cov.p5.supp {calibrator} | R Documentation |
Covariance function for posterior distribution of z(.) conditional on estimated hyperparameters and calibration parameters theta.
cov.p5.supp(x, xdash, theta, d, D1, D2, H1, H2, phi)
x |
first point, or a matrix whose rows are the points of interest |
xdash |
second point, or a matrix whose rows are the points of interest |
theta |
Parameters |
d |
Observed values |
D1 |
Code run design matrix |
D2 |
Observation points of real process |
H1 |
Basis function for D1 |
H2 |
Basis function for D2 |
phi |
Hyperparameters |
Evaluates the covariance function: the last formula on page 5 of the supplement
Returns a matrix of covariances
May return the transpose of the desired object
Robin K. S. Hankin
M. C. Kennedy and A. O'Hagan 2001. “Bayesian calibration of computer models”. Journal of the Royal Statistical Society B, 63(3) pp425-464
M. C. Kennedy and A. O'Hagan 2001. “Supplementary details on Bayesian calibration of computer models”, Internal report, University of Sheffield. Available at http://www.shef.ac.uk/~st1ao/ps/calsup.ps
R. K. S. Hankin 2005. “Introducing BACCO, an R bundle for Bayesian analysis of computer code output”, Journal of Statistical Software, 14(16)
data(toys) x <- rbind(x.toy,x.toy+1,x.toy,x.toy,x.toy) rownames(x) <- letters[1:5] xdash <- rbind(x*2,x.toy) rownames(xdash) <- LETTERS[1:6] cov.p5.supp(x=x,xdash=xdash,theta=theta.toy,d=d.toy,D1=D1.toy,D2=D2.toy,H1=H1.toy,H2=H2.toy,phi=phi.toy) phi.true <- phi.true.toy(phi=phi.toy) cov.p5.supp(x=x,xdash=xdash,theta=theta.toy,d=d.toy,D1=D1.toy,D2=D2.toy,H1=H1.toy,H2=H2.toy,phi=phi.true)