mdash.fun {approximator}

Mean of Gaussian process

Description

Returns the mean of the Gaussian process conditional on the observations and the hyperparameters

Usage

mdash.fun(x, D1, subsets, hpa, Vinv = NULL, use.Vinv = TRUE, z, basis)

Arguments

x

Point at which mean is desired

D1

Code design matrix for level 1 code

subsets

subsets object

hpa

Hyperparameter object

Vinv

Inverse of the variance matrix; if NULL, the function will calculate it

use.Vinv

Boolean, with default TRUE meaning to use the inverse of V and FALSE meaning to use a method that does not involve inverting V

z

observations

basis

Basis functions

Author(s)

Robin K. S. Hankin

References

M. C. Kennedy and A. O'Hagan 2000. “Predicting the output from a complex computer code when fast approximations are available” Biometrika, 87(1): pp1-13

Examples

data(toyapps)
mdash.fun(x=1:3,D1=D1.toy,subsets=subsets.toy,hpa=hpa.toy,z=z.toy,basis=basis.toy)

uu <- rbind(1:3,1,3:1,1:3)
rownames(uu) <- c("first","second","third","fourth")

mdash.fun(x=uu,D1=D1.toy,subsets=subsets.toy,hpa=hpa.toy,z=z.toy,basis=basis.toy)