beta1hat.fun {calibrator}R Documentation

beta1 estimator

Description

Least squares estimator for beta1

Usage

beta1hat.fun(D1, H1, y, phi)

Arguments

D1 code run points
H1 regressor basis funs
y code outputs
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

beta2hat.fun

Examples

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.


[Package calibrator version 1.0-48 Index]