mtkNativeEvaluator: The constructor of the class 'mtkNativeEvaluator'

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

View source: R/mtkNativeEvaluator.R

Description

The constructor.

Usage

1
mtkNativeEvaluator(model=NULL, Y=NULL, information=NULL)

Arguments

model

NULL, an R function or a string to specify the model to simulate.

Y

NULL or a data.frame to load the results of model simulation produced off-line.

information

a named list to provide with supplementary information about the simulation produced off-line or the parameters used by the evaluator.

Value

an object of the mtkNativeEvaluator class

Details

We can construct an object of the mtkNativeEvaluator class from the following situations:

For details uses, see examples from ?class(mtkNativeEvaluator).

Author(s)

Juhui WANG, MIA-Jouy, Inra, [email protected]

References

J. Wang, H. Richard, R. Faivre, H. Monod (2013). Le package mtk, une biblioth<c3><a8>que R pour l'exploration num<c3><a9>rique des mod<c3><a8>les. In: Analyse de sensibilit<c3><a9> et exploration de mod<c3><a8>les : Application aux sciences de la nature et de l'environnement (R. Faivre, B. Iooss, S. Mah<c3><a9>vas, D. Makowski, H. Monod, Eds). Editions Quae, Versailles.

See Also

?class(mtkNativeEvaluator)

Examples

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## 1) Create a model simulation  with the model "Ishigami" implemented in the package "mtk"
	evaluator <- mtkNativeEvaluator(model="Ishigami")
	
## 2) Create a model simulation  with a R function implemented outside the package "mtk"

#	a) Create a R function to represent the model of population

	ME <- function(K, Y0, a, t=5, ...){
	
	res <- exp(-a*t)
	res <- Y0+res*(K-Y0)
	res <- K*Y0/res
	out <- as.integer(res)
	
	return(out)	
	}
#	b) Do the sensitivity analysis for the function "ME"

	K <- make.mtkFactor(name="K", nominal=400, distribName="unif",
		distribPara=list(min=100, max=1000))
	Y0 <- make.mtkFactor(name="Y0", nominal=20, distribName="unif",
		distribPara=list(min=1, max=40))
	a <- make.mtkFactor(name="a", nominal=0.1, distribName="unif", 
		distribPara=list(min=0.05, max=0.2))
	factors <- mtkExpFactors(list(K,Y0,a))


	plan <- mtkNativeDesigner ("BasicMonteCarlo", 
		information=c(size=500))

	model <- mtkNativeEvaluator(model=ME, information=c(t=5))

	index<- mtkNativeAnalyser("Regression", information=c(nboot=20) )

	expt <- mtkExpWorkflow( expFactors=factors,
		processesVector=c(
				design= plan,
				evaluate= model,
				analyze= index)
		)
	run(expt)
	summary(expt)
	
##	3) Import the results of model simulation produced off-line into 
##	   an object of mtkNativeEvaluator

	data <- data.frame()
	model <- mtkNativeEvaluator(Y=data, 
		information = list(model="Ishigami"))

mtk documentation built on May 29, 2017, 5:34 p.m.