genie {approximator} | R Documentation |
Genie datasets that illustrate the package.
data(genie) D1.genie hpa.genie z.genie subsets.genie basis.genie(x) hpa.fun.genie(x) hpa.genie.start hpa.genie.optimal
x |
A 4-element vector (for basis.genie() ); a 19-element
vector (for hpa.fun.genie() ) |
The genie example is a case with three levels.
The D1.genie
matrix is 36 rows of code run points,
corresponding to the observations of the level 1 code. It has four
columns, one per parameter.
hpa.genie
is a hyperparameter object.
subsets.genie
is a list of three elements. Element i
corresponds to the rows of D1.genie
at which level i has
been observed.
z.genie
is a three element list. Each element is a vector;
element i corresponds to observations of level i. The
lengths will match those of subsets.genie
.
Function basis.genie()
is a suitable basis function.
Function hpa.fun.genie()
creates a hyperparameter object in a
form suitable for passing to the other functions in the library.
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(genie) z.genie jj <- list(trace=100,maxit=10) hpa.genie.level1 <- opt.1(D=D1.genie, z=z.genie, basis=basis.genie, subsets=subsets.genie, hpa.start=hpa.genie.start,control=jj) hpa.genie.level2 <- opt.gt.1(level=2, D=D1.genie, z=z.genie, basis=basis.genie, subsets=subsets.genie, hpa.start=hpa.genie.level1,control=jj) hpa.genie.level3 <- opt.gt.1(level=3, D=D1.genie, z=z.genie, basis=basis.genie, subsets=subsets.genie, hpa.start=hpa.genie.level2,control=jj)