`cox.mcmc.Rd`

This sampler function implements a derivative based MCMC algorithm for flexible Cox models with structured additive predictors.

sam_Cox(x, y, family, start, weights, offset, n.iter = 1200, burnin = 200, thin = 1, verbose = TRUE, digits = 4, step = 20, ...) cox_mcmc(x, y, family, start, weights, offset, n.iter = 1200, burnin = 200, thin = 1, verbose = TRUE, digits = 4, step = 20, ...)

x | The |
---|---|

y | The model response, as returned from function |

family | A bamlss family object, see |

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 |

n.iter | Sets the number of MCMC iterations. |

burnin | Sets the burn-in phase of the sampler, i.e., the number of starting samples that should be removed. |

thin | Defines the thinning parameter for MCMC simulation. E.g., |

verbose | Print information during runtime of the algorithm. |

digits | Set the digits for printing when |

step | How many times should algorithm runtime information be printed, divides |

… | Currently not used. |

The sampler uses derivative based proposal functions to create samples of parameters.
For time-dependent functions the proposals are based on one Newton-Raphson iteration centered
at the last state, while for the time-constant functions proposals can be based
on iteratively reweighted least squares (IWLS), see also function `GMCMC`

.
The integrals that are part of the time-dependent function updates are solved numerically.
In addition, smoothing variances are sampled using slice sampling.

The function returns samples of parameters. The samples are provided as a
`mcmc`

matrix.

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

# NOT RUN { library("survival") set.seed(123) ## Simulate survival data. d <- simSurv(n = 500) ## Formula of the survival model, note ## that the baseline is given in the first formula by s(time). f <- list( Surv(time, event) ~ s(time) + s(time, by = x3), gamma ~ s(x1) + s(x2) ) ## Cox model with continuous time. ## Note the the family object cox_bamlss() sets ## the default optimizer and sampler function! ## First, posterior mode estimates are computed ## using function opt_Cox(), afterwards the ## sampler sam_Cox() is started. b <- bamlss(f, family = "cox", data = d) ## Plot estimated effects. plot(b) # }