kmCok( formula = ~1, design, response, formula.rho = ~1, Z = NULL,
covtype = "matern5_2", coef.trend = NULL, coef.cov = NULL,
coef.var = NULL, nugget = NULL, nugget.estim = FALSE,
noise.var = NULL, estim.method="MLE", penalty = NULL,
optim.method = "BFGS", lower = NULL, upper = NULL, parinit = NULL,
control = NULL, gr = TRUE, iso = FALSE, scaling = FALSE, knots = NULL)"formula") specifying the linear trends of the adjustment coefficients. This formula should concern only the input variables, and not the output (response). If there is any, it is automatically dropped. The default is ~1, which defines a constant trend.
km
kmkmkmkm km km km km km km kmkmkm kmkm km km kmkmkm "kmCok" (see kmCok-class).
MATHERON, G. (1969), Le krigeage universel, Les Cahiers du Centre de Morphologie Mathematique de Fontainebleau, 1.
RASMUSSEN, C.E. and WILLIAMS, C.K.I. (2006), Gaussian Processes for Machine Learning, the MIT Press, http://www.GaussianProcess.org/gpml
SANTNER, T.J., WILLIAMS, B.J. and NOTZ, W.I. (2003), The Design and Analysis of Computer Experiments, New York: Springer.
STEIN, L.M. (1999), Interpolation of Spatial Data, Springer Series in Statistics.
LE GRATIET, L. & GARNIER, J. (2012), Recursive co-kriging model for Design of Computer Experiments with multiple levels of fidelity, arXiv:1210.0686
LE GRATIET, L. (2012), Bayesian analysis of hierarchical multi-fidelity codes, arXiv:1112.5389
predict,kmCok-method