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. |