Returns the log-likelihood value of a km object.

```
# S4 method for km
logLik(object, ...)
```

object

an object of class `km`

containing the trend and covariance structures.

...

no other argument for this method.

The log likelihood value.

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.