Survival dimension reduction and row-support selection

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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.

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
#>       criterion    value
#> 1           AIC 427.8951
#> 2           BIC 433.1054
#> 3          CAIC 435.1054
#> 4    ICOMP_IFIM 423.9302
#> 5 ICOMP_MISSPEC 424.0991
path <- fit_ssurvdr_sparse_path(dat$X, dat$time, dat$status,
                               dense_fit = fit, support_grid = c(4, 8, 12))
path$path
#>   support_size support_fraction retained_energy_proportion
#> 1            4        0.3333333                  0.5408401
#> 2            8        0.6666667                  0.8532966
#> 3           12        1.0000000                  1.0000000
#>   projection_distance_from_dense orthogonality_error      AIC      BIC     CAIC
#> 1                      1.3552268        4.440892e-16 430.0743 435.2847 437.2847
#> 2                      0.7660375        2.220446e-16 429.2443 434.4546 436.4546
#> 3                      0.0000000        2.220446e-16 427.8951 433.1054 435.1054
#>   ICOMP_IFIM ICOMP_MISSPEC   c_index c_index_se runtime_seconds
#> 1   426.0928      426.1755 0.5498155 0.04581237      0.02411699
#> 2   425.2594      425.3125 0.5749748 0.04381670      0.02133012
#> 3   423.9302      424.0991 0.5816840 0.04248321      0.05374599
#>   is_dense_support status error_message
#> 1            FALSE     ok          <NA>
#> 2            FALSE     ok          <NA>
#> 3             TRUE     ok          <NA>

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

weights <- compute_c1f_weights(fit$basis_fit)
range(weights$weights)
#> [1] 0.5242278 2.2526199
weights$c1f
#> [1] NA

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.