observed_data_loglik: Observed-data log-likelihood

In sarahlotspeich/logreg2ph_R_only: Implements Sieve Maximum Likelihood Estimation (SMLE) Approach for Two-Phase Logistic Regression

Observed-data log-likelihood

Description

This function returns the value of the observed-data log-likelihood (equation (2) in Lotspeich et al. (2021)) for a given dataset and parameter values theta, gamma, and p.

Usage

observed_data_loglik(
  N,
  n,
  Y_unval = NULL,
  Y_val = NULL,
  X_unval = NULL,
  X_val = NULL,
  C = NULL,
  Bspline = NULL,
  comp_dat_all,
  theta_pred,
  gamma_pred,
  theta,
  gamma,
  p
)

Arguments

N	Phase I sample size
n	Phase II sample size
Y_unval	Column names with the unvalidated outcome. If Y_unval is null, the outcome is assumed to be error-free.
Y_val	Column names with the validated outcome.
X_unval	Column name(s) with the unvalidated predictors. If X_unval and X_val are null, all precictors are assumed to be error-free.
X_val	Column name(s) with the validated predictors. If X_unval and X_val are null, all precictors are assumed to be error-free.
C	(Optional) Column name(s) with additional error-free covariates.
Bspline	Vector of column names containing the B-spline basis functions.
comp_dat_all	Augmented dataset containing rows for each combination of unvalidated subjects' data with values from Phase II (a matrix)
theta_pred	Vector of columns in comp_dat_all that pertain to the predictors in the analysis model.
gamma_pred	Vector of columns in comp_dat_all that pertain to the predictors in the outcome error model.
theta	Parameters for the analysis model (a column vector)
gamma	Parameters for the outcome error model (a column vector)
p	B-spline coefficients for the approximated covariate error model (a matrix)
Validated	Column name with the validation indicator. The validation indicator can be defined as Validated = 1 or TRUE if the subject was validated and Validated = 0 or FALSE otherwise.

Value

Scalar value of the function

sarahlotspeich/logreg2ph_R_only documentation built on Jan. 20, 2025, 6:20 p.m.