R: Fitting Kriging Models

kmNuggets.init {DiceKriging} | R Documentation

## Fitting Kriging Models

### 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`

[Package DiceKriging version 1.5.8 Index]