Package index • COMBO

Classification Probabilities

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

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

misclassification_prob2(): Compute Conditional Probability of Each Second-Stage Observed Outcome Given Each True Outcome and First-Stage Observed Outcome, for Every Subject

Estimation

Functions to estimate regression parameters

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

COMBO_MCMC(): MCMC Estimation of the Binary Outcome Misclassification Model

COMBO_EM_2stage(): EM-Algorithm Estimation of the Two-Stage Binary Outcome Misclassification Model

COMBO_MCMC_2stage(): MCMC Estimation of the Two-Stage Binary Outcome Misclassification Model

Data for examples

Data for examples and functions to generate data with misclassified binary outcomes

COMBO_data(): Generate Data to use in COMBO Functions

COMBO_data_2stage(): Generate data to use in two-stage COMBO Functions

COMBO_EM_data: Test data for the COMBO_EM function

LSAC_data: Example data from The Law School Admissions Council's (LSAC) National Bar Passage Study (Linda Wightman, 1998)

VPRAI_synthetic_data: Synthetic example data of pretrial failure risk factors and outcomes, VPRAI recommendations, and judge decisions

Internals

Internal functions and helpers

check_and_fix_chains(): Check Assumption and Fix Label Switching if Assumption is Broken for a List of MCMC Samples

check_and_fix_chains_2stage(): Check Assumption and Fix Label Switching if Assumption is Broken for a List of MCMC Samples

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

em_function_2stage(): EM-Algorithm Function for Estimation of the Two-Stage Misclassification Model

expit(): Expit function

jags_picker(): Set up a Binary Outcome Misclassification jags.model Object for a Given Prior

jags_picker_2stage(): Set up a Two-Stage Binary Outcome Misclassification jags.model Object for a Given Prior

label_switch(): Fix Label Switching in MCMC Results from a Binary Outcome Misclassification Model

label_switch_2stage(): Fix Label Switching in MCMC Results from a Binary Outcome Misclassification Model

loglik(): Expected Complete Data Log-Likelihood Function for Estimation of the Misclassification Model

loglik_2stage(): Expected Complete Data Log-Likelihood Function for Estimation of the Two-Stage Misclassification Model

mean_pistarjj_compute(): Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjects

model_picker(): Select a Binary Outcome Misclassification Model for a Given Prior

model_picker_2stage(): Select a Two-Stage Binary Outcome Misclassification Model for a Given Prior

naive_jags_picker(): Set up a Naive Logistic Regression jags.model Object for a Given Prior

naive_jags_picker_2stage(): Set up a Naive Two-Stage Regression jags.model Object for a Given Prior

naive_loglik_2stage(): Observed Data Log-Likelihood Function for Estimation of the Naive Two-Stage Misclassification Model

naive_model_picker(): Select a Logisitic Regression Model for a Given Prior

naive_model_picker_2stage(): Select a Naive Two-Stage Regression Model for a Given Prior

perfect_sensitivity_EM(): EM-Algorithm Estimation of the Binary Outcome Misclassification Model while Assuming Perfect Sensitivity

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

pistar_by_chain(): Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjects for each MCMC Chain

pistar_by_chain_2stage(): Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjects for each MCMC Chain for a 2-stage model

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

pistar_compute_for_chains(): Compute Conditional Probability of Each Observed Outcome Given Each True Outcome for a given MCMC Chain, for Every Subject

pistar_compute_for_chains_2stage(): Compute Conditional Probability of Each Observed Outcome Given Each True Outcome for a given MCMC Chain, for Every Subject for 2-stage models

pitilde_by_chain(): Compute the Mean Conditional Probability of Second-Stage Correct Classification, by First-Stage and True Outcome Across all Subjects for each MCMC Chain

pitilde_compute(): Compute Conditional Probability of Each Second-Stage Observed Outcome Given Each True Outcome and First-Stage Observed Outcome, for Every Subject

pitilde_compute_for_chains(): Compute Conditional Probability of Each Observed Outcome Given Each True Outcome for a given MCMC Chain, for Every Subject

q_beta_f(): M-Step Expected Log-Likelihood with respect to Beta

q_gamma_f(): M-Step Expected Log-Likelihood with respect to Gamma

q_delta_f(): M-Step Expected Log-Likelihood with respect to Delta

sum_every_n(): Sum Every "n"th Element

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

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

w_j_2stage(): Compute E-step for Two-Stage Binary Outcome Misclassification Model Estimated With the EM-Algorithm