tune_survdnn {survdnn} | R Documentation |
Tune Hyperparameters for a survdnn Model via Cross-Validation
Description
Performs k-fold cross-validation over a user-defined hyperparameter grid and selects the best configuration according to the specified evaluation metric.
Usage
tune_survdnn(
formula,
data,
times,
metrics = "cindex",
param_grid,
folds = 3,
.seed = 42,
refit = FALSE,
return = c("all", "summary", "best_model")
)
Arguments
formula |
A survival formula, e.g., 'Surv(time, status) ~ x1 + x2'. |
data |
A data frame. |
times |
A numeric vector of evaluation time points. |
metrics |
A character vector of evaluation metrics: "cindex", "brier", or "ibs". Only the first metric is used for model selection. |
param_grid |
A named list defining hyperparameter combinations to evaluate. Required names: 'hidden', 'lr', 'activation', 'epochs', 'loss'. |
folds |
Number of cross-validation folds (default: 3). |
.seed |
Optional seed for reproducibility (default: 42). |
refit |
Logical. If TRUE, refits the best model on the full dataset. |
return |
One of "all", "summary", or "best_model":
|
Value
A tibble or model object depending on the 'return' value.