# AIC_BIC_based_marginalLikelihood: Marginal likelihoods based on AIC or BIC In TBFmultinomial: TBF Methodology Extension for Multinomial Outcomes

## Description

This function computes the marginal likelihoods based on the AIC or on the BIC, that will later be used to calculate the TBF.

## Usage

 ```1 2 3``` ```AIC_BIC_based_marginalLikelihood(fullModel = NULL, candidateModels = NULL, data, discreteSurv = TRUE, AIC = TRUE, package = "nnet", maxit = 150, numberCores = 1) ```

## Arguments

 `fullModel` formula of the model including all potential variables `candidateModels` Instead of defining the full model we can also specify the candidate models whose deviance statistic and d.o.f should be computed `data` the data `discreteSurv` 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. `AIC` if `TRUE`, AIC will be used, else we use BIC `package` Which package should be used to fit the models; by default the `nnet` package is used; we could also specify to use the package 'VGAM' `maxit` Only needs to be specified with package `nnet`: maximal number of iterations `numberCores` How many cores should be used in parallel?

## Value

a vector with the marginal likelihoods of all candidate models

Rachel Heyard

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```# data extraction: data("VAP_data") # the definition of the full model with three potential predictors: FULL <- outcome ~ ns(day, df = 4) + gender + type + SOFA # here the define time as a spline with 3 knots # now we can compute the marginal likelihoods based on the AIC f.ex: mL_AIC <- AIC_BIC_based_marginalLikelihood(fullModel = FULL, data = VAP_data, discreteSurv = TRUE, AIC = TRUE) ```

TBFmultinomial documentation built on May 2, 2019, 2:11 p.m.