Stability Selection on Pairwise Log-Ratios — codaFS_stability

Applies subsampling-based stability selection to pairwise log-ratio features using elastic-net logistic models. Stable ratios are aggregated back to individual features by their incidence-weighted selection frequency.

Usage

codaFS_stability_logratio(
  X,
  y,
  n_features = NULL,
  positive = NULL,
  input_scale = c("log", "raw"),
  alpha = 0.9,
  subsample_frac = 0.7,
  n_subsamples = 20L,
  nfolds = 3L,
  nlambda = 60L,
  lambda_rule = c("lambda.1se", "lambda.min"),
  selection_threshold = 0.6,
  max_pair_features = 60L,
  prescreen_metric = c("clr_effect", "variance")
)

Arguments

X: Numeric matrix with samples in rows and features in columns.
y: Binary outcome vector.
n_features: Optional target panel size.
positive: Optional positive-class label for non-numeric outcomes.
input_scale: Either `"log"` (default) or `"raw"`.
alpha: Elastic-net mixing parameter.
subsample_frac: Fraction of samples used in each stability iteration.
n_subsamples: Number of stability iterations.
nfolds: Number of cross-validation folds within each subsample.
nlambda: Number of lambda values used by `cv.glmnet`.
lambda_rule: Either `"lambda.1se"` (default) or `"lambda.min"`.
selection_threshold: Frequency threshold used to define stable ratios.
max_pair_features: Maximum number of prescreened features retained before constructing pairwise ratios.
prescreen_metric: Either `"clr_effect"` or `"variance"`.

Value

A list with per-feature `scores`, stable pairwise log-ratio frequencies, and `selected_features`.