TestBNR: Test Bivariate Normal Regression Model.

In zrmacc/BNEM: Surrogate Outcome Regression Analysis

Test Bivariate Normal Regression Model.

Description

Performs a test of the null hypothesis that a subset of the regression parameters for the target outcome are zero in the bivariate normal regression model.

Usage

TestBNR(t, s, X, Z = NULL, is_zero, test = "Wald", ...)

Arguments

t	Target outcome vector.
s	Surrogate outcome vector.
X	Target model matrix.
Z	Surrogate model matrix.
is_zero	Logical vector, with as many entires as columns in the target model matrix, indicating which columns have coefficient zero under the null.
test	Either Score or Wald. Only Wald is available for LS.
...	Additional arguments accepted if fitting via EM. See FitBNEM.

Value

A numeric vector containing the test statistic, the degrees of freedom, and a p-value.

Examples

# Generate data.
set.seed(100)
n <- 1e3
X <- cbind(1, rnorm(n))
Z <- cbind(1, rnorm(n))
data <- rBNR(X = X, Z = Z, b = c(1, 0), a = c(-1, 0), t_miss = 0.1, s_miss = 0.1)

# Test 1st coefficient.
wald_test1 <- TestBNR(
  t = data[, 1], 
  s = data[, 2], 
  X = X, 
  Z = Z,
  is_zero = c(TRUE, FALSE),
  test = "Wald"
)

score_test1 <- TestBNR(
  t = data[, 1], 
  s = data[, 2], 
  X = X, 
  Z = Z,
  is_zero = c(TRUE, FALSE),
  test = "Score"
)

# Test 2nd coefficient.
wald_test2 <- TestBNR(
  t = data[, 1], 
  s = data[, 2], 
  X = X, 
  Z = Z,
  is_zero = c(FALSE, TRUE),
  test = "Wald"
)

score_test2 <- TestBNR(
  t = data[, 1], 
  s = data[, 2], 
  X = X, 
  Z = Z,
  is_zero = c(FALSE, TRUE),
  test = "Score"
)

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