# Qcoxph.control: Auxiliary for Controlling Qcoxph Fitting In Qest: Quantile-Based Estimator

 Qcoxph.control R Documentation

## Auxiliary for Controlling Qcoxph Fitting

### Description

Auxiliary function for controlling `Qcoxph` fitting. Estimation proceeds in three steps: (i) evaluation of starting points; (iia) stochastic gradient-based optimization (iib) standard gradient-based optimization; and (iii) Newton-Raphson. Step (i) is based on a preliminary fit of a Cox model via `coxph`. Steps (iia) and (iib) find an approximate solution, and make sure that the Jacobian matrix is well-defined. Finally, step (iii) finds a more precise solution.

### Usage

``````Qcoxph.control(tol = 1e-8, maxit, safeit, alpha0, display = FALSE)
``````

### Arguments

 `tol` tolerance for convergence of Newton-Raphson algorithm, default is 1e-8. `maxit` maximum number of iterations of Newton-Raphson algorithm. If not provided, a default is computed as `50 + 25*npar`, where `npar` is the total number of parameters. `safeit` maximum number of iterations of gradient-search algorithm. If not provided, a default is computed as `10 + 5*npar`, where `npar` is the total number of parameters. `alpha0` step size for the preliminary gradient-based iterations. If estimation fails, you can try choosing a small value of `alpha0`. If `alpha0` is missing, an adaptive choiche will be made internally. `display` Logical. If `TRUE`, tracing information on the progress of the optimization is printed on screen. Default is `FALSE`.

### Details

If called with no arguments, `Qcoxph.control()` returns a list with the current settings of these parameters. Any arguments included in the call sets those parameters to the new values, and then silently returns.

### Value

A list with named elements as in the argument list

### Author(s)

Gianluca Sottile <gianluca.sottile@unipa.it> Paolo Frumento <paolo.frumento@unipi.it>

`Qcoxph`