kspmControl: Control various aspects of the optimisation problem

In KSPM: Kernel Semi-Parametric Models

Description

Allow the user to set some characteristics of the optimisation algorithm

Usage

kspmControl(interval.upper = NA, interval.lower = NA, trace = FALSE,
  optimize.tol = .Machine$double.eps^0.25, NP = NA, itermax = 500,
  CR = 0.5, F = 0.8, initialpop = NULL, storepopfrom = itermax + 1,
  storepopfreq = 1, p = 0.2, c = 0,
  reltol = sqrt(.Machine$double.eps), steptol = itermax,
  parallel = FALSE)

Arguments

interval.upper	integer or vetor of initial maximum value(s) allowed for parameter(s)
interval.lower	integer or vetor of initial maximum value(s) allowed for parameter(s)
trace	boolean. If TRUE parameters value at each iteration are displayed.
optimize.tol	if optimize function is used. See optimize
NP	if DEoptim function is used. See DEoptim.control
itermax	if DEoptim function is used. See DEoptim.control
CR	if DEoptim function is used. See DEoptim.control
F	if DEoptim function is used. See DEoptim.control
initialpop	if DEoptim function is used. See DEoptim.control
storepopfrom	if DEoptim function is used. See DEoptim.control
storepopfreq	if DEoptim function is used. See DEoptim.control
p	if DEoptim function is used. See DEoptim.control
c	if DEoptim function is used. See DEoptim.control
reltol	if DEoptim function is used. See DEoptim.control
steptol	if DEoptim function is used. See DEoptim.control
parallel	if DEoptim function is used. See DEoptim.control

Details

When only one hyperparameter should be estimated, the optimisation problem calls the optimize function from stats basic package. Otherwise, it calls the DEoptim function from the package DEoptim. In both case, the parameters are choosen among the initial interval defined by interval.lower and interval.upper.

Value

search.parameters is an iterative algorithm estimating model parameters and returns the following components:

lambda	tuning parameters for penalization.
beta	vector of coefficients associated with linear part of the model, the size being the number of variable in linear part (including an intercept term).
alpha	vector of coefficients associated with kernel part of the model, the size being the sample size.
Ginv	a matrix used in several calculations. Ginv = (lambda I + K)^(-1).

Author(s)

Catherine Schramm, Aurelie Labbe, Celia Greenwood

See Also

link get.parameters for computation of parameters at each iteration

KSPM documentation built on Aug. 10, 2020, 5:07 p.m.