Description Usage Arguments Details Value Author(s) References See Also Examples

Setting up the SAVE methodology: construction of an emulator of the computer model and estimation of the parameters of the Gaussian process for the bias function.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
SAVE(response.name=NULL, controllable.names=NULL, calibration.names=NULL,
field.data=NULL, model.data=NULL, mean.formula=~1, bestguess=NULL,
kriging.controls=SAVE.controls(), verbose=FALSE)
## S4 method for signature 'SAVE'
show(object)
## S4 method for signature 'SAVE'
summary(object)
## S4 method for signature 'summary.SAVE'
show(object)
``` |

`response.name` |
A |

`controllable.names` |
Either a |

`calibration.names` |
A |

`field.data` |
A |

`model.data` |
A |

`mean.formula` |
A |

`bestguess` |
A named |

`kriging.controls` |
A named |

`verbose` |
A |

`object` |
An object of the corresponding signature. |

Based on computer model runs, `SAVE`

fits an approximation of the model output, usually called an emulator. The emulator is constructed using the Gaussian process response technique (GASP), described in more detail in `SAVE-class`

. Further, at this stage an estimation of the parameters of the Gaussian process specifying the bias function (difference between field observations and computer model outputs) is also performed. Some of the calculations are done using the package `DiceKriging`

`SAVE`

returns an S4 object of class `SAVE`

(see `SAVE-class`

).

Jesus Palomo, Rui Paulo and Gonzalo Garcia-Donato

Palomo J, Paulo R, Garcia-Donato G (2015). SAVE: An R Package for the Statistical Analysis of Computer Models. Journal of Statistical Software, 64(13), 1-23. Available from http://www.jstatsoft.org/v64/i13/

Bayarri MJ, Berger JO, Paulo R, Sacks J, Cafeo JA, Cavendish J, Lin CH, Tu J (2007). A Framework for Validation of Computer Models. Technometrics, 49, 138-154.

Craig P, Goldstein M, Seheult A, Smith J (1996). Bayes linear strategies for history matching of hydrocarbon reservoirs. In JM Bernardo, JO Berger, AP Dawid, D Heckerman, AFM Smith (eds.), Bayesian Statistics 5. Oxford University Press: London. (with discussion).

Higdon D, Kennedy MC, Cavendish J, Cafeo J, Ryne RD (2004). Combining field data and computer simulations for calibration and prediction. SIAM Journal on Scientific Computing, 26, 448-466.

Kennedy MC, O Hagan A (2001). Bayesian calibration of computer models (with discussion). Journal of the Royal Statistical Society B, 63, 425-464.

Roustant O., Ginsbourger D. and Deville Y. (2012). DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization. Journal of Statistical Software, 51(1), 1-55.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
## Not run:
library(SAVE)
#############
# load data
#############
data(spotweldfield,package='SAVE')
data(spotweldmodel,package='SAVE')
##############
# create the SAVE object which describes the problem and
# compute the corresponding mle estimates
##############
gfsw <- SAVE(response.name="diameter", controllable.names=c("current", "load", "thickness"),
calibration.names="tuning", field.data=spotweldfield,
model.data=spotweldmodel, mean.formula=~1,
bestguess=list(tuning=4.0))
# summary of the results
summary(gfsw)
## End(Not run)
``` |

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