survdnn_losses {survdnn}R Documentation

Loss Functions for survdnn Models

Description

These functions define various loss functions used internally by 'survdnn()' for training deep neural networks on right-censored survival data.

Usage

cox_loss(pred, true)

cox_l2_loss(pred, true, lambda = 1e-04)

aft_loss(pred, true)

coxtime_loss(pred, true)

Arguments

pred

A tensor of predicted values (typically linear predictors or log-times).

true

A tensor with two columns: observed time and status (1 = event, 0 = censored).

lambda

Regularization parameter for 'cox_l2_loss' (default: '1e-4').

Value

A scalar 'torch_tensor' representing the loss value.

Supported Losses

- **Cox partial likelihood loss** ('cox_loss'): Negative partial log-likelihood used in proportional hazards modeling. - **L2-penalized Cox loss** ('cox_l2_loss'): Adds L2 regularization to the Cox loss. - **Accelerated Failure Time (AFT) loss** ('aft_loss'): Mean squared error between predicted and log-transformed event times, applied to uncensored observations only. - **CoxTime loss** ('coxtime_loss'): Implements the partial likelihood loss from Kvamme & Borgan (2019), used in Cox-Time models.

Examples

# Used internally by survdnn()

[Package survdnn version 0.6.0 Index]