WH (version 1.1.2)

WH_1d_fixed_lambda: 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
)

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.

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