# h1.toy

0th

Percentile

##### 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) pp425-464

• 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)

H1.toy

• h1.toy
• h2.toy
##### Examples
# NOT RUN {
data(toys)
h1.toy(D1.toy[1,])
# }

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

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