DG {AteMeVs}

Generation of artificial data

Description

This function is used to generate an artificial dataset, which contains potential outcomes, treatments, and error-prone confounders.

Usage

DG(X,Z,gamma_X,gamma_Z,Sigma_e,outcome="continuous")

Arguments

X

an n\times p_x matrix of the error-prone confounders

Z

an n\times p_z matrix of the precisely measured confounders

gamma_X

a p_x-dimensional vector of parameters corresponding to the error-prone confounders X

gamma_Z

a p_z-dimensional vector of parameters corresponding to the precisely measured confounders Z

Sigma_e

a p_x \times p_x covariance matrix for the classical measurement error model

outcome

the indicator of the nature of the outcome variable; outcome="continuous" reflects normally distributed outcomes; outcome="binary" gives binary outcomes

Details

This function is used to generate artificial data, including potential outcomes, binary treatments, and error-prone and precisely measured confounders.

Value

data

an n\times (2+p_x+p_z) matrix of the artificial data. The first column is the potential outcome, and the second column is the binary treatment; column 3 to column (p_x+2) records error-prone confounders, and the remaining columns record precisely-measured confounders.

Author(s)

Chen, L.-P. and Yi, G. Y.

References

Yi, G. Y. and Chen, L.-P. (2023). Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders. Statistical Methods in Medical Research, 32, 691-711.

Examples

library(MASS)
n = 800
p_x = 10      # dimension of parameters
p_z = 10
p = p_x + p_z
gamma_X = c(rep(1,2),rep(0,p_x-2))
gamma_Z = c(rep(1,2),rep(0,p_z-2))
gamma = c(gamma_X, gamma_Z)

mu_X = rep(0,p_x)
mu_Z = rep(0,p_z)

Sigma_X = diag(1,p_x,p_x)
Sigma_Z = diag(1,p_z,p_z)
Sigma_e = diag(0.2,p_x)
X = mvrnorm(n, mu_X, Sigma_X, tol = 1e-6, empirical = FALSE, EISPACK = FALSE)
Z = mvrnorm(n, mu_Z, Sigma_Z, tol = 1e-6, empirical = FALSE, EISPACK = FALSE)
data = DG(X,Z,gamma_X,gamma_Z,Sigma_e,outcome="continuous")