getResult-methods: The 'getResult' method

Description Usage Arguments Details Value Author(s) References Examples

Description

Returns the results produced by the process as an object of the class mtkResult or its sub-classes.

Usage

1
getResult(this)

Arguments

this

the underlying object of class mtkProcess or its sub-classes

Details

  1. Sub-class of the class mtkProcess returns objects of different sub-class of the class mtkResult. For instance, an object of the class mtkDesigner returns an object of the class mtkDesignerResult.

  2. To fetch the results as a data.frame, please use the method getData().

Value

an object of the class mtkResult.

Author(s)

Juhui WANG, MIA-Jouy, Inra, Juhui.Wang@jouy.inra.fr

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
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
# 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)) 
	
# Builds 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)

# Extracts the results produced by the analysis process as an objet of the class mtkAnalyserResult.

 	getResult(getProcess(exp1, "analyze"))

mtk documentation built on May 2, 2019, 4:15 a.m.