orsf_control_net: Penalized Cox regression ORSF control

In aorsf: Accelerated Oblique Random Survival Forests

Penalized Cox regression ORSF control

Description

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).

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.

Value

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

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())

aorsf documentation built on Oct. 26, 2023, 5:08 p.m.