R: Fitting Kriging Models

km1Nugget.init {DiceKriging} | R Documentation

## Fitting Kriging Models

### Description

`km1Nugget.init` is used to give good initial values to fit kriging models when there is an unknown nugget effect to be estimated.

### Usage

    km1Nugget.init(model)

### Arguments

`model` | an object of class `km`.

### Details

The procedure can be summarized in 4 stages :

1) | Compute the variogram and deduce a first estimation of the total variance. If an initial value is provided for `nugget`, check its compatibility with the estimated variance. If not, use again the variogram to give a first estimation of the nugget effect.
2) | Simulate several values for the nugget effect and the process variance, around the estimations 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 + nugget effect + other covariance parameters), and take the best(s) 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]