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

Description Usage Arguments Details Value Warning Author(s) References See Also

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.

1
2
3

	## S4 method for signature 'kmCok'
predict(	object, newdata, newZ, type, 
		se.compute = TRUE, cov.compute = FALSE, checkNames = FALSE, ...)

`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`

see km

Olivier Roustant, David Ginsbourger, Ecole des Mines de St-Etienne.

Loic Le Gratiet, Universite Paris VII Denis-Diderot

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

kmCok, predict.MuFicokm

MuFiCokriging documentation built on May 2, 2019, 3:33 p.m.