survdnn (version 0.6.0)

gridsearch_survdnn: 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
)

Value

A tibble with configurations and their validation metrics

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", "ibs", "brier"

param_grid

A named list of hyperparameters (`hidden`, `lr`, `activation`, `epochs`, `loss`)

.seed

Optional random seed for reproducibility

Examples

# \donttest{
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")
# }