SAVE-package: Simulator Analysis and Validation Engine

In SAVE: Bayesian Emulation, Calibration and Validation of Computer Models

Description

This package provides a statistical framework for the analysis of complex computer models, that is, for computer models that are expensive to run. Special emphasis is placed on key aspects like emulation (predicting the output of the code at new input points), calibration (‘tuning’ the model so that it matches reality) and validation (answering the question of whether the model adequately represents reality).

The methodology implemented in SAVE is Bayesian and is directly based on Bayarri et al (2007) but has roots also in the papers by Craig et al (1996), Kennedy and O'Hagan (2001) and Higdon et al (2004).

Details

Package:	SAVE
Type:	Package
Version:	0.9.3.3
Date:	2013-04-05
License:	GPL-2

Index:

Author(s)

Jesus Palomo, Rui Paulo and Gonzalo Garcia-Donato

Maintainer: <[e-mail]>

References

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.

See Also

SAVE, SAVE-class

Examples

## Not run: 
	
#Validate the computer model in the Spotweld example (see Bayarri et al 2007 for details)
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))

##############
# obtain the posterior distribution of the unknown parameters 
##############

gfsw <- bayesfit(object=gfsw, prior=c(uniform("tuning", upper=8, lower=0.8)),
				 n.iter=20000, n.burnin=100, n.thin=2)

##############
# validate the computer model at chosen set of controllable
# inputs
###############

load <- c(4.0,5.3)
curr <- seq(from=20,to=30,length=20)
g <- c(1,2)

xnew <- as.data.frame(expand.grid(curr,load,g))
names(xnew)<-c("current","load","thickness")

valsw <- validate(object=gfsw,newdesign=xnew,n.burnin=100)

# summary of validation exercise:
summary(valsw)
# plot results
plot(valsw)

## End(Not run)

SAVE documentation built on May 2, 2019, 6:10 a.m.