EK.eqn10.supp {calibrator}  R Documentation 
Posterior mean of K
Description
Estimates the posterior mean of K as per equation 10 of KOH2001S, section 4.2
Usage
EK.eqn10.supp(X.dist, D1, D2, H1, H2, d, hbar.fun,
lower.theta, upper.theta, extractor, give.info=FALSE,
include.prior=FALSE, phi, ...)
Arguments
X.dist 
Probability distribution of 
D1 
Matrix whose rows are the code run points 
D2 
Matrix whose rows are field observation points 
H1 
Regression function for 
H2 
Regression function for 
d 
Vector of code outputs and field observations 
include.prior 
Boolean; passed to function 
hbar.fun 
Function that gives expectation (with respect to 
lower.theta 
Lower integration limit for 
upper.theta 
Lower integration limit for 
extractor 
Extractor function; see 
give.info 
Boolean, with default 
phi 
Hyperparameters 
... 
Extra arguments passed to the integration
function. If multidimensional (ie 
Details
This function evaluates a numerical approximation to equation 10 of section 4.2 of the supplement.
Equation 10 integrates over the prior distribution of theta
. If
theta
is a vector, multidimensional integration is necessary.
In the case of multidimensional integration, function
adaptIntegrate()
is used.
In the case of one dimensional integration—theta being a
scalar—function integrate()
of the stats package is used.
Note that equation 10 is conditional on the observed data and the hyperparameters
Value
Returns a scalar
Note
The function was not reviewed by the Journal of Statistical Software.
The package formely used adapt package, but this is no longer available on CRAN. The package now uses the cubature package.
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) pp425464

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)
Examples
1+1
## Not run:
# Not run because it takes R CMD check too long
data(toys)
EK.eqn10.supp(X.dist=X.dist.toy, D1=D1.toy, D2=D2.toy,
H1=H1.toy, H2=H2.toy, d=d.toy,
hbar.fun=hbar.fun.toy, lower.theta=c(3,3,3),
upper.theta=c(3,3,3),extractor=extractor.toy,
phi=phi.toy)
## End(Not run)