This function subtracts the C-index metric from one to obtain a loss function whose lower values indicate better model performance (useful for permutational feature importance)
loss_one_minus_c_index(y_true =NULL, risk =NULL, surv =NULL, times =NULL)
Arguments
y_true: a survival::Surv object containing the times and statuses of observations for which the metric will be evaluated
risk: a numeric vector of risk scores corresponding to each observation
surv: ignored, left for compatibility with other metrics
times: ignored, left for compatibility with other metrics
Returns
numeric from 0 to 1, lower values indicate better performance
References
[1] Harrell, F.E., Jr., et al. "Regression modelling strategies for improved prognostic prediction." Statistics in Medicine 3.2 (1984): 143-152.
Examples
library(survival)library(survex)rotterdam <- survival::rotterdam
rotterdam$year <-NULLcox_rotterdam_rec <- coxph(Surv(rtime, recur)~ ., data = rotterdam, model =TRUE, x =TRUE, y =TRUE)coxph_explainer <- explain(cox_rotterdam_rec)risk <- coxph_explainer$predict_function(coxph_explainer$model, coxph_explainer$data)loss_one_minus_c_index(y_true = coxph_explainer$y, risk = risk)