Description Usage Parameters Details References See Also Examples
A mtk
compliant implementation of the Sobol
' method for design of experiments and sensitivity analysis.
mtkSobolDesigner(listParameters = NULL)
mtkNativeDesigner(design="Sobol", information=NULL)
mtkSobolAnalyser(listParameters = NULL)
mtkNativeAnalyser(analyze="Sobol", information=NULL)
N
:the size of the basic samples; the final sample size will be N*(k+2) where k is the number of the factors to analyze.
nboot
:the number of bootstrap replicates (default 0). See the help on function sobol2002
in the package sensitivity
.
conf
:the confidence level for bootstrap confidence intervals (default 0.95). See the help on function sobol2002
in the package sensitivity
.
sampling
:character string specifying the type of sampling method: "MC" (default) for Monte Carlo sampling, "LHS" for Latin Hypercube sampling.
shrink
:a scalar or a vector of scalars between 0 and 1 (default 1), specifying shrinkage to be used on the probabilities before calculating the quantiles.
The mtk
implementation uses the sobol2002
function of the sensitivity
package. For further details on the arguments and the behavior, see help(sobol2002, sensitivity)
.
The mtk
implementation of the Sobol
' method includes the following classes:
mtkSobolDesigner
: for the Sobol
design processes.
mtkSobolAnalyser
: for Sobol
analysis processes.
mtkSobolDesignerResult
: to store and manage the design.
mtkSobolAnalyserResult
: to store and manage the analysis results.
Many ways to create a Sobol
designer are available in mtk
, but we recommend the following class constructors:
mtkSobolDesigner
or mtkNativeDesigner
.
Many ways to create a Sobol
analyser are available in mtk
, but we recommend the following class constructors:
mtkSobolAnalyser
or mtkNativeAnalyser
.
The Sobol
' method 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
help(sobol2002, sensitivity)
, Quantiles
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 83 84 85 86 87 | ## Sensitivity analysis of the "Ishigami" model with the "Sobol" method
# Example I: by using the class constructors: mtkSobolDesigner() and mtkSobolAnalyser()
# Generate the factors
data(Ishigami.factors)
# Build the processes and workflow:
# 1) the design process
exp1.designer <- mtkSobolDesigner( listParameters = list(N=100))
# 2) the simulation process
exp1.evaluator <- mtkNativeEvaluator(model="Ishigami")
# 3) the analysis process
exp1.analyser <- mtkSobolAnalyser()
# 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 mtkSobolAnalyser()
# Generate the factors
data(Ishigami.factors)
# Build the processes and workflow:
# 1) the design process
exp1.designer <- mtkNativeDesigner(design = "Sobol", information = list(N=10))
# 2) the simulation process
exp1.evaluator <- mtkNativeEvaluator(model="Ishigami")
# 3) the analysis process with the default method
exp1.analyser <- mtkSobolAnalyser()
# 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 III: by using the class constructors: mtkSobolDesigner() and mtkDefaultAnalyser()
# Generate the factors
data(Ishigami.factors)
# Build the processes and workflow:
# 1) the design process
exp1.designer <- mtkSobolDesigner( listParameters = list(N=10))
# 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)
print(exp1)
plot(exp1)
|
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