Use the coefficients from a proportional hazards model to create linear combinations of predictor variables while fitting an orsf model.

## Arguments

- method
(

*character*) a character string specifying the method for tie handling. If there are no ties, all the methods are equivalent. Valid options are 'breslow' and 'efron'. The Efron approximation is the default because it is more accurate when dealing with tied event times and has similar computational efficiency compared to the Breslow method.- eps
(

*double*) When using Newton Raphson scoring to identify linear combinations of inputs, iteration continues in the algorithm until the relative change in the log partial likelihood is less than`eps`

, or the absolute change is less than`sqrt(eps)`

. Must be positive. A default value of 1e-09 is used for consistency with survival::coxph.control.- iter_max
(

*integer*) iteration continues until convergence (see`eps`

above) or the number of attempted iterations is equal to`iter_max`

.- ...
Further arguments passed to or from other methods (not currently used).

## Value

an object of class `'orsf_control'`

, which should be used as
an input for the `control`

argument of orsf.

## Details

code from the survival package was modified to make this routine.

For more details on the Cox proportional hazards model, see coxph and/or Therneau and Grambsch (2000).

## References

Therneau T.M., Grambsch P.M. (2000) The Cox Model. In: Modeling Survival Data: Extending the Cox Model. Statistics for Biology and Health. Springer, New York, NY. DOI: 10.1007/978-1-4757-3294-8_3

## See also

linear combination control functions
`orsf_control_custom()`

,
`orsf_control_fast()`

,
`orsf_control_net()`

## Examples

```
orsf(data = pbc_orsf,
formula = Surv(time, status) ~ . - id,
control = orsf_control_cph())
#> ---------- Oblique random survival forest
#>
#> Linear combinations: Cox regression
#> N observations: 276
#> N events: 111
#> N trees: 500
#> N predictors total: 17
#> N predictors per node: 5
#> Average leaves per tree: 25
#> Min observations in leaf: 5
#> Min events in leaf: 1
#> OOB stat value: 0.84
#> OOB stat type: Harrell's C-statistic
#> Variable importance: anova
#>
#> -----------------------------------------
```