# toys

##### 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) pp425-464M. 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.psR. 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`

##### 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*