# Regression: The 'Regression' Method In mtk: Mexico ToolKit library (MTK)

## Description

A `mtk` compliant implementation of the `src` method for computing the sensitivity index based on standardized (rank) regression coefficients.

## Usage

• mtkRegressionAnalyser(listParameters = NULL)

• mtkNativeAnalyser(analyze="Regression", information=NULL)

## Parameters used to manage the method

`rank`:

logical. If TRUE, the analysis is done on the ranks (default is `FALSE`). See the help on function `src` in the package `sensitivity`.

`nboot`:

the number of bootstrap replicates (default 100). See the help on function `src` in the package `sensitivity`.

`conf`:

the confidence level for bootstrap confidence intervals (default 0.95). See the help on function `src` in the package `sensitivity`.

## Details

1. The `mtk` implementation uses the `src` function of the package `sensitivity`. For further details on the arguments and the behavior, see `help(src, sensitivity)`.

2. The implementation of the "Regression" method includes the class `mtkRegressionAnalyser` to manage the analysis task and the class `mtkRegressionAnalyserResult` to manage the results produced by the analysis process.

## References

A. Saltelli, K. Chan and E. M. Scott (2000). Sensitivity Analysis, Edition Wiley

`help(src, sensitivity)`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```# Uses the method "Regression" to analyze the model "Ishigami": # Generate the factors data(Ishigami.factors) # Builds experiment design with the Monte-Carlo method designer <- mtkBasicMonteCarloDesigner( listParameters=list(size=20) ) # Builds a simulator for the model "Ishigami" with the defined factors model <- mtkNativeEvaluator("Ishigami" ) # Builds an analyser with the method "Regression" implemented in the package "mtk" analyser <- mtkNativeAnalyser("Regression", information=list(nboot=20) ) # Builds a workflow to manage the processes scheduling. ishiReg <- mtkExpWorkflow( expFactors=Ishigami.factors, processesVector=c(design=designer, evaluate=model, analyze=analyser) ) # Runs the workflow et reports the results run(ishiReg) summary(ishiReg) plot(ishiReg) ```