W {calibrator} | R Documentation |
covariance matrix for beta
Description
Covariance matrix of beta given theta, phi, d
Usage
W(D1, D2, H1, H2, theta, det=FALSE, phi)
Arguments
D1 |
Matrix whose rows are code run points |
D2 |
Matrix whose rows are observation points |
H1 |
regression function |
H2 |
regression function |
theta |
parameters |
det |
Boolean, with default |
phi |
Hyperparameters |
Details
This function is defined between equations 2 and 3 of the
supplement. It is used in functions betahat.fun.koh()
,
p.eqn8.supp()
, and p.joint()
.
Returns
{\mathbf W} (\theta)=
\left(
{\mathbf H}(\theta)^T {\mathbf V}_d(\theta)^{-1} {\mathbf H}(\theta)
\right)^{-1}
If only the determinant is required, setting argument det
to
TRUE
is faster than using det(W(..., det=FALSE))
, as the
former avoids an unnecessary use of solve()
.
Author(s)
Robin K. S. Hankin
References
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.tonyohagan.co.uk/academic/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)
See Also
Examples
data(toys)
W(D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, theta=theta.toy, phi=phi.toy)