orsf_control_net {aorsf} | R Documentation |
Penalized Cox regression ORSF control
orsf_control_net(alpha = 1/2, df_target = NULL, ...)
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). |
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.
an object of class 'orsf_control'
, which should be used as
an input for the control
argument of orsf.
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()
# 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())