beta1hat.fun {calibrator} | R Documentation |
Least squares estimator for beta1
beta1hat.fun(D1, H1, y, phi)
D1 |
code run points |
H1 |
regressor basis funs |
y |
code outputs |
phi |
hyperparameters |
Robin K. S. Hankin
data(toys) y.toy <- create.new.toy.datasets(D1=D1.toy , D2=D2.toy)$y.toy beta1hat.fun(D1=D1.toy, H1=H1.toy, y=y.toy, phi=phi.toy) # now cheat: force the hyperparameters to have the correct psi1: phi.fix <- phi.change(old.phi=phi.toy,psi1=c(1, 0.5, 1.0, 1.0, 0.5, 0.4),phi.fun=phi.fun.toy) # The value for psi1 is obtained by cheating and #examining the source # code for computer.model(); see ?phi.change # Create a new toy dataset with 40 observations: D1.big <- latin.hypercube(40,5) jj <- create.new.toy.datasets(D1=D1.big , D2=D2.toy) # We know that the real coefficients are 4:9 because we # we can cheat and look at the source code for computer.model() # Now estimate the coefficients without cheating: beta1hat.fun(D1=D1.big, H1=H1.toy, jj$y, phi=phi.toy) # Not bad! # We can do slightly better by cheating and using the # correct value for the hyperparameters: beta1hat.fun(D1=D1.big, H1=H1.toy, jj$y,phi=phi.true.toy(phi=phi.toy)) #marginally worse.