emulator (version 1.2-21)

emulator-package: tools:::Rd_package_title("emulator")

Description

tools:::Rd_package_description("emulator")

Author

tools:::Rd_package_author("emulator")

Maintainer: tools:::Rd_package_maintainer("emulator")

Details

The DESCRIPTION file: tools:::Rd_package_DESCRIPTION("emulator") tools:::Rd_package_indices("emulator")

References

• J. Oakley 1999. “Bayesian uncertainty analysis for complex computer codes”, PhD thesis, University of Sheffield.

• R. K. S. Hankin 2005. “Introducing BACCO, an R bundle for Bayesian analysis of computer code output”, Journal of Statistical Software, 14(16)

Examples

## More detail given in optimal.scales.Rd
scales_true  <- c(1,1,1,1,1,4)

## and a real (linear) relation:
real.relation <- function(x){sum( (1:6)*x )}

## Now a design matrix:
val  <- latin.hypercube(100,6)

## apply the real relation:
d <- apply(val,1,real.relation)

## and add some suitably correlated Gaussian noise:
A <- corr.matrix(val,scales=scales_true)
d.noisy <-  as.vector(rmvnorm(n=1,mean=apply(val,1,real.relation), 0.3*A))

## Now try to predict the values at points x:

x <- latin.hypercube(20,6)
predicted <- int.qq(x,d.noisy,xold=val, Ainv=solve(A),pos.def.matrix=diag(scales_true))
observed <- apply(x,1,real.relation)

par(pty='s')
plot(predicted,observed,xlim=c(4,18),ylim=c(4,18))
abline(0,1)