pqr

An R package to construct confidence intervals of coefficients via regularized projection score estimation of treatment effects in high-dimensional quantile regression.

A regularized projection score method is proposed for estimating treatment effects in quantile regression in the presence of high-dimensional confounding covariates. This method is based on an estimated projection score function of the low-dimensional treatment parameters in the presence of high-dimensional confounding covariates. We propose one-step algorithm and a reffitted wild bootstrapping approach for variance estimation. This enables us to construct confidence intervals for the treatment effects in the high-dimensional circumstances.

Installation

#install Rtools 3.5 (http://cran.r-project.org/bin/windows/Rtools)
#install.packages("devtools")
library(devtools)
install_github("xliusufe/pqr")

Usage

• pqr-manual ------------ Details of the usage of the package.

Example

library(pqr)

# Example 1: the usage of "inferen()"

n <- 50
d <- 3
s <- 3
p <- 20
alpha <- 0.95
beta <- rep(3,d)
eta <- c(rep(3,s),numeric(p-s))
x <- matrix(rnorm(n*d),n,d)
z <- matrix(rnorm(n*(p-1)),n,p-1)
y <- x%*%beta + cbind(1,z)%*%eta + rnorm(n)
fit <- inferen(y,x,z,tau=0.5)
ests <- fit$ests
est.coef <- ests$coef
boot.var <- diag(fit$cov)
lbounds <- ests$coef - qnorm((1+alpha)/2)*sqrt(boot.var)
ubounds <- ests$coef + qnorm((1+alpha)/2)*sqrt(boot.var)
counts <- ifelse(lbounds<beta&beta<ubounds,1,0)

#Example 2: the usage of function "mvr()"

n <- 100
q <- 5
s <- 3
p <- 100
B <- matrix(runif(q*s, 2,3), s)
x <- matrix(rnorm(n*p),n,p)
y <- x[,1:s]%*%B + matrix(rnorm(n*q),n)
fit <- mvr(y,x)
fit$activeX
fit$Bhat
which(rowSums(fit$Bhat^2)>0)
fit$muhat 

#example 3: the usage of function "mvr()"

n <- 200
q <- 5
s <- 3
d <- 3
p <- 100
B <- matrix(runif(q*s, 2,3), s)
C <- matrix(runif(q*d, 1,2), d)
x <- matrix(rnorm(n*p),n,p)
z <- matrix(rnorm(n*d),n)
y <- x[,1:s]%*%B + z%*%C + matrix(rnorm(n*q),n)
fit <- mvr(y,x,z)
fit$activeX
fit$Bhat
which(rowSums(fit$Bhat^2)>0)
fit$Chat
fit$muhat	

References

Cheng, C., Feng, X., Huang, J. and Liu, X. (2020). Regularized projection score estimation of treatment effects in high-dimensional quantile regression. Manuscript.

Development

The R-package is developed by Xu Liu (liu.xu@sufe.edu.cn) and Chao Cheng.