# NOT RUN {
# Generate dataset
N <- 50 # number of subjects
p <- 3 # number of explanatory variables
mu.star <- rep(0,p) # mean of the explanatory variables
Sigma.star <- diag(rep(1,p)) # covariance
beta.star <- c(1, 1, 0) # coefficients
beta0.star <- 0 # intercept
beta.true = c(beta0.star,beta.star)
X.complete <- matrix(rnorm(N*p), nrow=N)%*%chol(Sigma.star) +
matrix(rep(mu.star,N), nrow=N, byrow = TRUE)
p1 <- 1/(1+exp(-X.complete%*%beta.star-beta0.star))
y <- as.numeric(runif(N)<p1)
# Generate missingness
p.miss <- 0.10
patterns <- runif(N*p)<p.miss #missing completely at random
X.obs <- X.complete
X.obs[patterns] <- NA
# Louis formula to obtain variance of estimates
V_obs = louis_lr_saem(beta.true,mu.star,Sigma.star,y,X.obs)
# }
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