WH_1d_fixed_lambda: Whittaker-Henderson Smoothing (Maximum Likelihood, fixed...
In WH: Enhanced Implementation of Whittaker-Henderson Smoothing

WH_1d_fixed_lambda

R Documentation

Whittaker-Henderson Smoothing (Maximum Likelihood, fixed lambda)

Description

Whittaker-Henderson Smoothing (Maximum Likelihood, fixed lambda)

Usage

WH_1d_fixed_lambda(
  d,
  ec,
  y,
  wt,
  lambda = 1000,
  q = 2,
  p,
  reg = FALSE,
  verbose = FALSE,
  accu_dev = 1e-12
)

Arguments

`d`	Vector of observed events
`ec`	Vector of central exposure
`y`	Vector of observations
`wt`	Optional vector of weights
`lambda`	Smoothing parameter
`q`	Order of penalization. Polynoms of degrees q - 1 are considered smooth and are therefore unpenalized
`p`	The number of eigenvectors to keep
`reg`	Should the regression framework be used ? Boolean. If `TRUE`, will stop after the first iteration.
`accu_dev`	Tolerance for the convergence of the optimization procedure

Value

An object of class "WH_1d" i.e. a list containing model fit, variance, residuals and degrees of freedom as well as diagnosis to asses the quality of the fit.

WH documentation built on Sept. 11, 2024, 9:12 p.m.