beta2hat.fun: estimator for beta2
In calibrator: Bayesian Calibration of Complex Computer Codes

Description Usage Arguments Author(s) References See Also Examples

estimates beta2 as per the equation of page 4 of the supplement. Used by p.page4()

1 2	beta2hat.fun(D1, D2, H1, H2, V, z, etahat.d2, extractor, E.theta, Edash.theta, phi)

`D1`	Matrix of code run points
`D2`	Matrix of observation points
`H1`	regression basis functions
`H2`	regression basis functions
`V`	overall covariance matrix
`z`	vector of observations
`etahat.d2`	expectation as per `etahat.vector`
`extractor`	extractor function
`E.theta`	Expectation
`Edash.theta`	Expectation wrt thetadash
`phi`	hyperparameters

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.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)

W2

data(toys)

etahat.d2 <- etahat(D1=D1.toy, D2=D2.toy, H1=H1.toy, y=y.toy,
E.theta=E.theta.toy, extractor=extractor.toy, phi=phi.toy)

beta2hat.fun(D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, V=NULL,
z=z.toy, etahat.d2=etahat.d2, extractor=extractor.toy,
E.theta=E.theta.toy, Edash.theta=Edash.theta.toy, phi=phi.toy)

jj <- create.new.toy.datasets(D1.toy , D2.toy)
phi.true <- phi.true.toy(phi=phi.toy)
y.toy <- jj$y.toy
z.toy <- jj$z.toy
d.toy <- jj$d.toy

etahat.d2 <- etahat(D1=D1.toy, D2=D2.toy, H1=H1.toy, y=y.toy,
E.theta=E.theta.toy, extractor=extractor.toy, phi=phi.toy)

beta2hat <- beta2hat.fun(D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, V=NULL,
z=z.toy, etahat.d2=etahat.d2, extractor=extractor.toy,
E.theta=E.theta.toy, Edash.theta=Edash.theta.toy,
phi=phi.toy)

print(beta2hat)

plot(z.toy , H2.toy(D2.toy) %*% beta2hat)