TBF Methodology Extension for Multinomial Outcomes
Marginal likelihoods based on AIC or BIC
Formulas of all the candidate models
Convert a PMP object into a data frame
Cause-specific variable selection (CSVS)
Prior model probability
Posterior inclusion probabilities (PIPs) by landmarking
Plot a CSVS object
Class for PMP objects
Posterior model probability
Posterior inclusion probability (PIP)
Samples from a PMP object
Test-based Bayes factor
Ingredients to calculate the TBF
Objective Bayesian variable selection for multinomial regression and d...
Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].