Package {ssurvdr}

Compute basis-adaptive variable weights

Description

Computes adaptive variable weights using row-wise initial SDR signal strength and covariance-complexity adjustment. The function is intended for weighted sparse projection in high-dimensional sufficient dimension reduction.

Usage

compute_c1f_weights(
  basis_fit,
  matrix = NULL,
  alpha = 1,
  gamma = 1,
  eps = 1e-08,
  normalise = TRUE,
  cap = NULL
)

Arguments

Details

The returned c1f field is retained for compatibility. Its implemented formula is (p / 2) * log(arithmetic_mean / geometric_mean) of the stabilised eigenvalues, the same C1 expression used by compute_icomp(). The variable weights use row norms and a separate loading-weighted eigenvalue-dispersion score; the scalar c1f is a diagnostic and does not directly enter the weights. With matrix = NULL, the complexity scores are zero and c1f is missing. Finite weights are strictly positive, with no universal upper bound when cap = NULL. Normalisation precedes optional capping.

Value

A list containing adaptive weights, initial signal scores, complexity scores, and diagnostics.

Examples

basis <- matrix(c(1, 2, 4), ncol = 1)
weights <- compute_c1f_weights(basis, matrix = diag(c(1, 2, 4)))
weights$weights
weights$c1f

Compute information criteria for reduced Cox model

Description

Computes AIC, BIC, CAIC, ICOMP(IFIM), and ICOMP(misspec) for a reduced Cox proportional hazards model fitted after sufficient dimension reduction.

Usage

compute_icomp(cox_fit, eps = 1e-08)

Arguments

Value

A list containing information criteria, covariance-complexity diagnostics, and likelihood quantities.

Evaluate ssurvdr fitted model against known simulation truth

Description

Evaluates selected variables, subspace recovery, and survival prediction performance when the true active set and true SDR basis are known.

Usage

evaluate_ssurvdr(
  fit,
  simulation,
  variable_selection = NULL,
  top = NULL,
  threshold = 1e-06,
  screen_indices = NULL
)

Arguments

Value

A list containing variable-selection metrics, projection distance, C-index, selected variables, and diagnostics.

Extract sufficient dimension reduction basis

Description

Extracts leading sufficient dimension reduction directions from an object returned by save_kernel(). The function reuses the eigensystem already computed by save_kernel() and therefore avoids a redundant eigendecomposition.

Usage

extract_sdr_basis(
  save_fit,
  d = 1,
  standardise = TRUE,
  sort_eigenvalues = TRUE,
  store_projection = NULL
)

Arguments

Details

For a dual-space SAVE fit, estimation is restricted to the observed data span. This removes non-identifiable null-space directions that arise when p > n.

Value

A list containing SDR basis vectors, eigenvalues, optional projection matrix, preprocessing metadata, and diagnostics.

Fit reduced Cox proportional hazards model

Description

Fits a Cox proportional hazards model using sufficient dimension reduction scores obtained from SDR basis vectors.

Usage

fit_reduced_cox(
  X,
  time,
  status,
  basis_fit,
  ties = "efron",
  x = TRUE,
  y = TRUE,
  model = TRUE
)

Arguments

Value

A list containing the reduced predictors, fitted Cox model, coefficients, linear predictors, likelihood statistics, and diagnostics.

Fit sparse survival dimension reduction model

Description

Fits the current end-to-end ssurvdr workflow: survival-informed slicing, covariance-stabilised survival SAVE kernel construction, SDR basis extraction, reduced Cox proportional hazards modelling, and information-criterion scoring.

Usage

fit_ssurvdr(
  X,
  time,
  status,
  d = 1,
  n_slices = 5,
  min_slice_size = 2,
  covariance = c("mec", "sample"),
  rho = 0.5,
  alpha = 0.05,
  eps = 1e-08,
  centre = TRUE,
  scale = FALSE,
  ties = "efron",
  backend = c("auto", "primal", "dual")
)

Arguments

Value

A list containing slicing output, SAVE kernel, SDR basis, reduced Cox model, information criteria, and diagnostics.

