MuFiCokriging (version 1.2)

predict.kmCok: Kriging predictions and confidence intervals used in Multi-Fidelity Cokriging models

Description

An internal function wich provides predicted values and conditional variances based on a kmCok model. 95% confidence intervals are given based on Gaussian process assumption. This might be abusive in particular in the case where the number of observations is small.

Usage

"predict"(	object, newdata, newZ, type, 
		se.compute = TRUE, cov.compute = FALSE, checkNames = FALSE, ...)

Arguments

newZ

a vector giving the predictions of the level $k=1,...,(nlevel-1)$ at points newdata.

object

see km

newdata

see km

type

see km

se.compute

see km

cov.compute

see km

checkNames

see km

...

see km

Value

see km

Warning

see km

Details

see km

References

KRIGE, D.G. (1951), A statistical approach to some basic mine valuation problems on the witwatersrand, J. of the Chem., Metal. and Mining Soc. of South Africa, 52 no. 6, 119-139.

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

See Also

kmCok, predict.MuFicokm