Penalized Cox regression ORSF controlSource:
Use regularized Cox proportional hazard models to identify linear combinations of input variables while fitting an orsf model.
(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