Description Usage Arguments Details Value Author(s) References
Predicts value and confidence interval at new inputs using Gaussian Process Emulation.
This function should be preceded by the fitEmulator
function.
1 2 3 
object 
A fit object of class inheriting from 
newdata 
A data matrix of input(s) at which emulation is desired (new inputs).
Must contain at least all parameters given in 
var.cov 
Optionally calculates posterior variance covariance matrix. Default is set to FALSE. For large numbers of training and prediction data, this is quite time consuming. 
sd 
Optionally calculates only the posterior standard deviation. Default is set to 
tol 
The tolerance for capping negative small values of posterior standard deviation to zero. The default is 10^11. 
... 
Further arguments not used and an error is thrown if provided. 
Note that when using the LMC method, calculating the posterior variance is quite timeconsuming.
The function returns a list containting the following components:
posterior.mean  Approximation of the outputs for the given inputs in newdata 
posterior.variance  Variance covariance matrix around this approximation 
standard.deviation  Standard Deviation of the approximation. It equals the squareroot
of the diagonal of the posterior.variance

When the number of outputs to emulate is more than 1, method = 'separable'
, and object is of class "emulatorFit"
two extra values are returned from this function. These are
correlation.Matrix  A spatial correlation matrix. 
sigmahat  A between outputs covariance matrix. 
Originally written by Jeremy Oakley. Modified by Sajni Malde.
Oakley, J. (1999). Bayesian uncertainty analysis for complex computer codes, Ph.D. thesis, University of Sheffield.
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