Description Usage Arguments Value Author(s) Examples

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

1 2 3 |

`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 |

`package` |
Which package should be used to fit the models; by default
the |

`maxit` |
Only needs to be specified with package |

`numberCores` |
How many cores should be used in parallel? |

a vector with the marginal likelihoods of all candidate models

Rachel Heyard

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)
``` |

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