FitBNEM: Fit Bivariate Normal Regression Model via Expectation...

In zrmacc/BNEM: Surrogate Outcome Regression Analysis

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

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

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.

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.

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