complete_data: Create complete dataset This function returns the complete...

In sarahlotspeich/logreg2ph: Implements Sieve Maximum Likelihood Estimation (SMLE) Approach for Two-Phase Logistic Regression

Create complete dataset This function returns the complete dataset trying all validated values for each unvalidated subject

Description

Create complete dataset This function returns the complete dataset trying all validated values for each unvalidated subject

Usage

complete_data(
  Y_unval = NULL,
  Y_val = NULL,
  X_unval = NULL,
  X_val = NULL,
  C = NULL,
  Validated = NULL,
  Bspline = NULL,
  data,
  theta_pred = NULL,
  gamma_pred = NULL
)

Arguments

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.
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.
Bspline	Vector of column names containing the B-spline basis functions.
data	A dataframe with one row per subject containing columns: Y_unval, Y_val, X_unval, X_val, C, Validated, and Bspline.
theta_pred	Vector of columns in data that pertain to the predictors in the analysis model.
gamma_pred	Vector of columns in data that pertain to the predictors in the outcome error model.

Value

dataframe

sarahlotspeich/logreg2ph documentation built on July 24, 2022, 6:34 p.m.