---
title: "Survival dimension reduction and row-support selection"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Survival dimension reduction and row-support selection}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

A dense SAVE basis reduces the predictors to a small set of scores. A Cox model
is fitted to those scores. The row-support path then evaluates subsets of the
original predictors by truncating the fitted basis and refitting the Cox model.

```{r}
library(ssurvdr)
dat <- simulate_ssurvdr(n = 100, p = 12, active = 1:3, seed = 2026)
fit <- fit_ssurvdr(dat$X, dat$time, dat$status, d = 2,
                  n_slices = 3, covariance = "mec", backend = "primal")
fit$criteria
path <- fit_ssurvdr_sparse_path(dat$X, dat$time, dat$status,
                               dense_fit = fit, support_grid = c(4, 8, 12))
path$path
```

Full support remains in the path so that sparsity is not forced. The procedure
uses the existing reduced Cox criteria; it adds no separate predictor-support
penalty. Training criterion selection is not an independent prediction assessment.

## Basis-adaptive weights

```{r}
weights <- compute_c1f_weights(fit$basis_fit)
range(weights$weights)
weights$c1f
```

With no matrix supplied, weights depend on the estimated basis row norms and the
scalar diagnostic is missing. With a matrix, the weights also use a
loading-weighted eigenvalue-dispersion adjustment. The historical `c1f` output
field contains the C1 arithmetic/geometric eigenvalue expression implemented by
this package; it does not directly enter the weights.

## Validation

For a test set, reuse the training centring/scaling and fitted basis. Never
estimate those transformations on the test outcomes. The package's simulation
evaluation function reports training concordance; the separate research scripts
implement the thesis validation workflows.