# toys

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##### Toy datasets

Toy datasets that illustrate the package.

Keywords
datasets
##### Usage
data(toys)
D1.toy
D2.toy
d.toy
phi.toy
theta.toy
V.toy
X.dist.toy
##### 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”.

##### Format

The D1.toy matrix is 8 rows of code run points, with five columns. The first two columns are the lat and long and the next three are parameter values.

The D2.toy matrix is five rows of observations on two variables, x and y which are styled “latitude and longitude”.

d.toy is the “data” vector consisting of length 13: elements 1-8 are code runs and elements 9-13 are observations.

theta.toy is a vector of length three that is a working example of $\theta$. The parameters are designed to work with computer.model().

t.vec.toy is a matrix of eight rows and three columns. Each row specifies a value for $\theta$. The eight rows correspond to eight code runs.

x.toy and x.toy2 are vectors of length two that gives a sample point at which observations may be made (or the code run). The gloss of the two elements is latitude and longitude.

x.vec is a matrix whose rows are reasonable x values but not those in D2.toy.

y.toy is a vector of length eight. Each element corresponds to the output from a code run at each of the rows of D1.toy.

z.toy is a vector of length five. Each element corresponds to a measurement at each of the rows of D2.toy.

V.toy is a five by five variance-covariance matrix for the toy datasets.

X.dist.toy is a toy example of a distribution of X for use in calibrated uncertainty analysis, section 4.2.

Brief description of toy functions fully documented under their own manpage

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

Function E.theta.toy() returns expectation of H(D) with respect to $\theta$; Edash.theta.toy() returns expectation with respect to $E'$.

Function extractor.toy() extracts x.star.toy and t.vec.toy from D2; toy example needed because the extraction differs from case to case.

Function H1.toy() applies basis functions to rows of D1 and D2

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

Function phi.change.toy() modifies the hyperparameter object.

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

##### References

• 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)

##### See Also

create.new.toy.datasets, E.theta.toy, extractor.toy, H1.toy, phi.fun.toy, stage1

• D1.toy
• D2.toy
• d.toy
• phi.toy
• theta.toy
• t.vec.toy
• toys
• x.toy
• x.toy2
• x.vec
• y.toy
• z.toy
• V.toy
• X.dist.toy
##### Examples
# NOT RUN {
data(toys)
D1.toy
extractor.toy(D1.toy)

D2.fun(theta=theta.toy , D2=D2.toy)
D2.fun(theta=theta.toy,D2=D2.toy[1,,drop=FALSE])

library("emulator")
corr.matrix(D1.toy,scales=rep(1,5))
corr.matrix(D1.toy, pos.def.matrix=diag(5))

# }

Documentation reproduced from package calibrator, version 1.2-8, License: GPL-2

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