# tee

##### Auxiliary functions for equation 9 of the supplement

Returns a vector whose elements are the “distances” from a point
to the observations and code run points (`tee()`

); and basis
functions for use in `Ez.eqn9.supp()`

- Keywords
- array

##### Usage

```
tee(x, theta, D1, D2, phi)
h.fun(x, theta, H1, H2, phi)
```

##### Arguments

- x
Point from which distances are calculated

- theta
Value of parameters

- D1,D2
Design matrices of code run points and field observation points respectively (

`tee()`

)- H1,H2
Basis functions for eta and model inadequacy term respectively (

`h.fun()`

)- phi
Hyperparameters

##### Details

Equation 9 of the supplement is identical to equation 10 of KOH2001.

Function `h.fun()`

returns the first of the subsidiary equations
in equation 9 of the supplement and function `tee()`

returns the
second (NB: do not confuse this with functions `t1bar()`

and
`t2bar()`

which are internal to `EK.eqn10.supp()`

)

##### 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)
tee(x=x.toy, theta=theta.toy, D1=D1.toy, D2=D2.toy, phi=phi.toy)
# Now some vectorized examples:
jj <- rbind(x.toy , x.toy , x.toy+0.01,x.toy+1,x.toy*10)
tee(x=jj, theta=theta.toy, D1=D1.toy, D2=D2.toy, phi=phi.toy)
h.fun(x=jj, theta=theta.toy, H1=H1.toy, H2=H2.toy, phi=phi.toy)
# }
```

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