R: Control various aspects of the optimisation problem

kspmControl {KSPM}

R Documentation

Control various aspects of the optimisation problem

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