R: Cox Model Posterior Mode Estimation

opt_Cox {bamlss} | R Documentation

## Cox Model Posterior Mode Estimation

### Description

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`.

### Usage

    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, ...)

### 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:

`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.

### References

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

### See Also

`sam_Cox`, `cox_bamlss`, `surv_transform`, `simSurv`, `bamlss`

### Examples

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

[Package bamlss version 1.1-7 Index]