Package index • COMMA

Classification Probabilities

true_classification_prob(): Compute Probability of Each True Mediator, for Every Subject

misclassification_prob(): Compute Conditional Probability of Observed Mediator Given True Mediator, for Every Subject

Estimation

Functions to estimate regression parameters and standard errors

COMMA_EM(): EM Algorithm Estimation of the Binary Mediator Misclassification Model

COMMA_EM_bootstrap_SE(): Estimate Bootstrap Standard Errors using EM

COMMA_PVW(): Predictive Value Weighting Estimation of the Binary Mediator Misclassification Model

COMMA_PVW_bootstrap_SE(): Estimate Bootstrap Standard Errors using PVW

COMMA_OLS(): Ordinary Least Squares Estimation of the Binary Mediator Misclassification Model

COMMA_OLS_bootstrap_SE(): Estimate Bootstrap Standard Errors using OLS

Data generation for examples and sample datasets

Function to generate data with misclassified binary outcomes

COMMA_data(): Generate Data to use in COMMA Functions

NCHS2022_sample: Example data from the National Vital Statistics System of the National Center for Health Statistics (NCHS), 2022

Internals

Internal functions and helpers

COMBO_EM_algorithm(): EM-Algorithm Estimation of the Binary Outcome Misclassification Model

COMBO_EM_function(): EM-Algorithm Function for Estimation of the Misclassification Model

COMBO_weight(): Compute E-step for Binary Outcome Misclassification Model Estimated With the EM-Algorithm

COMMA_boot_sample(): Generate Bootstrap Samples for Estimating Standard Errors

EM_function_bernoulliY(): EM Algorithm Function for Estimation of the Misclassification Model

EM_function_bernoulliY_XM(): EM Algorithm Function for Estimation of the Misclassification Model

EM_function_normalY(): EM Algorithm Function for Estimation of the Misclassification Model

EM_function_normalY_XM(): EM Algorithm Function for Estimation of the Misclassification Model

EM_function_poissonY(): EM Algorithm Function for Estimation of the Misclassification Model

EM_function_poissonY_XM(): EM Algorithm Function for Estimation of the Misclassification Model

pi_compute(): Compute Probability of Each True Outcome, for Every Subject

pistar_compute(): Compute Conditional Probability of Each Observed Outcome Given Each True Outcome, for Every Subject

sum_every_n(): Sum Every "n"th Element

sum_every_n1(): Sum Every "n"th Element, then add 1

theta_optim(): Likelihood Function for Normal Outcome Mechanism with a Binary Mediator

theta_optim_XM(): Likelihood Function for Normal Outcome Mechanism with a Binary Mediator and an Interaction Term

w_m_binaryY(): Compute E-step for Binary Mediator Misclassification Model Estimated With the EM Algorithm

w_m_normalY(): Compute E-step for Binary Mediator Misclassification Model Estimated With the EM Algorithm

w_m_poissonY(): Compute E-step for Binary Mediator Misclassification Model Estimated With the EM Algorithm