat)
p_treat <- p_x(rep(1, n), X1, X2, X3)
p_cont <- p_x(rep(0, n), X1, X2, X3)
cate <- p_treat - p_cont

y_lim <- c(min(tau, cate), max(tau, cate))
plot(c(min(dat$X1), max(dat$X1)), y_lim, type = "n", main = "segmenTree",
     xlab = "X1", ylab = "true (red) vs predicted (black) lift")
points(dat$X1, cate, col = "red")
points(dat$X1, tau)


## ----prune tree using tunecp, warning=FALSE-----------------------------
optimal_cp_cv <- tune_cp(segment_tree)
optimal_cp_cv <- optimal_cp_cv$optimal_cp
pruned_segment_tree <- prune(segment_tree, cp = optimal_cp_cv)


## ----predict treatment effect pruned tree-------------------------------
tau <- predict(pruned_segment_tree, dat)

y_lim <- c(min(tau, cate), max(tau, cate))
plot(c(min(dat$X1), max(dat$X1)), y_lim, type = "n", main = "segmenTree",
     xlab = "X1", ylab = "true (red) vs predicted (black) lift")
points(dat$X1, cate, col = "red")
points(dat$X1, tau)


## ----fit segment tree with n weights------------------------------------
lift_method <- import_lift_method(f_n = function(x) x)

weighted_segment_tree <- rpart(y ~ ., data = dat,
                      method = lift_method, 
                      control = rpart.control(cp = 0, minbucket = 1000),
                      x = T)


## ----predict treatment effect weighted tree-----------------------------
tau <- predict(weighted_segment_tree, dat)

y_lim <- c(min(tau, cate), max(tau, cate))
plot(c(min(dat$X1), max(dat$X1)), y_lim, type = "n", main = "segmenTree",
     xlab = "X1", ylab = "true (red) vs predicted (black) lift")
points(dat$X1, cate, col = "red")
points(dat$X1, tau)


## ----compare segmenTree with 2 other approches--------------------------
par(mfrow = c(1, 3))
# segmenTree pruned model
tau <- predict(pruned_segment_tree, dat)
p_treat <- p_x(rep(1, n), X1, X2, X3)
p_cont <- p_x(rep(0, n), X1, X2, X3)
cate <- p_treat - p_cont

y_lim <- c(min(tau, cate), max(tau, cate))
plot(c(min(dat$X1), max(dat$X1)), y_lim, type = "n", main = "segmenTree",
     xlab = "X1", ylab = "true (red) vs predicted (black) lift")
points(dat$X1, cate, col = "red")
points(dat$X1, tau)

# Model jointly the treatment and covariates
dat2 <- data.frame(y, X1, X2, X3, Tr)
fit2 <- rpart(y ~ ., data = dat2)

dat2_treat <- dat2; dat2_cont <- dat2
dat2_treat$Tr <- 1L; dat2_cont$Tr <- 0L
tau2 <- predict(fit2, dat2_treat) - predict(fit2, dat2_cont)

y_lim <- c(min(tau2, cate), max(tau2, cate))
plot(c(min(dat$X1), max(dat$X1)), y_lim, type = "n", main = "regular model",
     xlab = "X1", ylab = "")
points(dat$X1, cate, col = "red")
points(dat$X1, tau2)

# Train a model on the treatment units and a seperate model on the control units

dat3_treat <- dat2[dat2$Tr == 1, -5]
dat3_cont <- dat2[dat2$Tr == 0, -5]

fit3_treat <- rpart(y ~ ., data = dat3_treat)
fit3_cont <- rpart(y ~ ., data = dat3_cont)
dat2_treat <- dat2; dat2_cont <- dat2
tau3 <- predict(fit3_treat, dat2) - predict(fit3_cont, dat2)

y_lim <- c(min(tau3, cate), max(tau3, cate))
plot(c(min(dat$X1), max(dat$X1)), y_lim, type = "n", main = "Two models",
     xlab = "X1", ylab = "")
points(dat$X1, cate, col = "red")
points(dat$X1, tau3)

IyarLin/segmenTree documentation built on July 24, 2020, 7:35 p.m.