Description Usage Arguments Details Author(s) References See Also Examples
Perform O'Hagan's three stage optimization on the toy dataset. Function
stage1()
and stage2()
find the optimal values for
the hyperparameters and stage3()
finds the optimal values for
the three parameters.
1 2 3 4 5 6 7 8 9 10 11 12 | stage1(D1, y, H1, maxit, trace=100, method="Nelder-Mead",
directory = ".", do.filewrite=FALSE, do.print=TRUE,
phi.fun, lognormally.distributed=FALSE, include.prior=TRUE, phi)
stage2(D1, D2, H1, H2, y, z, maxit, trace=100, method = "Nelder-Mead",
directory = ".", do.filewrite=FALSE, do.print=TRUE, extractor,
phi.fun, E.theta, Edash.theta, isotropic=FALSE,
lognormally.distributed = FALSE, include.prior = TRUE,
use.standin = FALSE, rho.eq.1 = TRUE, phi)
stage3(D1, D2, H1, H2, d, maxit, trace=100, method="Nelder-Mead",
directory = ".", do.filewrite=FALSE, do.print=TRUE,
include.prior = TRUE, lognormally.distributed=FALSE,
theta.start=NULL, phi)
|
maxit |
Maximum number of iterations as passed to |
trace |
Amount of information displayed, as passed to |
D1 |
Matrix whose rows are points at which code output is known |
D2 |
Matrix whose rows are points at which observations were made |
H1,H2 |
Regressor basis functions for |
y |
Code outputs. Toy example is |
z |
Observations. Toy example is |
d |
Data vector consisting of the code runs and observations |
extractor |
extractor function for |
E.theta,Edash.theta |
Expectation WRT theta, and dashed theta.
Toy examples are |
phi.fun |
Function to create hyperparameters; passed (in
|
method |
Method argument passed to |
include.prior |
Boolean variable with default |
lognormally.distributed |
Boolean with |
do.filewrite |
Boolean, with |
directory |
The directory to write files to; only matters if
|
isotropic |
In function |
do.print |
Boolean, with default |
use.standin |
In |
rho.eq.1 |
Boolean, with default |
theta.start |
In |
phi |
Hyperparameters. Used as initial values for the hyperparameters in the optimization routines |
The three functions documented here carry out the multi-stage
optimization detailed in KOH2001 (actually, KOH2001 only defined stage
1 and stage 2, which estimated the hyperparameters. What is here
called “stage3()
” estimates the true value of
theta given the hyperparameters).
stage1()
carries out stage 1 of KOH2001 which is used to
estimate psi1 using optimization.
In function stage2()
, setting argument isotropic
to
TRUE
will force phi$omegastar_x
to be a function of a
length one scalar. The value of phi$omegastar_x
used will
depend on pdm.maker.psi2()
(an internal function appearing in
hpa.fun.toy()
). In stage2()
, several kludges are made.
The initial conditions are provided by argument phi
. The
relevant part of this is phi$psi2
.
Function stage2()
estimates psi2 and
rho and lambda, using
optimization. Note that psi2 includes
sigma2squared in addition to omegastar_X
(in
the toy case, psi2 has three elements: the first two are
the diagonal of omegastar_x
and the third is
sigma2squared but this information is
encoded in phi.fun.toy()
, which changes from application to
application).
Function stage3()
attempts to find the maximum likelihood
estimate of theta, given hyperparameters and
observations, using optimization
Robin K. S. Hankin
M. C. Kennedy and A. O'Hagan 2001. Bayesian calibration of computer models. Journal of the Royal Statistical Society B, 63(3) pp425-464
M. C. Kennedy and A. O'Hagan 2001. Supplementary details on Bayesian calibration of computer models, Internal report, University of Sheffield. Available at http://www.tonyohagan.co.uk/academic/ps/calsup.ps
R. K. S. Hankin 2005. Introducing BACCO, an R bundle for Bayesian analysis of computer code output, Journal of Statistical Software, 14(16)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | data(toys)
stage1(D1=D1.toy,y=y.toy,H1=H1.toy, maxit=5, phi.fun=phi.fun.toy, phi=phi.toy)
##now try with a slightly bigger dataset:
##Examples below take a few minutes to run:
set.seed(0)
data(toys)
jj <- create.new.toy.datasets(D1.toy , D2.toy)
y.toy <- jj$y.toy
z.toy <- jj$z.toy
d.toy <- jj$d.toy
phi.toy.stage1 <- stage1(D1=D1.toy, y=y.toy, H1=H1.toy, maxit=10, phi.fun=phi.fun.toy, phi=phi.toy)
phi.toy.stage2 <- stage2(D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy,
y=y.toy, z=z.toy, extractor=extractor.toy,
phi.fun=phi.fun.toy, E.theta=E.theta.toy, Edash.theta=Edash.theta.toy,
maxit=3, phi=phi.toy.stage1)
stage3(D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, d=d.toy, maxit=3, phi=phi.toy.stage2)
# Now try with the true values of the hyperparameters:
phi.true <- phi.true.toy(phi=phi.toy)
stage3(D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, d=d.toy, maxit=3, phi=phi.true)
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