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Penalized Cox regression ORSF control

Source: R/orsf_control.R
orsf_control_net.Rd

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

Usage

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

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[Superseded]

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

  1. Simon, Noah, Friedman, Jerome, Hastie, Trevor, Tibshirani, Rob (2011). "Regularization paths for Cox's proportional hazards model via coordinate descent." Journal of statistical software, 39(5), 1.

See also

linear combination control functions orsf_control(), orsf_control_cph(), orsf_control_custom(), orsf_control_fast()