Fast Weighted Logistic Regression, Estimate Only (C++ Backend)

Fits the same logistic regression model as fast_logistic_regression_cpp (see that page for the full model, log-odds-ratio interpretation, and optimizer contract), with each observation's contribution to the log-likelihood and IRLS working weights multiplied by a row weight weights[i]. Setting all weights to 1 recovers fast_logistic_regression_cpp exactly; this is the backend used when the logistic model must be fit on bootstrap-reweighted or otherwise weighted data.

Usage

fast_logistic_regression_weighted_cpp(
  X,
  y,
  weights,
  warm_start_beta = NULL,
  smart_cold_start = FALSE,
  maxit = 100L,
  tol = 1e-8,
  fixed_idx = NULL,
  fixed_values = NULL,
  optimization_alg = "irls",
  warm_start_weights = NULL,
  warm_start_fisher_info = NULL
)

Arguments

X: A numeric matrix of predictor variables. It is assumed that an intercept column (e.g., a column of ones) is already included in X if desired.
y: A numeric vector of the response variable, expected to be binary (0 or 1).
weights: A numeric vector of weights for each observation.
warm_start_beta: Optional starting values for coefficients \(\beta\). If provided, smart_cold_start is ignored.
smart_cold_start: Logical. If TRUE and no warm_start_beta is supplied, use an OLS-based initial guess rather than a zero cold start.
maxit: Maximum number of iterations for the IRLS algorithm. Defaults to 100.
tol: Convergence tolerance. Defaults to 1e-8.
fixed_idx: Optional integer indices of coefficients to hold fixed rather than estimate.
fixed_values: Optional values to fix the parameters named by fixed_idx at.
optimization_alg: Optimization algorithm: "irls" (default), "lbfgs", or "newton_raphson"; see .normalize_optimizer_algorithm.
warm_start_weights: Optional initial IRLS working weights for the first iteration.
warm_start_fisher_info: Optional initial Fisher Information matrix to warm-start curvature information.

Value

A list containing the following components:

b: A numeric vector of the estimated logistic regression coefficients \(\hat\beta\).
mu: The fitted probabilities \(\hat\mu_i\).
XtWX, fisher_information: Two aliases for the same working-weights curvature matrix \(X^\top W X\) at the final iteration.
score: The (weighted) score vector at the fitted coefficients.
neg_ll: The weighted negative log-likelihood at the fitted coefficients.
converged: A logical value indicating whether the final gradient norm was below tol (gradient_norm < tol); uniform across the "irls"/"lbfgs" optimizers.
num_iter: The number of optimizer iterations performed.
hit_iteration_cap: A logical value, mutually exclusive with converged: TRUE iff the optimizer exhausted maxit iterations without meeting the gradient-norm convergence criterion.
gradient_norm: The norm of the score vector at the returned coefficients.

Usage

Arguments

Value

See also