toyapps {approximator}

Toy datasets for approximator package

Description

Toy datasets that illustrate the package.

Usage

  data(toyapps)

Format

The toy example is a case with four levels.

The D1.toy matrix is 20 rows of code run points, corresponding to the observations of the level 1 code. It has three columns, one per parameter.

hpa.toy is a hyperparameter object. It is a list of three elements: sigmas, B, and rhos.

subsets.toy is a list of four elements. Element i corresponds to the rows of D1.toy at which level i has been observed.

z.toy is a four element list. Each element is a vector; element i corresponds to obsevations of level i. The lengths will match those of subsets.toy.

betas.toy is a matrix of coefficients.

Brief description of toy functions fully documented under their own manpage

Function generate.toy.observations() creates new toy datasets with any number of observations and code runs.

Function basis.toy() is an example of a basis function

Function hpa.fun.toy() creates a hyperparameter object such as phi.toy in a form suitable for passing to the other functions in the library.

See the helpfiles listed in the “see also” section below

Details

All toy datasets are documented here. There are also several toy functions that are needed for a toy problem; these are documented separately (they are too diverse to document fully in a single manpage). Nevertheless a terse summary for each toy function is provided on this page. All toy functions in the package are listed under “See Also”.

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)

is.consistent(subsets.toy , z.toy)

generate.toy.observations(D1.toy, subsets.toy, basis.toy, hpa.toy, betas.toy)