tune_survdnn: Tune Hyperparameters for a survdnn Model via Cross-Validation

In survdnn: Deep Neural Networks for Survival Analysis with R 'torch'

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,
  .device = c("auto", "cpu", "cuda"),
  .threads = NULL,
  na_action = c("omit", "fail"),
  verbose = TRUE,
  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.
.device	Character string indicating the computation device used when fitting models during cross-validation and refitting. One of '"auto"', '"cpu"', or '"cuda"'. '"auto"' uses CUDA if available, otherwise falls back to CPU.
.threads	Optional positive integer. If provided, sets Torch CPU thread count for all nested model fits via 'torch::torch_set_num_threads()'.
na_action	Character. How to handle missing values: '"omit"' drops incomplete rows; '"fail"' errors if any NA is present.
verbose	Logical; whether to print tuning progress and propagate verbose messages to nested cross-validation and optional refit (default: TRUE).
refit	Logical. If TRUE, refits the best model on the full dataset.
return	One of "all", "summary", or "best_model": "all" Returns the full cross-validation result across all combinations. "summary" Returns averaged results per configuration. "best_model" Returns the refitted model or best hyperparameters.

Value

A tibble or model object depending on the 'return' value.

survdnn documentation built on April 29, 2026, 9:07 a.m.