Description Usage Parameters used to manage the sampling method Parameters used to manage the analysis method Details References See Also Examples
A mtk
compliant implementation of the so-called extended-FAST
or e-Fast
method for experiments design and sensitivity analysis.
mtkFastDesigner(listParameters = NULL)
mtkNativeDesigner(design="Fast", information=NULL)
mtkFastAnalyser()
mtkNativeAnalyser(analyze="Fast", information=NULL)
n
:(numeric
) the number of iteration.
No parameter is necessary.
The mtk
implementation uses the fast99
function of the sensitivity
package. For further details on the arguments and the behaviour, see help(fast99, sensitivity)
.
The mtk
implementation of the Fast
method includes the following classes:
mtkFastDesigner
:for Fast
design processes.
mtkFastAnalyser
:for Fast
analysis processes.
mtkFastDesignerResult
:to store and manage the design.
mtkFastAnalyserResult
:to store and manage the analysis results.
Many ways to create a Fast
designer are available in mtk
, but we recommend the following class constructors:
mtkFastDesigner
or mtkNativeDesigner
.
Many ways to create a Fast
analyser are available in mtk
, but we recommend the following class constructors:
mtkFastAnalyser
or mtkNativeAnalyser
.
The method Fast
is usually used both to build the experiment design and to carry out the sensitivity analysis. In such case,
we can use the mtkDefaultAnalyser
instead of naming explicitly the method for sensitivity analysis (see example III in the examples section)
A. Saltelli, K. Chan and E. M. Scott (2000). Sensitivity Analysis. Wiley, New York.
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.
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 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | ## Sensitivity analysis of the "Ishigami" model with the "Fast" method
# Example I: by using the class constructors: mtkFastDesigner() and mtkFastAnalyser()
# Input the factors
data(Ishigami.factors)
# Build the processes and workflow:
# 1) the design process
exp1.designer <- mtkFastDesigner(listParameters
= list(n=1000))
# 2) the simulation process
exp1.evaluator <- mtkNativeEvaluator(model="Ishigami")
# 3) the analysis process
exp1.analyser <- mtkFastAnalyser()
# 4) the workflow
exp1 <- mtkExpWorkflow(expFactors=Ishigami.factors,
processesVector = c(design=exp1.designer,
evaluate=exp1.evaluator, analyze=exp1.analyser))
# Run the workflow and reports the results.
run(exp1)
print(exp1)
plot(exp1)
## Example II: by using the class constructors: mtkNativeDesigner() and mtkFastAnalyser()
# Generate the factors
data(Ishigami.factors)
# Build the processes and workflow:
# 1) the design process
exp1.designer <- mtkNativeDesigner(design = "Fast",information=list(n=1000))
# 2) the simulation process
exp1.evaluator <- mtkNativeEvaluator(model="Ishigami")
# 3) the analysis process with the default method
exp1.analyser <- mtkFastAnalyser()
# 4) the workflow
exp1 <- mtkExpWorkflow(expFactors=Ishigami.factors,
processesVector = c(design=exp1.designer,
evaluate=exp1.evaluator, analyze=exp1.analyser))
# Run the workflow and reports the results.
run(exp1)
plot(exp1)
## Example III: by using the class constructors: mtkFastDesigner() and mtkDefaultAnalyser()
# Generate the factors
data(Ishigami.factors)
# Build the processes and workflow:
# 1) the design process
exp1.designer <- mtkFastDesigner( listParameters = list(n=2000))
# 2) the simulation process
exp1.evaluator <- mtkNativeEvaluator(model="Ishigami")
# 3) the analysis process with the default method
exp1.analyser <- mtkDefaultAnalyser()
# 4) the workflow
exp1 <- mtkExpWorkflow(expFactors=Ishigami.factors,
processesVector = c(design=exp1.designer,
evaluate=exp1.evaluator, analyze=exp1.analyser))
# Run the workflow and reports the results.
run(exp1)
plot(exp1)
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