mtkNativeEvaluator-class: The 'mtkNativeEvaluator' class

In mtk: Mexico ToolKit library (MTK)

Description

The mtkNativeEvaluator class is a sub-class of the class mtkEvaluator used to manage the simulation task implemented locally (i.e. tasks don't need to call services from the Web). It provides all the slots and methods defined in the class mtkEvaluator.

Class Hierarchy

Parent classes :

mtkEvaluator

Direct Known Subclasses :

Constructor

mtkNativeEvaluator

signature(model=NULL, Y=NULL, information=NULL)

Slots

model:

(ANY) a string, an R fonction, or NULL to inform the model to simulate.

name:

(character) always takes the string "evaluate".

protocol:

(character) a string to name the protocol used to run the process: http, system, R, etc. Here, it always takes "R".

site:

(character) a string to indicate where the service is located. Here, it always takes "mtk".

service:

(character) a string to name the service to invoke.

parameters:

(vector) a vector of [mtkParameter] containing the parameters to pass while calling the service.

ready:

(logical) a logical to tell if the process is ready to run.

state:

(logical) a logical to tell if the results produced by the process are available and ready to be consumed.

result:

(ANY) a data holder to hold the results produced by the process

Methods

setName

signature(this = "mtkNativeEvaluator", name = "character"): Not used, method inherited from the parent class.

setParameters

signature(this = "mtkNativeEvaluator", f = "vector"): Assigns new parameters to the process.

getParameters

signature(this = "mtkNativeEvaluator"): Returns the parameters as a named list.

is.ready

signature( = "mtkNativeEvaluator"): Tests if the process is ready to run.

setReady

signature(this = "mtkNativeEvaluator", switch = "logical"): Makes the process ready to run.

is.ready

signature( = "mtkNativeEvaluator"): Tests if the results produced by the process are available.

setReady

signature(this = "mtkNativeEvaluator", switch = "logical"): Marks the process as already executed.

getResult

signature(this = "mtkNativeEvaluator"): Returns the results produced by the process as a [mtkEvaluatorResult].

getData

signature(this = "mtkNativeEvaluator"): Returns the results produced by the process as a data.frame.

serializeOn

signature(this = "mtkNativeEvaluator"): Returns all data managed by the process as a named list.

run

signature(this = "mtkNativeEvaluator", context= "mtkExpWorkflow"): runs the simulation.

summary

signature(object = "mtkNativeEvaluator"): Provides a summary of the results produced by the process.

print

signature(x = "mtkNativeEvaluator"): Prints a report of the results produced by the process.

plot

signature(x = "mtkNativeEvaluator"): Plots the results produced by the process.

report

signature(this = "mtkNativeEvaluator"): Reports the results produced by the process.

Details

We can construct an object of the mtkNativeEvaluator class from the following situations: 1) 2) 3) the experimental design is produced off-line and available as a data.frame.

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

1. The evaluator is provided within the package "mtk";

2. The evaluator is provided as an R function outside the package "mtk";

3. The simulation is carried out off-line. We just want to use the "mtk" package for reporting.

Author(s)

Juhui WANG, MIA-Jouy, Inra, [e-mail]

References

J. Wang, H. Richard, R. Faivre, H. Monod (2013). Le package mtk, une bibliothèque R pour l'exploration numérique des modèles. In: Analyse de sensibilité et exploration de modèles : Application aux sciences de la nature et de l'environnement (R. Faivre, B. Iooss, S. Mahévas, D. Makowski, H. Monod, Eds). Editions Quae, Versailles.

Examples

## 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 2, 2019, 4:15 a.m.