plot_gps: Plot the propensity score by treatment

In riAFTBART: A Flexible Approach for Causal Inference with Multiple Treatments and Clustered Survival Outcomes

Plot the propensity score by treatment

Description

This function estimates the propensity score for each treatment group and then plot the propensity score by each treatment to check covariate overlap.

Usage

plot_gps(trt, X, cluster.id, method = "Multinomial")

Arguments

trt	A numeric vector representing the treatment groups.
X	A dataframe or matrix, including all the covariates but not treatments, with rows corresponding to observations and columns to variables.
cluster.id	A vector of integers representing the clustering id. The cluster id should be an integer and start from 1.
method	A character indicating how to estimate the propensity score. The default is "Multinomial", which uses multinomial regression to estimate the propensity score.

Value

A plot

Examples

library(riAFTBART)
set.seed(20181223)
n = 5       # number of clusters
k = 50      # cluster size
N = n*k     # total sample size
cluster.id = rep(1:n, each=k)
tau.error = 0.8
b = stats::rnorm(n, 0, tau.error)
alpha = 2
beta1 = 1
beta2 = -1
sig.error = 0.5
censoring.rate = 0.02
x1 = stats::rnorm(N,0.5,1)
x2 = stats::rnorm(N,1.5,0.5)
trt.train = sample(c(1,2,3), N, prob = c(0.4,0.3,0.2), replace = TRUE)
plot_gps(trt = trt.train, X = cbind(x1, x2), cluster.id = cluster.id)

riAFTBART documentation built on May 17, 2022, 1:07 a.m.