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Conditional Logistic Regression with Individual-level External Data and Elastic Net Penalty (CLR-Indi-ENet)

ncc_indi_enet.Rd

Fits a series of penalized Conditional Logistic Regression models for matched case-control data that integrate external individual-level data via a composite likelihood weight etas, while applying an Elastic Net penalty for variable selection and regularization in high-dimensional settings.

Usage

ncc_indi_enet(
  y_int,
  z_int,
  stratum_int,
  y_ext,
  z_ext,
  stratum_ext,
  etas,
  alpha = 1,
  lambda = NULL,
  nlambda = 100,
  lambda.min.ratio = NULL,
  lambda.early.stop = FALSE,
  tol = 1e-04,
  Mstop = 1000,
  max.total.iter = (Mstop * nlambda),
  group = NULL,
  group.multiplier = NULL,
  standardize = TRUE,
  nvar.max = NULL,
  group.max = NULL,
  stop.loss.ratio = 0.01,
  actSet = TRUE,
  actIter = Mstop,
  actGroupNum = NULL,
  actSetRemove = FALSE,
  returnX = FALSE,
  trace.lambda = FALSE,
  message = FALSE,
  ...
)

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 nonnegative external weights. eta = 0 gives internal-only fit.

alpha

The Elastic Net mixing parameter, with \(0 < \alpha \le 1\). alpha = 1 is Lasso; alpha close to 0 approaches Ridge. Default 1.

lambda

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

nlambda

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

lambda.min.ratio

Numeric. Ratio of smallest to largest lambda. Default NULL (determined automatically based on sample size vs. number of covariates).

lambda.early.stop

Logical. If TRUE, stops the lambda path early if the loss improvement is small. Default FALSE.

tol

Convergence tolerance. Default 1e-4.

Mstop

Maximum coordinate descent iterations per lambda. Default 1000.

max.total.iter

Maximum total iterations across the entire lambda path. Default Mstop * nlambda.

group

Integer vector defining group membership for grouped penalties. Default treats each variable as its own group.

group.multiplier

Numeric vector of multiplicative factors for group penalties.

standardize

Logical. If TRUE, z is standardized internally. Coefficients are returned on the original scale. Default TRUE.

nvar.max

Integer. Maximum number of active variables. Default ncol(z_int).

group.max

Integer. Maximum number of active groups.

stop.loss.ratio

Numeric. Threshold for early stopping. Default 1e-2.

actSet

Logical. If TRUE, uses active-set strategy. Default TRUE.

actIter

Integer. Iterations for active set refinement. Default Mstop.

actGroupNum

Integer. Limit on active groups.

actSetRemove

Logical. Whether to allow removal from active set. Default FALSE.

returnX

Logical. If TRUE, returns the standardized design matrix. Default FALSE.

trace.lambda

Logical. If TRUE, prints the lambda sequence progress. Default FALSE.

message

Logical. If TRUE, shows a progress bar. Default FALSE.

...

Additional arguments passed to cox_indi_enet.

Value

An object of class "ncc_indi_enet" and "cox_indi_enet". See cox_indi_enet for a description of the return components.

Details

This function maps the CLR problem to a Cox PH model with fixed event time \(T=1\) and \(\delta=y\) for both internal and external datasets, then calls cox_indi_enet as the core engine.

  • If alpha = 1, the penalty is Lasso.

  • If alpha is close to 0, the penalty approaches Ridge.

  • If eta = 0, external data is ignored and the model reduces to a standard Elastic Net CLR on internal data only.

See also

cox_indi_enet, ncc_indi