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 X, in the form of a two-element list. The first element is the mean (which should have name “mean”), and the second element is the variance matrix, which should be a positive definite matrix of the correct size, and have name “var” D1 Matrix whose rows are the code run points D2 Matrix whose rows are field observation points H1 Regression function for D1 H2 Regression function for D2 d Vector of code outputs and field observations include.prior Boolean; passed to function p.eqn8.supp() (qv) hbar.fun Function that gives expectation (with respect to X) of h1(x,theta) and h2(x) as per section 4.2 lower.theta Lower integration limit for theta (NB: a vector) upper.theta Lower integration limit for theta (NB: a vector) extractor Extractor function; see extractor.toy() for an example give.info Boolean, with default FALSE meaning to return just the answer and TRUE to return the answer along with all output from both integrations as performed by adaptIntegrate() phi Hyperparameters ... Extra arguments passed to the integration function. If multidimensional (ie length(theta)>1), then the arguments are passed to adaptIntegrate(); if one dimensional, they are passed to integrate()

### 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

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) 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)

### 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)


[Package calibrator version 1.2-8 Index]