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Use regularized Cox proportional hazard models to identify linear combinations of input variables while fitting an orsf model.


orsf_control_net(alpha = 1/2, df_target = NULL, ...)



(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.


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


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


an object of class 'orsf_control', which should be used as an input for the control argument of orsf.



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.


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()