<code>TBF_ingredients_object</code> ingredients for the TBF
(and g) calculation.

ingredients

if <code>ingredients</code> is <code>NULL</code>, formula of the model
including all potential variables

fullModel

tells us which method for the definition of g should be
used. Possibilities are: <code>LEB</code>, <code>GEB</code>, <code>g=n</code>, <code>hyperG</code>,
<code>ZS</code>, <code>ZSadapted</code> and <code>hyperGN</code>

method

the data frame with all the information. Only needed if
<code>ingredients</code> is <code>NULL</code>

data

Boolean variable telling us whether a 'simple'
multinomial regression is looked for or if the goal is a discrete
survival-time model for multiple modes of failure is needed.

discreteSurv

should a dependent or a flat prior be used on the model space?
Only needed if <code>method = `GEB`</code>.

prior

Which package should be used to fit the models; by default
the <code>nnet</code> package is used; we could also specify to use the package
'VGAM'

package

Only needs to be specified with package <code>nnet</code>: maximal
number of iterations

maxit

This function computes the TBF as well as g

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>].

TBF: Test-based Bayes factor

Description

Usage

Arguments

Value