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Cross-Validated CLR with Individual-Level External Data and Elastic Net Penalty

cv.ncc_indi_enet.Rd

Performs K-fold cross-validation (CV) to jointly select the integration parameter eta and the Elastic Net penalty parameter lambda for Conditional Logistic Regression with individual-level external data integration and Elastic Net penalty, implemented via ncc_indi_enet.

This function is designed for 1:m matched case-control settings where each stratum (matched set) contains exactly one case and \(m\) controls.

Usage

cv.ncc_indi_enet(
  y_int,
  z_int,
  stratum_int,
  y_ext,
  z_ext,
  stratum_ext,
  etas = NULL,
  alpha = 1,
  lambda = NULL,
  nlambda = 100,
  lambda.min.ratio = NULL,
  nfolds = 5,
  criteria = c("loss", "AUC", "CIndex", "Brier"),
  message = FALSE,
  seed = NULL,
  ...
)

Arguments

y_int

Numeric vector of binary outcomes for the internal dataset (0 = control, 1 = case).

z_int

Numeric matrix of covariates for the internal dataset.

stratum_int

Numeric or factor vector defining the internal matched sets. Required.

y_ext

Numeric vector of binary outcomes for the external dataset (0 = control, 1 = case).

z_ext

Numeric matrix of covariates for the external dataset.

stratum_ext

Numeric or factor vector defining the external matched sets. Required.

etas

Numeric vector of candidate tuning values for \(\eta\). Required.

alpha

Elastic Net mixing parameter in \((0,1]\). Default 1 (Lasso).

lambda

Optional numeric vector of lambda values. If NULL, a lambda path is generated automatically for each eta.

nlambda

Integer. Number of lambda values to generate. Default 100.

lambda.min.ratio

Numeric in \((0,1)\). Ratio of minimum to maximum lambda. If NULL, set internally based on sample size vs. number of covariates.

nfolds

Number of cross-validation folds. Default 5.

criteria

Character string specifying the CV performance criterion. One of "loss" (default), "AUC", "CIndex", or "Brier".

message

Logical. If TRUE, prints progress messages. Default FALSE.

seed

Optional integer seed for reproducible fold assignment. Default NULL.

...

Additional arguments passed to ncc_indi_enet.

Value

A list of class "cv.ncc_indi_enet" containing:

best

A list with the global best \((\eta, \lambda)\): best_eta, best_lambda, best_beta, criteria.

integrated_stat.full_results

A data.frame with the CV score for every \((\eta, \lambda)\) combination.

integrated_stat.best_per_eta

A data.frame with the best lambda and score for each eta.

integrated_stat.betahat_best

Matrix of full-data coefficients at the best lambda for each eta.

criteria

The CV criterion used.

alpha

The Elastic Net mixing parameter.

nfolds

The number of folds used.

Details

Cross-validation is performed at the stratum level on the internal dataset: each matched set is treated as an indivisible unit and assigned to a single fold using get_fold_cc. The external dataset is used in full during every training fold.

For each candidate eta, a full lambda path is fit on the complete internal + external data, and then K-fold CV is used to evaluate each lambda along this path. The function performs a 2D search over \((\eta, \lambda)\).

The criteria argument controls the CV performance metric:

  • "loss": Average negative conditional log-likelihood on held-out strata (lower is better).

  • "AUC": Matched-set AUC based on within-stratum comparisons (higher is better).

  • "CIndex": Alias for "AUC" in the 1:m matched setting.

  • "Brier": Conditional Brier score based on within-stratum softmax probabilities (lower is better).

See also

ncc_indi_enet, cv.ncckl_enet