sam_Cox {bamlss} | R Documentation |

## Cox Model Markov Chain Monte Carlo

### Description

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

### Usage

```
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, ...)
```

### Arguments

`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. |

### Details

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.

### Value

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

matrix.

### References

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

### See Also

`opt_Cox`

, `cox_bamlss`

, `surv_transform`

,
`simSurv`

, `bamlss`

### Examples

```
## 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)
## End(Not run)
```

*bamlss*version 1.2-3 Index]