mtk (version 1.0)

mtkRegressionAnalyser-class: The mtkRegressionAnalyser class

Description

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.

Class Hierarchy

Parent classes :

mtkAnalyser

Direct Known Subclasses :

Constructor

mtkRegressionAnalyser

signature(mtkParameters = NULL, listParameters = NULL)

Slots

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

Methods

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.

See Also

help(morris, sensitivity) and help(Regression)

Examples

## 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)