SAVE (version 0.9.3.3)

SAVE: Functions to start the Statistical Analysis and Validation Engine of a computer model and corresponding exploration of results

Description

SAVE starts the main object for subsequent analyses.

Usage

SAVE(response.name, controllable.names, calibration.names, field.data, model.data,
             mean.formula, bestguess)

## S3 method for class 'SAVE':
show(object)

## S3 method for class 'SAVE':
summary(object)

## S3 method for class 'summary.SAVE':
show(object)

Arguments

response.name

A character object with the name of the response variable.

controllable.names

A character object with the names of the controllable inputs.

calibration.names

A character object with the names of the calibration inputs. Use NULL if the model does not depend on any calibration inputs.

field.data

A data.frame with the observations coming from the field experiment. The column names of this data.frame should contain the controllable.names and the response.name.

model.data

A data.frame with the observations coming from runs of the computer model. The column names of this data.frame should contain the controllable.names, the calibration.names and the response.name

mean.formula

A formula specifying the mean function of the Gaussian Process approximation to the output of the computer model (the emulator). Can only involve terms in controllable.names.

bestguess

A named list specifying the best guess for the calibration parameters.

object

An object of the corresponding signature.

Value

• SAVE returns an S4 object of class SAVE (see SAVE-class).

Details

This function fits the emulator of the computer model and an estimation of the bias function. Calculations are partially based on the package DiceKriging

References

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.

See Also

"predictcode", bayesfit

Examples

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="N", controllable.names=c("C", "L", "G"), calibration.names=c("t"), field.data=spotweldfield, model.data=spotweldmodel, mean.formula=as.formula("~1"), bestguess=list(t=4.0))

# summary of the results

summary(gfsw)