beta2hat.fun {calibrator} | R Documentation |
estimates beta2 as per the equation of page 4 of the supplement. Used
by p.page4()
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
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)