Allow the user to set some characteristics of the optimisation algorithm
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)integer or vetor of initial maximum value(s) allowed for parameter(s)
integer or vetor of initial maximum value(s) allowed for parameter(s)
boolean. If TRUE parameters value at each iteration are displayed.
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
if DEoptim function is used. See DEoptim.control
search.parameters is an iterative algorithm estimating model parameters and returns the following components:
tuning parameters for penalization.
vector of coefficients associated with linear part of the model, the size being the number of variable in linear part (including an intercept term).
vector of coefficients associated with kernel part of the model, the size being the sample size.
a matrix used in several calculations. \(Ginv = (\lambda I + K)^{-1}\).
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.
link get.parameters for computation of parameters at each iteration