MRME is a Mendelian randomization (MR) method to estimate the causal effect by considering missing values in exposure variable.
You can install the development version of MRME from GitHub with:
# install.packages("devtools")
devtools::install_github("AnqiWang2021/MRME")This is a basic example which shows you how to solve a common problem:
library(MRME)
library(MASS)
N = 10000 #the sample size
K = 1 #the number of instrumental variables
n = 2
p = 0.3
#Sigma matrix
Sigma = matrix(c(0.58,0.35,0.35,0.58),2,2)
######parameter set
beta0 = 0.8
beta1 = 0.2
alpha0 = 0.13
alphav = 0.2
epsilon = mvrnorm(N,rep(0,2),Sigma)
#########genetic matrix
G<-rbinom(N*K,n,p)
G = as.matrix(G)
######## generate X and Y
X = alpha0 + as.matrix(G)%*%alphav + epsilon[,1]
Y = beta0 + beta1 *X +epsilon[,2]
########generate missing X, missing rate 30%
p_miss = exp(0.25 + 0.6*Y + G%*%rep(0.05,1))/(1 + exp(0.25 +0.6*Y + G%*%rep(0.05,1)))
maxit = 1000
tol = 1e-8
Sigma_start= matrix(c(0.4,0.2,0.2,0.4),2,2)
para_start = c(0.6,0.1,rep(0.05,2))
result = EM_miss(para_start,Sigma_start,G,Y,X,maxit,tol)
Sigma_est = result$Sigma
par_est = result$para
result_var = EM_miss_var(Sigma_est,par_est,G,Y,X)