mtk (version 1.0)

mtkDefaultAnalyser-class: The mtkDefaultAnalyser class

Description

The mtkDefaultAnalyser class is a sub-class of the class mtkAnalyser. It provides all the slots and methods defined in the class mtkAnalyser. The mtkDefaultAnalyser class is used when the method used for the sensitivity analysis is the same as the method used for the experiment design.

Class Hierarchy

Parent classes :

mtkAnalyser

Direct Known Subclasses :

Constructor

mtkDefaultAnalyser

signature()

Slots

name:

(character) always takes the string "analyze".

protocol:

(character) a string to name the protocol used to run the process: http, system, R, etc.

site:

(character) a string to indicate where the service is located.

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 = "mtkDefaultAnalyser", name = "character"): Not used, method inherited from the parent class.

setParameters

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

getParameters

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

is.ready

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

setReady

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

is.ready

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

setReady

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

getResult

signature(this = "mtkDefaultAnalyser"): Returns the results produced by the process as a mtkAnalyserResult.

getData

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

serializeOn

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

run

signature(this = "mtkDefaultAnalyser", context= "mtkExpWorkflow"): Runs the sensitivity analysis defined in the context.

summary

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

print

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

plot

signature(x = "mtkDefaultAnalyser"): Reports graphically the results produced by the process.

report

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

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

# Create a designer and an analyser avec the method "Morris"
# to analyze the model "Ishigami":

# Specify the factors to analyze:
	x1 <- make.mtkFactor(name="x1", distribName="unif",
		 distribPara=list(min=-pi, max=pi)) 
	x2 <- make.mtkFactor(name="x2", distribName="unif",
   	  distribPara=list(min=-pi, max=pi)) 
	x3 <- make.mtkFactor(name="x3", distribName="unif", 
   	  distribPara=list(min=-pi, max=pi)) 
		factors <- mtkExpFactors(list(x1,x2,x3)) 
# Build the processes:
#   1) the experimental design process with the method "Morris".
	exp1.designer <- mtkNativeDesigner(design = "Morris",
		 information=list(r=20,type="oat",levels=4,grid.jump=2)) 

#   2) the model simulation process with the model "Ishigami".
	exp1.evaluator <- mtkNativeEvaluator(model="Ishigami") 

#   3) the analysis process with the default method.
#      Here, it is the "Morris" method.
	exp1.analyser <- mtkDefaultAnalyser()

# Build the  workflow with the processes defined previously.
	exp1 <- mtkExpWorkflow(expFactors=factors,
	    processesVector = c(design=exp1.designer,
		evaluate=exp1.evaluator, analyze=exp1.analyser))
		
# Run the workflow and report the results.
	run(exp1)
	print(exp1)