survdnn: Fit a Deep Neural Network for Survival Analysis
In survdnn: Deep Neural Networks for Survival Analysis Using 'torch'

survdnn

R Documentation

Fit a Deep Neural Network for Survival Analysis

Description

Trains a deep neural network (DNN) to model right-censored survival data using one of the predefined loss functions: Cox, AFT, or Coxtime.

Usage

survdnn(
  formula,
  data,
  hidden = c(32L, 16L),
  activation = "relu",
  lr = 1e-04,
  epochs = 300L,
  loss = c("cox", "cox_l2", "aft", "coxtime"),
  verbose = TRUE
)

Arguments

`formula`	A survival formula of the form 'Surv(time, status) ~ predictors'.
`data`	A data frame containing the variables in the model.
`hidden`	Integer vector. Sizes of the hidden layers (default: c(32, 16)).
`activation`	Character string specifying the activation function to use in each layer. Supported options: '"relu"', '"leaky_relu"', '"tanh"', '"sigmoid"', '"gelu"', '"elu"', '"softplus"'.
`lr`	Learning rate for the Adam optimizer (default: '1e-4').
`epochs`	Number of training epochs (default: 300).
`loss`	Character name of the loss function to use. One of '"cox"', '"cox_l2"', '"aft"', or '"coxtime"'.
`verbose`	Logical; whether to print loss progress every 50 epochs (default: TRUE).

Value

An object of class '"survdnn"' containing:

model: Trained 'nn_module' object.
formula: Original survival formula.
data: Training data used for fitting.
xnames: Predictor variable names.
x_center: Column means of predictors.
x_scale: Column standard deviations of predictors.
loss_history: Vector of loss values per epoch.
final_loss: Final training loss.
loss: Loss function name used ("cox", "aft", etc.).
activation: Activation function used.
hidden: Hidden layer sizes.
lr: Learning rate.
epochs: Number of training epochs.

Examples

set.seed(123)
df <- data.frame(
  time = rexp(100, rate = 0.1),
  status = rbinom(100, 1, 0.7),
  x1 = rnorm(100),
  x2 = rbinom(100, 1, 0.5)
)
mod <- survdnn(Surv(time, status) ~ x1 + x2, data = df, epochs = 5

, loss = "cox", verbose = FALSE)
mod$final_loss

survdnn documentation built on Aug. 8, 2025, 6:05 p.m.