Description Class Hierarchy Constructor Slots Methods See Also Examples
The mtkRegressionAnalyser
class is a sub-class of the class mtkAnalyser
. It implements the sensitivity analysis method Regression
and provides all the slots and methods defined in the class mtkAnalyser
.
mtkAnalyser
mtkRegressionAnalyser
signature(mtkParameters = NULL, listParameters = NULL)
name
:(character
) always takes the string "analyze".
protocol
:(character
) always takes the string "R".
site
:(character
) always takes the string "mtk".
service
:(character
) always takes the string "Regression".
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
setName
signature(this = "mtkRegressionAnalyser", name = "character"): Not used, method inherited from the parent class.
setParameters
signature(this = "mtkRegressionAnalyser", f = "vector"): Assigns new parameters to the process.
getParameters
signature(this = "mtkRegressionAnalyser"): Gets the parameters as a named list.
is.ready
signature( = "mtkRegressionAnalyser"): Tests if the process is ready to run.
setReady
signature(this = "mtkRegressionAnalyser", switch = "logical"): Makes the process ready to run.
is.ready
signature( = "mtkRegressionAnalyser"): Tests if the results produced by the process are available.
setReady
signature(this = "mtkRegressionAnalyser", switch = "logical"): Marks the process as already executed.
getResult
signature(this = "mtkRegressionAnalyser"): Returns the results produced by the process as a [mtkRegressionAnalyserResult
].
getData
signature(this = "mtkRegressionAnalyser"): Returns the results produced by the process as a data.frame.
serializeOn
signature(this = "mtkRegressionAnalyser"): Returns all data managed by the process as a named list.
run
signature(this = "mtkRegressionAnalyser", context= "mtkExpWorkflow"): Generates the experimental design by sampling the factors.
summary
signature(object = "mtkRegressionAnalyser"): Provides a summary of the results produced by the process.
print
signature(x = "mtkRegressionAnalyser"): Prints a report of the results produced by the process.
plot
signature(x = "mtkRegressionAnalyser"): Plots the results produced by the process.
report
signature(this = "mtkRegressionAnalyser"): Reports the results produced by the process.
help(morris, sensitivity)
and help(Regression)
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 | ## Sensitivity analysis of the "Ishigami" model with the "Monte-Carlo" and "Regression" methods
# Generate the factors
data(Ishigami.factors)
# Build the processes and workflow:
# 1) the design process
exp.designer <- mtkBasicMonteCarloDesigner (listParameters=list(size=20))
# 2) the simulation process
exp.evaluator <- mtkIshigamiEvaluator()
# 3) the analysis process
exp.analyser <- mtkRegressionAnalyser(listParameters=list(nboot=20) )
# 4) the workflow
exp1 <- mtkExpWorkflow(expFactors=Ishigami.factors,
processesVector = c(design=exp.designer,
evaluate=exp.evaluator, analyze=exp.analyser))
# Run the workflow and report the results.
run(exp1)
print(exp1)
|
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