Correcting Misclassified Binary Outcomes in Association Studies

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Documentation for package ‘COMBO’ version 1.1.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

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
COMBO_data	Generate Data to use in COMBO Functions
COMBO_data_2stage	Generate data to use in two-stage COMBO Functions
COMBO_EM	EM-Algorithm Estimation of the Binary Outcome Misclassification Model
COMBO_EM_2stage	EM-Algorithm Estimation of the Two-Stage Binary Outcome Misclassification Model
COMBO_EM_data	Test data for the COMBO_EM function
COMBO_MCMC	MCMC Estimation of the Binary Outcome Misclassification Model
COMBO_MCMC_2stage	MCMC Estimation of the Two-Stage Binary Outcome Misclassification Model
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
LSAC_data	Example data from The Law School Admissions Council's (LSAC) National Bar Passage Study (Linda Wightman, 1998)
mean_pistarjj_compute	Compute the Mean Conditional Probability of Correct Classification, by True Outcome Across all Subjects
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
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
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
pi_compute	Compute Probability of Each True Outcome, for Every Subject
q_beta_f	M-Step Expected Log-Likelihood with respect to Beta
q_delta_f	M-Step Expected Log-Likelihood with respect to Delta
q_gamma_f	M-Step Expected Log-Likelihood with respect to Gamma
sum_every_n	Sum Every "n"th Element
sum_every_n1	Sum Every "n"th Element, then add 1
true_classification_prob	Compute Probability of Each True Outcome, for Every Subject
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