gridsearch_survdnn: Grid Search for survdnn Hyperparameters

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

Grid Search for survdnn Hyperparameters

Description

Performs grid search over user-specified hyperparameters and evaluates performance on a validation set.

Usage

gridsearch_survdnn(
  formula,
  train,
  valid,
  times,
  metrics = c("cindex", "ibs"),
  param_grid,
  .seed = 42,
  .device = c("auto", "cpu", "cuda")
)

Arguments

formula	A survival formula (e.g., 'Surv(time, status) ~ .')
train	Training dataset
valid	Validation dataset
times	Evaluation time points (numeric vector)
metrics	Evaluation metrics (character vector): any of "cindex" and "ibs".
param_grid	A named list of hyperparameters to search over. Currently supported entries are hidden, lr, activation, epochs, and loss.
.seed	Optional random seed for reproducibility
.device	Character string indicating the computation device used when fitting all models in the grid search. One of "auto", "cpu", or "cuda". This is a runtime setting and is not part of the hyperparameter grid.

Value

A tibble with configurations and their validation metrics

Examples

library(survdnn)
library(survival)
set.seed(123)

# Simulate small dataset
n <- 300
x1 <- rnorm(n); x2 <- rbinom(n, 1, 0.5)
time <- rexp(n, rate = 0.1)
status <- rbinom(n, 1, 0.7)
df <- data.frame(time, status, x1, x2)

# Split into training and validation
idx <- sample(seq_len(n), 0.7 * n)
train <- df[idx, ]
valid <- df[-idx, ]

# Define formula and param grid
formula <- Surv(time, status) ~ x1 + x2
param_grid <- list(
  hidden     = list(c(16, 8), c(32, 16)),
  lr         = c(1e-3),
  activation = c("relu"),
  epochs     = c(100),
  loss       = c("cox", "coxtime")
)

# Run grid search
results <- gridsearch_survdnn(
  formula = formula,
  train   = train,
  valid   = valid,
  times   = c(10, 20, 30),
  metrics = c("cindex", "ibs"),
  param_grid = param_grid
)

# View summary
dplyr::group_by(results, hidden, lr, activation, epochs, loss, metric) |>
  dplyr::summarise(mean = mean(value, na.rm = TRUE), .groups = "drop")

survdnn documentation built on Jan. 8, 2026, 9:07 a.m.