optimal.scales: Use optimization techniques to find the optimal scales
In emulator: Bayesian Emulation of Computer Programs

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Uses optimization techniques (either Nelder-Mead or simulated annealing) to find the optimal scales, or roughness lengths. Function optimal.scale() (ie singular) finds the optimal scale on the assumption that the roughness is isotropic so all scales are identical.

optimal.scales(val, scales.start, d, use.like = TRUE,  give.answers =
FALSE, func=regressor.basis, ...)
optimal.scale(val, d, use.like = TRUE,  give.answers =
FALSE, func=regressor.basis, ...)

`val`	Matrix with rows corresponding to points at which the function is known
`scales.start`	Initial guess for the scales (plural). See details section for explanation
`d`	vector of observations, one for each row of `val`
`use.like`	Boolean, with default `TRUE` meaning to use likelihood for the objective function, and `FALSE` meaning to use a leave-out-one bootstrap estimator
`give.answers`	Boolean, with default `FALSE` meaning to return just the roughness lengths and `TRUE` meaning to return extra information as returned by `optim()`
`func`	Function used to determine basis vectors, defaulting to `regressor.basis` if not given
`...`	Extra parameters passed to `optim()` or `optimize()`. See examples for usage of this argument

Internally, this function works with the logarithms of the roughness lengths, because they are inherently positive. However, note that the lengths themselves must be supplied to argument scales.start, not their logarithms.

The reason that there are two separate functions is that optim() and optimize() are very different.

If give.answers takes the default value of FALSE, a vector of roughness lengths is returned. If TRUE, output from optim() is returned directly (note that element par is the logarithm of the desired roughness length as the optimization routine operates with the logs of the lengths as detailed above)

This function is slow to evaluate because it needs to calculate and invert A each time it is called, because the scales change from call to call.

In this package, “scales” means the diagonal elements of the B matrix. See the help page for corr for more discussion of this topic.

Note the warning about partial matching under the “dot-dot-dot” argument in both optim.Rd [used in optimal.scales()] and optimize.Rd [used in optimal.scale()]: any unmatched arguments will be passed to the objective function. Thus, passing named but unmatched arguments to optimal.scale[s]() will cause an error, because those arguments will be passed, by optim() or optimize(), to the (internal) objective.fun().

In particular, note that passing control=list(maxit=4) to optimal.scale() will cause an error for this reason [optimize() does not take a control argument].

Robin K. S. Hankin

J. Oakley 2004. Estimating percentiles of uncertain computer code outputs. Applied Statistics, 53(1), pp89-93.
J. Oakley 1999. Bayesian uncertainty analysis for complex computer codes, PhD thesis, University of Sheffield.

interpolant,corr

##First, define some scales:
fish <- c(1,1,1,1,1,4)

## and a sigmasquared value:
REAL.SIGMASQ <- 0.3

## and a real relation:
real.relation <- function(x){sum( (1:6)*x )}

## Now a design matrix:
val  <- latin.hypercube(100,6)

## apply the real relation:
d <- apply(val,1,real.relation)

## and add some suitably correlated Gaussian noise:
A <- corr.matrix(val,scales=fish)
d.noisy <-  as.vector(rmvnorm(n=1,mean=apply(val,1,real.relation),REAL.SIGMASQ*A))

##  Now see if we can estimate the roughness lengths well.  Remember that
##  the true values are those held in vector "fish":
optimal.scales(val=val, scales.start=rep(1,6), d=d.noisy,
       method="SANN",control=list(trace=1000,maxit=3),
       give=FALSE)



# Now a test of optimal.scale(), where there is only a single roughness
#  scale to estimate.  This should be more straightforward:


df <- latin.hypercube(40,6)
fish2 <- rep(2,6)
A2 <- corr.matrix(df,scales=fish2)
d.noisy <- as.vector(rmvnorm(n=1, mean=apply(df,1,real.relation), sigma=A2))

jj.T <- optimal.scale(val=df,d=d.noisy,use.like=TRUE)
jj.F <- optimal.scale(val=df,d=d.noisy,use.like=FALSE)