rBNR: Simulate Bivariate Normal Data with Missingness

In zrmacc/BNEM: Surrogate Outcome Regression Analysis

Simulate Bivariate Normal Data with Missingness

Description

Function to simulate from a bivariate normal regression model with outcomes missing completely at random.

Usage

rBNR(
  X,
  Z,
  b,
  a,
  t_miss = 0,
  s_miss = 0,
  sigma = NULL,
  include_residuals = TRUE
)

Arguments

X	Target design matrix.
Z	Surrogate design matrix.
b	Target regression coefficient.
a	Surrogate regression coefficient.
t_miss	Target missingness in [0,1].
s_miss	Surrogate missingness in [0,1].
sigma	2x2 target-surrogate covariance matrix.
include_residuals	Include the residual? Default: TRUE.

Value

Numeric Nx2 matrix. The first column contains the target outcome, the second contains the surrogate outcome.

Examples

set.seed(100)
# Observations.
n <- 1e3
# Target design.
X <- cbind(1, matrix(rnorm(3 * n), nrow = n))
# Surrogate design.
Z <- cbind(1, matrix(rnorm(3 * n), nrow = n))
# Target coefficient.
b <- c(-1, 0.1, -0.1, 0.1)
# Surrogate coefficient.
a <- c(1, -0.1, 0.1, -0.1)
# Covariance structure.
sigma <- matrix(c(1, 0.5, 0.5, 1), nrow = 2)
# Data generation, target and surrogate subject to 10% missingness.
y <- rBNR(X, Z, b, a, t_miss = 0.1, s_miss = 0.1, sigma = sigma)

zrmacc/BNEM documentation built on March 31, 2024, 12:20 a.m.