kmNuggets.init: Fitting Kriging Models

In DiceKriging: Kriging Methods for Computer Experiments

Description

kmNuggets.init is used to give initial values to fit kriging models, in presence of noisy observations.

Usage

kmNuggets.init(model)

Arguments

model

an object of class km.

Details

The procedure can be summarized in 4 stages:

1)	Compute the variogram and give a first estimation of the process variance, as well as lower and upper bounds.
2)	Simulate several values for the process variance, around the estimation obtained at stage 1). The number of simulations is the one given in model@control$pop.size.
3)	If no initial value is provided for the other covariance parameters, simulate them uniformly inside the domain delimited by model@lower and model@upper. The number of simulations is the same as in stage 2).
4)	Compute the likelihood at each simulated "point" (variance + other covariance parameters), and take the best one(s). This(these) point(s) gives the first initial value(s). The number of values considered can be set by the argument multistart in km.

Value

par	a matrix whose rows contain initial vectors of parameters.
value	a vector containing the function values corresponding to par.
cov	a list containing the covariance objects corresponding to par.
lower	,
upper	vectors containing lower and upper bounds for parameters.

Author(s)

O. Roustant, David Ginsbourger, Ecole des Mines de St-Etienne.

See Also

km, kmEstimate

DiceKriging documentation built on Feb. 24, 2021, 1:07 a.m.