Fit sparse SDR directions over a lambda grid — fit_sparse_grid • risdr

Accepts either an explicit dense direction matrix or a historical kernel object containing directions or vectors.

Usage

fit_sparse_grid(
  kernel_obj = NULL,
  directions = NULL,
  d = 2L,
  lambda_grid = seq(0, 0.5, length.out = 25L),
  penalty_weights = NULL,
  adapt_weights = FALSE,
  Sigma = NULL,
  weight_mode = c("c1f", "inverse_c1f", "uniform"),
  tol = 1e-10
)

Arguments

kernel_obj: Optional historical kernel object.
directions: Optional dense direction matrix.
d: Structural dimension.
lambda_grid: Numeric vector of tuning values.
penalty_weights: Optional user-supplied penalty weights.
adapt_weights: Logical; derive weights from Sigma when TRUE.
Sigma: Optional covariance matrix.
weight_mode: Adaptive weighting mode.
tol: Numerical tolerance.

Value

Sparse-grid object.