# logLikFun

##### Concentrated log-likelihood of a km object

Returns the concentrated log-likelihood, obtained from the likelihood by plugging in the estimators of the parameters that can be expressed in function of the other ones.

##### Usage

`logLikFun(param, model, envir=NULL)`

##### Arguments

- param
a vector containing the optimization variables.

- model
an object of class

`km`

.- envir
an optional environment specifying where to assign intermediate values for future gradient calculations. Default is NULL.

##### Details

When there is no nugget effect nor observation noise, the concentrated log-likelihood is obtained by plugging in the variance and the trend MLE. Maximizing the likelihood is then equivalent to maximizing the concentrated log-likelihood with respect to the covariance parameters. In the other cases, the maximization of the concentrated log-likelihood also involves other parameters (the variance explained by the stationary part of the process for noisy observations, and this variance divided by the total variance if there is an unknown homogeneous nugget effect).

##### Value

The concentrated log-likelihood value.

##### References

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.

##### See Also

*Documentation reproduced from package DiceKriging, version 1.5.6, License: GPL-2 | GPL-3*