SurrogateRegression (version 0.5.0)

Fit.BNEM: Fit Bivariate Normal Regression Model via Expectation Maximization.

Description

Estimation procedure for bivariate normal regression models in which the target and surrogate outcomes are both subject to missingness.

Usage

Fit.BNEM(
  t,
  s,
  X,
  Z,
  sig = 0.05,
  b0 = NULL,
  a0 = NULL,
  sigma0 = NULL,
  maxit = 100,
  eps = 1e-06,
  report = TRUE
)

Value

An object of class 'bnr' with slots containing the estimated regression coefficients, the target-surrogate covariance matrix, the information matrices for the regression and covariance parameters, and the residuals.

Arguments

t

Target outcome vector.

s

Surrogate outcome vector.

X

Target model matrix.

Z

Surrogate model matrix.

sig

Type I error level.

b0

Initial target regression coefficient.

a0

Initial surrogate regression coefficient.

sigma0

Initial covariance matrix.

maxit

Maximum number of parameter updates.

eps

Minimum acceptable improvement in log likelihood.

report

Report fitting progress?

Details

The target and surrogate model matrices are expected in numeric format. Include an intercept if required. Expand factors and interactions in advance. Initial values may be specified for any of the target coefficient b0, the surrogate coefficient a0, or the target-surrogate covariance matrix sigma0.

Examples

# \donttest{
set.seed(100)
n <- 1e3
X <- rnorm(n)
Z <- rnorm(n)
data <- rBNR(X = X, Z = Z, b = 1, a = -1, t_miss = 0.1, s_miss = 0.1)

# Log likelihood.
fit_bnem <- Fit.BNEM(
  t = data[, 1],
  s = data[, 2],
  X = X,
  Z = Z
)
# }