Examples

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

Fit ssurvdr model over a tuning grid

Description

Fits the ssurvdr workflow over candidate covariance estimators and structural dimensions, then returns model-comparison results using AIC, BIC, CAIC, ICOMP(IFIM), ICOMP(misspec), C-index, runtime, and condition number.

Usage

fit_ssurvdr_grid(
  X,
  time,
  status,
  d_grid = 1:3,
  covariance_grid = c("mec", "sample"),
  n_slices = 5,
  min_slice_size = 2,
  rho = 0.5,
  alpha = 0.05,
  eps = 1e-08,
  centre = TRUE,
  scale = FALSE,
  ties = "efron",
  selector = c("ICOMP_IFIM", "ICOMP_MISSPEC", "AIC", "BIC", "CAIC"),
  continue_on_error = TRUE,
  backend = c("auto", "primal", "dual"),
  reuse_kernel = TRUE
)

Arguments

Details

The survival slices and SAVE operator are computed once per covariance estimator and reused across structural dimensions, avoiding redundant kernel construction and eigendecomposition.

Value

A list containing all fitted models, a model-comparison table, the best model, cached SAVE operators, and diagnostics.

Fit an ICOMP-guided row-sparsity path

Description

Evaluates a sequence of row-sparse SDR bases obtained from a previously fitted dense SDR basis. Predictor rows are ranked by their Euclidean row norms, the leading k rows are retained for each candidate support size, and the retained basis is re-orthonormalised before refitting the reduced Cox model.

Usage

fit_ssurvdr_sparse_path(
  X,
  time,
  status,
  dense_fit,
  support_grid = NULL,
  selector = c("ICOMP_IFIM", "ICOMP_MISSPEC", "AIC", "BIC", "CAIC"),
  ties = "efron",
  eps = 1e-08,
  variable_names = NULL,
  rank_tol = 1e-10,
  continue_on_error = TRUE,
  store_fits = FALSE
)

Arguments

Details

Model comparison uses the existing Cox-model criteria returned by compute_icomp(). In particular, ICOMP_IFIM and ICOMP_MISSPEC remain the package's established information-complexity criteria. No additional support- size penalty is silently added. Consequently, the returned path should be inspected for boundary selection and compared with the dense-support candidate.

This procedure is a post-estimation row-sparsification path. It is not a group-lasso, Fantope, or penalised SAVE estimator.

Value

An object of class ssurvdr_sparse_path containing the support-path results, selected support, best sparse support excluding the fully dense candidate, and the selected fitted model.

Maximum Entropy Covariance Estimator

Description

Computes the Maximum Entropy Covariance Estimator (MEC) from a predictor matrix and survival-informed slice membership. The estimator combines a pooled within-slice covariance matrix with the entropy-maximising slice covariance matrix, with optional diagonal stabilisation.

Usage

mec_covariance(
  X,
  slices,
  rho = 0.5,
  alpha = 0.05,
  eps = 1e-08,
  centre = TRUE,
  scale = FALSE
)

Arguments

Value

A list containing the MEC covariance matrix, pooled covariance matrix, entropy-selected covariance matrix, entropy scores, selected slice, and diagnostic quantities.

Run Monte Carlo simulation for ssurvdr

Description

Runs repeated simulation experiments for sparse survival dimension reduction under low-dimensional, borderline-dimensional, high-dimensional, and ultra-high-dimensional settings.

Usage

run_ssurvdr_simulation(
  n = 120,
  p = 30,
  active = 1:5,
  covariance = c("identity", "ar1", "compound", "block"),
  rho = 0.5,
  signal_strength = 1.2,
  censoring_rate = 0.08,
  n_rep = 50,
  screening = TRUE,
  screen_top = NULL,
  covariance_methods = c("mec", "sample"),
  d_grid = 1:3,
  n_slices = 4,
  selector = c("ICOMP_IFIM", "ICOMP_MISSPEC", "AIC", "BIC", "CAIC"),
  variable_top = NULL,
  seed = NULL,
  continue_on_error = TRUE
)

Arguments

Value

A list containing replication-level results, summary results, and simulation settings.

