beta2hat.fun {calibrator}R Documentation

estimator for beta2

Description

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

Usage

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

Arguments

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

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.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)

See Also

W2

Examples

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) 


[Package calibrator version 1.0-37 Index]