====== Sensitivity analysis ======

[[http://en.wikipedia.org/wiki/Sensitivity_analysis|Sensitivity analysis]] can be performed on probalistic simulation outputs (see [[Probabilistic_simulation|probabilistic simulation]]). After a probabilistic Monte Carlo simulation, sensitivity indices can be calculated between simulation inputs (parameters) and simulation outputs (blocks).

===== Performing a sensitivity analysis =====

|Note|The [[Sensitivity_analysis_toolbox|sensitivity analysis toolbox]] offers many sensitivity analysis methods and several additional [[Chart|charts]].|

  - Run a probabilistic simulation as explained in [[Probabilistic_simulation|probabilistic simulation]].
  - Create [[Tornado_chart|tornado charts]] or [[Correlation_table|correlation tables]] to graphically display sensitivity indices. Seven different types of sensitivity indices are available in Ecolego.

===== Sensitivity indices =====

^Method                                                                    ^When to Use                                                                                                                                                                     ^
|Pearson product-moment correlation coefficient ([[Pearson|Pearson]])|Computes the correlation coefficient. Interesting when model is linearly depending on parameters.                                                                               |
|Spearman's rank correlation coefficient ([[Spearman|Spearman]])     |Computes the ranked correlation coefficient. Interesting when model is linearly or monotonically depending on parameters.                                                       |
|Standardized Regression Coefficient ([[SRC|SRC]])                   |Sets up a regression model from the raw data. Interesting when model is linearly depending on parameters.                                                                       |
|Standardized Rank Regression Coefficient ([[SRRC|SRRC]])            |Sets up a regression model from the ranked data. Interesting when model is linearly or monotonically depending on parameters.                                                   |
|Partial Correlation Coefficient ([[PCC|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|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|EASI]])            |Quantifies the first order effects, if model is additive this will take into account the full variance.                                                                         |
|XEASI Higher Order Sensitivity Indices([[XEASI|XEASI]])             |XEASI indices can assist in model calibration and validation processes by revealing which parameters have the most substantial impact on model predictions.|
===== See also =====

  * [[Correlation_table|Correlation table]]
  * [[Tornado_chart|Tornado chart]]