plot_loss

Visualize the evolution of the training loss across epochs for a fitted
`survdnn` model. Helps inspect convergence, instability, or callback effects
(e.g., early stopping).

Provides deep learning models for right-censored survival data using the 'torch' backend.
Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox,
and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation,
hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score,
and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>.

Imad El Badisy

survdnn

Deep Neural Networks for Survival Analysis with R 'torch'

Imad EL BADISY

plot_loss function

<dl><dt>object</dt>
<dd>A fitted `survdnn` model.</dd>
<dt>smooth</dt>
<dd>Logical; if `TRUE`, overlays a smoothed loess curve.</dd>
<dt>log_y</dt>
<dd>Logical; if `TRUE`, uses a log10 y-scale.</dd>
<dt>...</dt>
<dd>Reserved for future use.</dd></dl>

Arguments

Plot Training Loss for a survdnn Model — plot_loss

<dl>

<dt>object</dt>
<dd>A fitted `survdnn` model.</dd>


<dt>smooth</dt>
<dd>Logical; if `TRUE`, overlays a smoothed loess curve.</dd>


<dt>log_y</dt>
<dd>Logical; if `TRUE`, uses a log10 y-scale.</dd>


<dt>...</dt>
<dd>Reserved for future use.</dd>

</dl>

plot_loss: Plot Training Loss for a survdnn Model

Description

Usage

Value

Arguments