Description Usage Arguments Value Author(s) Examples

This function computes the posterior probability of all candidate models

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 frame with all the information |

`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. |

`modelPrior` |
optionaly the model priors can be computed before if candidateModels is different from NULL. |

`method` |
tells us which method for the definition of g should be
used. Possibilities are: |

`prior` |
should a dependent or a flat prior be used on the model space?
Only needed 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? |

an object of class `TBF.ingredients`

Rachel Heyard

1 2 3 4 5 6 7 8 9 10 11 | ```
# extract the data:
data("VAP_data")
# the definition of the full model with three potential predictors:
FULL <- outcome ~ ns(day, df = 4) + gender + type + SOFA
# here we define time as a spline with 3 knots
# computation of the posterior model probabilities:
test <- PMP(fullModel = FULL, data = VAP_data,
discreteSurv = TRUE, maxit = 150)
class(test)
``` |

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