# cox_mode

##### Cox Model Posterior Mode Estimation

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`

.

- Keywords
- regression, survival

##### Usage

```
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, ...)
```

##### Arguments

- x
The

`x`

list, as returned from function`bamlss.frame`

and transformed by function`surv_transform`

, holding all model matrices and other information that is used for fitting the model.- y
The model response, as returned from function

`bamlss.frame`

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

`parameters`

.- weights
Prior weights on the data, as returned from function

`bamlss.frame`

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

`bamlss.frame`

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

`"AICc"`

, the`"BIC"`

and`"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

`verbose = TRUE`

.- …
Currently not used.

##### Value

A list containing the following objects:

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

The estimated set regression coefficients and smoothing variances.

The equivalent degrees of freedom used to fit the model.

The value of the log-likelihood.

The value of the log-posterior.

The Hessian matrix evaluated at the posterior mode.

Logical, indicating convergence of the backfitting algorithm.

The runtime of the algorithm.

##### References

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

##### See Also

##### Examples

```
# NOT RUN {
## Please see the examples of function cox_mcmc()!
# }
```

*Documentation reproduced from package bamlss, version 1.1-2, License: GPL-2 | GPL-3*