Penalized Cox regression ORSF control

## Arguments

- alpha
(

*double*) The elastic net mixing parameter. A value of 1 gives the lasso penalty, and a value of 0 gives the ridge penalty. If multiple values of alpha are given, then a penalized model is fit using each alpha value prior to splitting a node.- df_target
(

*integer*) Preferred number of variables used in a linear combination.- ...
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

`df_target`

has to be less than `mtry`

, which is a separate argument in
orsf that indicates the number of variables chosen at random prior to
finding a linear combination of those variables.

## References

Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox's proportional hazards model via coordinate descent. *Journal of statistical software* 2011 Mar; 39(5):1. DOI: 10.18637/jss.v039.i05

## See also

linear combination control functions
`orsf_control_cph()`

,
`orsf_control_custom()`

,
`orsf_control_fast()`

## Examples

```
# orsf_control_net() is considerably slower than orsf_control_cph(),
# The example uses n_tree = 25 so that my examples run faster,
# but you should use at least 500 trees in applied settings.
orsf(data = pbc_orsf,
formula = Surv(time, status) ~ . - id,
n_tree = 25,
control = orsf_control_net())
#> ---------- Oblique random survival forest
#>
#> Linear combinations: Penalized Cox regression
#> N observations: 276
#> N events: 111
#> N trees: 25
#> N predictors total: 17
#> N predictors per node: 5
#> Average leaves per tree: 22
#> Min observations in leaf: 5
#> Min events in leaf: 1
#> OOB stat value: 0.55
#> OOB stat type: Harrell's C-statistic
#> Variable importance: anova
#>
#> -----------------------------------------
```