R/generate_dataset.R

In DDL: Doubly Debiased Lasso (DDL)

generate_dataset = function(n=300, p=500, s=5, q=3, sigmaE=2, sigma=2, pert=1){
  #create H and Gamma with N(0,1) values and of appropriate size. H can be tuned with pert
  H = pert*matrix(rnorm(n*q,mean=0,sd=1),n,q,byrow = TRUE)
  Gamma = matrix(rnorm(q*p,mean=0,sd=1),q,p,byrow = TRUE)
  #value of X independent from H
  E = matrix(rnorm(n*p,mean=0,sd=sigmaE),n,p,byrow = TRUE)

  #defined in eq. (2), high-dimensional measured covariates
  X = E + H %*% Gamma

  delta = matrix(rnorm(q*1,mean=0,sd=1),q,1,byrow = TRUE)

  #px1 matrix, creates beta with 1s in the first s entries and the remaining p-s as 0s
  beta = matrix(rep(c(1,0),times = c(s,p-s)),p,1,byrow = TRUE)

  #nx1 matrix with values of mean 0 and SD of sigma, error in Y independent of X
  nu = matrix(rnorm(n*1,mean=0,sd=sigma),n,1,byrow = TRUE)

  #eq. (1), the response of the Structural Equation Model
  Y = X %*% beta + H %*% delta + nu
  return_list = list("X"= X,"Y"= Y)
  return(return_list)
}