TestBNR function

Test Bivariate Normal Regression Model.

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.

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.

Returns

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"
)