Log-Contrast Lasso on CLR Features — codaFS_logcontrast

Fits a lasso or elastic-net logistic model on centered log-ratio (CLR) features, then projects the coefficient vector onto the zero-sum hyperplane to obtain a valid log-contrast representation. Feature importance is the absolute projected coefficient magnitude.

Usage

codaFS_logcontrast_lasso(
  X,
  y,
  n_features = NULL,
  positive = NULL,
  input_scale = c("log", "raw"),
  alpha = 1,
  nfolds = 5L,
  nlambda = 100L,
  lambda_rule = c("lambda.1se", "lambda.min")
)

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 passed to `glmnet`.
nfolds: Number of cross-validation folds used by `cv.glmnet`.
nlambda: Number of lambda values used by `cv.glmnet`.
lambda_rule: Either `"lambda.1se"` (default) or `"lambda.min"`.

Value

A list with per-feature `scores`, the raw and zero-sum projected coefficients, and `selected_features`.