AIC_BIC_based_marginalLikelihood {TBFmultinomial}

Marginal likelihoods based on AIC or BIC

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

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

Author(s)

Rachel Heyard

Examples

# 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)