# h1.toy

##### Basis functions

Basis functions for D1 and D2 respectively.

- Keywords
- array

##### Usage

```
h1.toy(x)
h2.toy(x)
```

##### Arguments

- x
Vector of lat/long or lat/long and theta

##### Details

Note that `h1()`

operates on a vector: for dataframes, use
`H1.toy()`

which is a wrapper for `apply(D1, 1, h1)`

.

**NB** If the definition of `h1.toy()`

or `h2.toy()`

is
changed, then function `hbar.toy()`

must be changed to match.
This cannot be done automatically, as the form of `hbar.toy()`

depends on the distribution of `X`

. The shibboleth is whether
`E_X()`

commutes with `h_1()`

; it does in this case but does
not in general (for example, consider
\(h(x,\theta)=c(1,x,x^2)\) and \(X\sim
N(m,V)\). Then \(E_X(h(x,\theta))\) will
be \((1,m,m^2+V,\theta)\); note the V)

##### Value

Returns basis functions of a vector; in the toy case, just prepend a
`1`

.

##### 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

##### Examples

```
# NOT RUN {
data(toys)
h1.toy(D1.toy[1,])
# }
```

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