Compute survival SAVE kernel matrix

Description

Computes the covariance-stabilised survival SAVE operator for sufficient dimension reduction in censored survival data. For very high-dimensional problems, a compact dual-space backend avoids materialising dense p x p covariance and SAVE matrices and restricts estimation to the observed data span.

Usage

save_kernel(
  X,
  slices,
  covariance = c("sample", "mec"),
  rho = 0.5,
  alpha = 0.05,
  eps = 1e-08,
  centre = TRUE,
  scale = FALSE,
  backend = c("auto", "primal", "dual")
)

Arguments

Value

A list containing the SAVE representation, eigensystem, preprocessing metadata, and diagnostics.

Screen variables using univariate Cox models

Description

Performs preliminary survival variable screening using univariate Cox proportional hazards models. This function is intended as a high-dimensional screening layer before survival SAVE, MEC covariance estimation, and sparse SDR modelling.

Usage

screen_survival_variables(
  X,
  time,
  status,
  top = NULL,
  p_value = NULL,
  min_events = 5,
  ties = "efron"
)

Arguments

Value

A list containing screening scores, selected variables, selected indices, and diagnostics.

Select variables from SDR basis

Description

Selects variables using row norms of the estimated sufficient dimension reduction basis matrix.

Usage

select_variables(
  basis_fit,
  threshold = 1e-06,
  top = NULL,
  variable_names = NULL
)

Arguments

Value

A list containing variable scores, selected variables, selected indices, and selection diagnostics.

Simulate high-dimensional censored survival data for ssurvdr

Description

Simulates predictors, sparse true SDR direction, event times, censoring times, observed survival times, and censoring indicators for testing sparse survival dimension reduction methods.

Usage

simulate_ssurvdr(
  n = 100,
  p = 50,
  active = 1:5,
  beta = NULL,
  covariance = c("identity", "ar1", "compound", "block"),
  rho = 0.5,
  block_size = 10,
  signal_strength = 1,
  baseline_hazard = 0.1,
  censoring_rate = 0.1,
  standardise_X = TRUE,
  seed = NULL
)

Arguments

Value

A list containing simulated predictors, survival outcome, true basis, true active set, covariance matrix, and simulation metadata.

Sparse projection from SAVE kernel

Description

Computes sparse sufficient dimension reduction directions from a SAVE kernel using weighted soft-thresholding followed by eigendecomposition.

Usage

sparse_projection(
  save_fit,
  d = 1,
  lambda = 0.01,
  weights = NULL,
  threshold = 1e-06,
  standardise = TRUE
)

Arguments

Details

This function thresholds entries of a primal SAVE kernel before extracting directions. It requires a dense primal kernel. For a row-support path using a fitted primal or dual basis, use fit_ssurvdr_sparse_path().

Value

A list containing sparse basis, sparse projection matrix, selected variables, thresholded kernel, and diagnostics.

Row-sparsify an SDR basis

Description

Constructs a row-sparse sufficient dimension reduction basis by retaining the predictors with the largest row norms from an existing SDR basis. The retained columns are re-orthonormalised without altering the exact zero pattern of the excluded predictor rows.

Usage

sparsify_sdr_basis(
  basis_fit,
  support_size,
  variable_names = NULL,
  rank_tol = 1e-10
)

Arguments

Details

This function is deliberately a post-estimation row-sparsification operator. It does not claim to solve a penalised SAVE optimisation problem.

Value

An object inheriting from ssurvdr_sparse_basis and ssurvdr_basis. It contains the row-sparse orthonormal basis, selected predictor indices, row-norm ranking, retained basis energy, and subspace diagnostics.

Summarise ssurvdr model output

Description

Produces clean summary tables from objects returned by fit_ssurvdr(), fit_ssurvdr_grid(), and select_variables().

Usage

summarise_ssurvdr(object, top = 10)

Arguments

Value

A list of summary tables.

Construct survival-informed slices for censored survival data

Description

This function partitions survival observations by censoring status and then slices observed survival times within each censoring group. It is intended for sufficient dimension reduction methods for censored survival data.

Usage

survival_slicing(
  time,
  status,
  n_slices = 5,
  min_slice_size = 2,
  drop_empty = TRUE
)

Arguments

Value

A list with the slice membership, slice labels, slice table, and basic metadata.