orsf_control_net {aorsf} R Documentation

## Penalized Cox regression ORSF control

### Description

Penalized Cox regression ORSF control

### 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

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())


[Package aorsf version 0.0.4 Index]