Penalized Cox regression ORSF control
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
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
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())
#> ---------- Oblique random survival forest
#>
#> Linear combinations: Penalized Cox regression
#> N observations: 276
#> N events: 111
#> N trees: 25
#> N predictors total: 17
#> N predictors per node: 5
#> Average leaves per tree: 22
#> Min observations in leaf: 5
#> Min events in leaf: 1
#> OOB stat value: 0.55
#> OOB stat type: Harrell's C-statistic
#> Variable importance: anova
#>
#> -----------------------------------------