orsf_control_cph {aorsf}R Documentation

Cox regression ORSF control


Use the coefficients from a proportional hazards model to create linear combinations of predictor variables while fitting an orsf model.


orsf_control_cph(method = "efron", eps = 1e-09, iter_max = 20, ...)



(character) a character string specifying the method for tie handling. If there are no ties, all the methods are equivalent. Valid options are 'breslow' and 'efron'. The Efron approximation is the default because it is more accurate when dealing with tied event times and has similar computational efficiency compared to the Breslow method.


(double) When using Newton Raphson scoring to identify linear combinations of inputs, iteration continues in the algorithm until the relative change in the log partial likelihood is less than eps, or the absolute change is less than sqrt(eps). Must be positive. A default value of 1e-09 is used for consistency with survival::coxph.control.


(integer) iteration continues until convergence (see eps above) or the number of attempted iterations is equal to iter_max.


Further arguments passed to or from other methods (not currently used).


code from the survival package was modified to make this routine.

For more details on the Cox proportional hazards model, see coxph and/or Therneau and Grambsch (2000).


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


Therneau T.M., Grambsch P.M. (2000) The Cox Model. In: Modeling Survival Data: Extending the Cox Model. Statistics for Biology and Health. Springer, New York, NY. DOI: 10.1007/978-1-4757-3294-8_3

See Also

linear combination control functions orsf_control_custom(), orsf_control_fast(), orsf_control_net()


orsf(data = pbc_orsf,
     formula = Surv(time, status) ~ . - id,
     control = orsf_control_cph())

[Package aorsf version 0.0.4 Index]