Use regularized Cox proportional hazard models to identify linear combinations of input variables while fitting an orsf model.
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
![[Superseded]](figures/lifecycle-superseded.svg)
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(),
orsf_control()