A simplified formula based interface to `glmBayesMfp`

to fit Cox models. Can return
Maximum a posteriori (MAP) model, Median probability model (MPM) or Bayesian model average (BMA).
Provides global empirical Bayes and AIC/BIC based model inference.

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`formula` |
model formula with Surv object as LHS and |

`data` |
data.frame for model variables |

`type` |
type of model to fit, one of "MAP","MPM","BMA","BMAFull" |

`baseline` |
how to calculate the baseline hazard function. "cox" uses unshrunken coefficients. "shrunk" refits baseline with shrunken coefficients (default). |

`globalEB` |
use global empirical bayes estimate of g (default=FALSE) |

`IC` |
use information criteria based model selection (default=FALSE). Either "AIC" or "BIC". |

`sep` |
estimate baseline hazard for each estimate of model coefficients (default=FALSE). |

`keepModelList` |
keep the model list returned by glmBayesMfp for MAP and MPM models (default=FALSE). |

`...` |
additional arguments to pass to |

`overrideConfig` |
replaces the the MAP model with the given configuration, which is passed to |

An object of S3 class `TBFcox`

or `TBFcox.sep`

if sep=TRUE.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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