Reference-Based Multiple Imputation for Ordinal/Binary Response
Apply Copy-Reference(CR) Method to Update JAGS MCMC outputs under MAR ...
Apply Delta adjustment to Update JAGS MCMC outputs under MAR for Cumul...
Apply Jump-to-Reference(J2R) Method to Update JAGS MCMC outputs under ...
Common Parameters used by functions of remiod
Extract a specified number of multiple imputed datasets
Obtain ordinal results based on log-odds (eta) and cut-offs (gamma) fr...
Create multiple imputed datasets based on assigned imputation method.
Prepare imputation-model-related information
Extract specific parameters from MCMC samples
Apply Copy-Reference(CR) Method to Update JAGS MCMC outputs under MAR ...
Apply Delta adjustment to Update JAGS MCMC outputs under MAR for Gener...
Apply Jump-to-Reference(J2R) Method to Update JAGS MCMC outputs under ...
Listing the sequence of models used for imputation
Visualizing the posterior sample Creates a set of plots for visually e...
Takes multiply imputed datasets (as generated by the `extract_MIdata()...
Apply Copy-Reference(CR) Method to Update JAGS MCMC outputs under MAR ...
Apply Delta adjustment to Update JAGS MCMC outputs under MAR for Cumul...
Apply Jump-to-Reference(J2R) Method to Update JAGS MCMC outputs under ...
Extract MCMC samples of monitored parameters from JointAI object. Code...
Reference-Based Multiple Imputation for Ordinal/Binary Response
Summarize the results from an object of class remiod
Implement controlled multiple imputation algorithms proposed by Tang
Apply a MI method following initial run of remoid function
Reference-based multiple imputation of ordinal and binary responses under Bayesian framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis.