loss_one_minus_integrated_cd_auc {survex} | R Documentation |
Calculate integrated C/D AUC loss
Description
This function subtracts integrated the C/D AUC metric from one to obtain a loss function whose lower values indicate better model performance (useful for permutational feature importance)
Usage
loss_one_minus_integrated_cd_auc(
y_true = NULL,
risk = NULL,
surv = NULL,
times = NULL
)
Arguments
y_true |
a |
risk |
ignored, left for compatibility with other metrics |
surv |
a matrix containing the predicted survival functions for the considered observations, each row represents a single observation, whereas each column one time point |
times |
a vector of time points at which the survival function was evaluated |
Value
numeric from 0 to 1, lower values indicate better performance
#' @section References:
[1] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.
[2] Hung, Hung, and Chin‐Tsang Chiang. "Optimal composite markers for time‐dependent receiver operating characteristic curves with censored survival data." Scandinavian Journal of Statistics 37.4 (2010): 664-679.
See Also
integrated_cd_auc()
cd_auc()
loss_one_minus_cd_auc()
Examples
library(survival)
library(survex)
cph <- coxph(Surv(time, status) ~ ., data = veteran, model = TRUE, x = TRUE, y = TRUE)
cph_exp <- explain(cph)
y <- cph_exp$y
times <- cph_exp$times
surv <- cph_exp$predict_survival_function(cph, cph_exp$data, times)
# calculating directly
loss_one_minus_integrated_cd_auc(y, surv = surv, times = times)