wridge_solver: Fit B-Splines with weighted penalization over differences of...

In aspline: Spline Regression with Adaptive Knot Selection

Fit B-Splines with weighted penalization over differences of parameters

Description

Fit B-Splines with weighted penalization over differences of parameters

Usage

wridge_solver(
  XX_band,
  Xy,
  degree,
  pen,
  w = rep(1, nrow(XX_band) - degree - 1),
  old_par = rep(1, nrow(XX_band)),
  maxiter = 1000,
  tol = 1e-08
)

Arguments

XX_band	The matrix X^T X where X is the design matrix. This argument is given in the form of a band matrix, i.e., successive columns represent superdiagonals.
Xy	The vector of currently estimated points X^T y, where y is the y-coordinate of the data.
degree	The degree of the B-splines.
pen	Positive penalty constant.
w	Vector of weights. The case \mathbf w = \mathbf 1 corresponds to fitting P-splines with difference #' order degree + 1 (see Eilers, P., Marx, B. (1996) Flexible smoothing with B-splines and penalties.)
old_par	Initial parameter to serve as starting point of the iterating process.
maxiter	Maximum number of Newton-Raphson iterations to be computed.
tol	The tolerance chosen to diagnostic convergence of the adaptive ridge procedure.

Value

The estimated parameter of the spline regression.

aspline documentation built on June 9, 2022, 9:05 a.m.