kmNuggets.init is used to give initial values to fit kriging models, in presence of noisy observations.
kmNuggets.init(model)
a matrix whose rows contain initial vectors of parameters.
a vector containing the function values corresponding to par.
a list containing the covariance objects corresponding to par.
,
vectors containing lower and upper bounds for parameters.
an object of class km.
O. Roustant, David Ginsbourger, Ecole des Mines de St-Etienne.
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. |
km, kmEstimate