sensitivity_analysis

Sensitivity analysis can be performed on probalistic simulation outputs (see probabilistic analysis). After a probabilistic Monte Carlo simulation, sensitivity indices can be calculated between simulation inputs (parameters) and simulation outputs (blocks).

Note | The sensitivity analysis toolbox offers many sensitivity analysis methods and several new charts. |

- Run a probabilistic simulation as explained in probability analysis.
- Create tornado charts or correlation tables to graphically display sensitivity indices. Seven different types of sensitivity indices are available in Ecolego.

Method | When to Use |
---|---|

Pearson product-moment correlation coefficient (Pearson) | Computes the correlation coefficient. Interesting when model is linearly depending on parameters. |

Spearman's rank correlation coefficient (Spearman) | Computes the ranked correlation coefficient. Interesting when model is linearly or monotonically depending on parameters. |

Standardized Regression Coefficient (SRC) | Sets up a regression model from the raw data. Interesting when model is linearly depending on parameters. |

Standardized Rank Regression Coefficient (SRRC) | Sets up a regression model from the ranked data. Interesting when model is linearly or monotonically depending on parameters. |

Partial Correlation Coefficient (PCC) | Computes the correlation coefficient taking into account the rest of the varying parameters. Interesting when model is linearly depending on parameters. |

Partial Rank Correlation Coefficient (PRCC) | Computes the ranked correlation coefficient taking into account the rest of the varying parameters. Interesting when model is linearly or monotonically depending on parameters. |

EASI first order correlation coefficient (EASI) | Quantifies the first order effects, if model is additive this will take into account the full variance. |

sensitivity_analysis.txt · Last modified: 2019/11/18 13:34 (external edit)