README.md
In yuyy-shandong/NCOR:

NCETS

NCOR(Negative Control Outcome Regression),is an R package for causal effect estimation of environmental exposure on health outcome. Just taking advantage of a pre-exposure outcome as an auxiliary variable, the NCOR can obtain an unbiased and robust causal effect estimation of exposure on outcome.

Installation

It is easy to install the development version of NCOR package using the 'devtools' package. The typical install time on a "normal" desktop computer is less than one minute.

# install.packages("devtools")
library(devtools)
install_github("yuyy-shandong/NCOR")

Usage

There are two main functions in NCOR package. One is ncor_ind which could eliminate the unmeasured confounders and estimate causal effect for individual data. And the other one is ncor_summary which eliminate the unmeasured confounders and estimate causal effect for summary data. You can find the instructions by '?ncor_ind' and '?ncor_summary'.

library(NCOR)

?ncor_ind

?ncor_summary

Example

 NN <- 10
 data_total <- NULL
 for(i in 1:NN){
 c0 <- 0.5
 dia <- 0.5
 diff_c1 <- 0
 r <- 0.5
 c1 <- rnorm(1,0.5,1)
 c2 <- rnorm(1,0.5,1)
 N <- 100
 Sigma <- matrix(c(1,r,r^2,r,1,r,r^2,r,1),3,3)
 u <- MASS::mvrnorm(n=N, mu=rep(0,3), Sigma=Sigma)
 colnames(u) <- c('u1','u2','u3')
 u <- data.frame(u)
 u1 <- u$u1
 u2 <- u$u2
 u3 <- u$u3

 eps_y1 <- rnorm(N,0,0.1)
 eps_y3 <- rnorm(N,0,0.1)
 eps_x2 <-  rnorm(N,0,0.1)
 y1 <- c1*u1 + eps_y1
 x2 <- c2*u2 + eps_x2
 y3 <- c0*x2 + c1*u3+diff_c1*u3 + dia*y1 + eps_y3
 data_cenre <- data.frame(x2,u1,u2,u3,y1,y3,i)
 data_total <- rbind(data_total,data_cenre)

 }

 model <- ncor_ind (data=data_total, pre_outc_name='y1',expo_namem = 'x2',
                   post_outc_name='y3',centre='i',method='IVW',boot_no=1000)

 model