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TBFmultinomial (version 0.1.3)

TBF Methodology Extension for Multinomial Outcomes

Description

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) ] and for the Cox model [Held et al. (2016) ].

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Version

Install

install.packages('TBFmultinomial')

Monthly Downloads

106

Version

0.1.3

License

GPL (>= 2)

Maintainer

Rachel Heyard

Last Published

October 12th, 2018

Functions in TBFmultinomial (0.1.3)

PIPs_by_landmarking

Posterior inclusion probabilities (PIPs) by landmarking
TBFmultinomial-package

Objective Bayesian variable selection for multinomial regression and discrete time-to-event models with competing risks
VAP_data

Data on VAP acquistion in one ICU
all_formulas

Formulas of all the candidate models
TBF_ingredients

Ingredients to calculate the TBF
plot_CSVS

Plot a CSVS object
postInclusionProb

Posterior inclusion probability (PIP)
AIC_BIC_based_marginalLikelihood

Marginal likelihoods based on AIC or BIC
CSVS

Cause-specific variable selection (CSVS)
PMP

Posterior model probability
TBF

Test-based Bayes factor
as.data.frame.PMP

Convert a PMP object into a data frame
model_priors

Prior model probability
PMP-class

Class for PMP objects
sample_multinomial

Samples from a PMP object