object {approximator} | R Documentation |
Returns the likelihood of a set of hyperparameters given the data.
Functions opt1()
and opt.gt.1()
find hyperparameters
that maximize the relevant likelihood for level 1 and higher levels
respectively. Function object()
returns the expression given
by equation 9 in KOH2000, which is minimized opt1()
and
opt.gt.1()
.
object(level, D, z, basis, subsets, hpa) opt.1(D, z, basis, subsets, hpa.start, give.answers=FALSE, ...) opt.gt.1(level, D, z, basis, subsets, hpa.start, give.answers=FALSE, ...)
level |
level |
D |
Design matrix for top-level code |
z |
Data |
basis |
Basis function |
subsets |
subsets object |
hpa |
hyperparameter object |
hpa.start |
Starting value for hyperparameter object |
give.answers |
Boolean, with default FALSE meaning to
return just the point estimate, and TRUE meaning to return
extra information from the call to optim() . |
... |
Extra arguments passed to optim() . A common one
would be control=list(trace=100) . |
This function is the object function used in toy optimizers
optimal.hpa()
.
Robin K. S. Hankin
M. C. Kennedy and A. O'Hagan 2000. “Predicting the output from a complex computer code when fast approximations are available” Biometrika, 87(1): pp1-13
data(toyapps) object(level=4, D=D1.toy , z=z.toy,basis=basis.toy, subsets=subsets.toy, hpa=hpa.fun.toy(1:19)) object(level=4, D=D1.toy , z=z.toy,basis=basis.toy, subsets=subsets.toy, hpa=hpa.fun.toy(3+(1:19))) # Now a little example of finding optimal hyperpameters in the toy case # (a bigger example is given on the genie help page) jj <- list(trace=100,maxit=10) hpa.toy.level1 <- opt.1(D=D1.toy, z=z.toy, basis=basis.toy, subsets=subsets.toy, hpa.start=hpa.toy,control=jj) hpa.toy.level2 <- opt.gt.1(level=2, D=D1.toy, z=z.toy, basis=basis.toy, subsets=subsets.toy, hpa.start=hpa.toy.level1,control=jj) hpa.toy.level3 <- opt.gt.1(level=3, D=D1.toy, z=z.toy, basis=basis.toy, subsets=subsets.toy, hpa.start=hpa.toy.level2,control=jj) hpa.toy.level4 <- opt.gt.1(level=4, D=D1.toy, z=z.toy, basis=basis.toy, subsets=subsets.toy, hpa.start=hpa.toy.level3,control=jj)