R: log-likelihood of a km object

logLik {DiceKriging} | R Documentation

## log-likelihood of a km object

### Description

Returns the log-likelihood value of a km object.

### Usage

    ## S4 method for signature 'km'
    logLik(object, ...)

### Arguments

`object` | an object of class `km` containing the trend and covariance structures.
`...` | no other argument for this method.

### Value

The log likelihood value.

### Author(s)

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

### References

N.A.C. Cressie (1993), Statistics for spatial data, Wiley series in probability and mathematical statistics.

D. Ginsbourger, D. Dupuy, A. Badea, O. Roustant, and L. Carraro (2009), A note on the choice and the estimation of kriging models for the analysis of deterministic computer experiments, Applied Stochastic Models for Business and Industry, 25 no. 2, 115-131.

R. Li and A. Sudjianto (2005), Analysis of Computer Experiments Using Penalized Likelihood in Gaussian Kriging Models, Technometrics, 47 no. 2, 111-120.

K.V. Mardia and R.J. Marshall (1984), Maximum likelihood estimation of models for residual covariance in spatial regression, Biometrika, 71, 135-146.

J.D. Martin and T.W. Simpson (2005), Use of kriging models to approximate deterministic computer models, AIAA Journal, 43 no. 4, 853-863.

J.-S. Park and J. Baek (2001), Efficient computation of maximum likelihood estimators in a spatial linear model with power exponential covariogram, Computer Geosciences, 27 no. 1, 1-7.

C.E. Rasmussen and C.K.I. Williams (2006), Gaussian Processes for Machine Learning, the MIT Press, http://www.GaussianProcess.org/gpml

J. Sacks, W.J. Welch, T.J. Mitchell, and H.P. Wynn (1989), Design and analysis of computer experiments, Statistical Science, 4, 409-435.

M.L. Stein (1999), Interpolation of spatial data, some theory for kriging, Springer.

### See Also

`km`, `logLikFun`

[Package DiceKriging version 1.5.5 Index]