opt_Cox | R Documentation |

This function computes posterior mode estimates of the parameters of a flexible Cox model
with structured additive predictors using a Newton-Raphson algorithm. Integrals are solved
numerically. Moreover, optimum smoothing variances are computed using a stepwise optimization,
see also the details section of function `bfit`

.

opt_Cox(x, y, start, weights, offset, criterion = c("AICc", "BIC", "AIC"), nu = 0.1, update.nu = TRUE, eps = .Machine$double.eps^0.25, maxit = 400, verbose = TRUE, digits = 4, ...) cox_mode(x, y, start, weights, offset, criterion = c("AICc", "BIC", "AIC"), nu = 0.1, update.nu = TRUE, eps = .Machine$double.eps^0.25, maxit = 400, verbose = TRUE, digits = 4, ...)

`x` |
The |

`y` |
The model response, as returned from function |

`start` |
A named numeric vector containing possible starting values, the names are based on
function |

`weights` |
Prior weights on the data, as returned from function |

`offset` |
Can be used to supply model offsets for use in fitting,
returned from function |

`criterion` |
Set the information criterion that should be used, e.g., for smoothing
variance selection. Options are the corrected AIC |

`nu` |
Calibrates the step length of parameter updates of one Newton-Raphson update. |

`update.nu` |
Should the updating step length be optimized in each iteration of the backfitting algorithm. |

`eps` |
The relative convergence tolerance of the backfitting algorithm. |

`maxit` |
The maximum number of iterations for the backfitting algorithm |

`verbose` |
Print information during runtime of the algorithm. |

`digits` |
Set the digits for printing when |

`...` |
Currently not used. |

A list containing the following objects:

`fitted.values` |
A named list of the fitted values of the modeled parameters of the selected distribution. |

`parameters` |
The estimated set regression coefficients and smoothing variances. |

`edf` |
The equivalent degrees of freedom used to fit the model. |

`logLik` |
The value of the log-likelihood. |

`logPost` |
The value of the log-posterior. |

`hessian` |
The Hessian matrix evaluated at the posterior mode. |

`converged` |
Logical, indicating convergence of the backfitting algorithm. |

`time` |
The runtime of the algorithm. |

Umlauf N, Klein N, Zeileis A (2016). Bayesian Additive Models for Location
Scale and Shape (and Beyond). *(to appear)*

`sam_Cox`

, `cox_bamlss`

, `surv_transform`

,
`simSurv`

, `bamlss`

## Please see the examples of function sam_Cox()!

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