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sensitivity_analysis_toolbox_example_-_cow [2022/09/27 12:48]
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sensitivity_analysis_toolbox_example_-_cow [2023/03/06 13:37] (current)
mina |
| **Selection of probabilistic parameters** | **Selection of probabilistic parameters** |
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| |{{:sensitivitytoolboxparameterselectiontab1.png?400|}}{{:sensitivitytoolboxparameterselectiontab2.png?400|}}|Go to Select tab to open the [[Parameter_selection_view|Parameter selection view]]. Here, the parameters for which the sensitivity analysis should be run are selected.\\ \\ //Select the five parameters in the table above by moving them to the right in the parameter selection view//| | |{{:sensitivitytoolboxparameterselectiontab1.png?400|}}{{:sensitivitytoolboxparameterselectiontab2.png?400|}}|Go to **Select** tab to open the [[Parameter_selection_view|Parameter selection view]]. Here, the parameters for which the sensitivity analysis should be run are selected.\\ \\ //Select the five parameters in the table above by moving them to the right in the parameter selection view//| |
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| **Selection of Sensitivity analysis method** | **Selection of Sensitivity analysis method** |
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| |{{:sensitivityanalysismethod.png?400|}}|Go to Method tab to open the [[Sensitivity_analysis_method_view|Sensitivity analysis method view]]. Here, the [[Sensitivity_analysis_methods|Sensitivity analysis method]] and its settings is selected.\\ \\ //Select the method Probabilistic with the following settings:\\ \\ Base sample size: 1000\\ Sampling type: Latin Hypercube\\ Calculate second order indices: Unchecked\\ Seed: Auto\\ //| | |{{:sensitivityanalysismethod.png?400|}}|Go to **Method** tab to open the [[Sensitivity_analysis_method_view|Sensitivity analysis method view]]. Here, the [[Sensitivity_analysis_in_Ecolego#Methods|sensitivity analysis method]] and its settings is selected.\\ \\ //Select the method Probabilistic with the following settings:\\ \\ Base sample size: 1000\\ Sampling type: Latin Hypercube\\ Calculate second order indices: Unchecked\\ Seed: Auto\\ //| |
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| **Modification of parameter values (optional)** | **Modification of parameter values (optional)** |
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| |{{:modificationofparametervalues.png?400|}}|Go to Values to open the Parameter values view. Here, the Parameter values can be reviewed and changed if necessary.\\ \\ //Keep the values set earlier.\\ \\ //| | |{{:modificationofparametervalues.png?400|}}|Go to **Values** to open the Parameter values view. Here, the Parameter values can be reviewed and changed if necessary.\\ \\ //Keep the values set earlier.\\ \\ //| |
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| **Generate parameter samples** | **Generate parameter samples** |
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| |{{:sensitivitytoolboxinputstab8.png?400|}}{{:sensitivitytoolboxinputstab2.png?400|}}|Go to Inputs tab to open the Input samples view. Here, samples for the selected parameters can be generated, inspected and plotted.\\ \\ //Press Generate to generate the input samples.\\ \\ // After successful generation, the tree should show the model inputs and outputs.| | |{{:sensitivitytoolboxinputstab8.png?400|}}{{:sensitivitytoolboxinputstab2.png?400|}}|Go to **Inputs** tab to open the Input samples view. Here, samples for the selected parameters can be generated, inspected and plotted.\\ \\ //Press Generate to generate the input samples.\\ \\ // After successful generation, the tree should show the model inputs and outputs.| |
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| **Simulate model outputs** | **Simulate model outputs** |
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| |{{:sensitivitytoolboxoutputstab1.png?400|}}{{:sensitivitytoolboxoutputstab2.png?400|}}|Go to Outputs tab to open the Output samples view. Here, samples for the model outputs can be simulated, inspected and plotted.\\ \\ //Press Simulate to generate the output samples.//\\ \\ After successful generation, the tree should show the model inputs and outputs.| | |{{:sensitivitytoolboxoutputstab1.png?400|}}{{:sensitivitytoolboxoutputstab2.png?400|}}|Go to **Outputs** tab to open the Output samples view. Here, samples for the model outputs can be simulated, inspected and plotted.\\ \\ //Press Simulate to generate the output samples.//\\ \\ After successful generation, the tree should show the model inputs and outputs.| |
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| {{:sensitivityanalysisresultstoolbar.png?700|}} \\ | {{:sensitivityanalysisresultstoolbar.png?700|}} \\ |
| Go to the Results tab to show the Results view. Here, a shortcut is given to general both inputs and outputs by clicking generate inputs & outputs. In this view, sensitivity and correlation measures can be inspected in charts and/or tables\\ | Go to the **Results** tab to show the Results view. Here, a shortcut is given to general both inputs and outputs by clicking generate inputs & outputs. In this view, sensitivity and correlation measures can be inspected in charts and/or tables\\ |
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| //In the tree, select C<sub>beef</sub> and C<sub>milk</sub>//\\ | //In the tree, select C<sub>beef</sub> and C<sub>milk</sub>//\\ |
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| A [[Correlation_table|Correlation table]] is created for the selected outputs, displaying the sensitivity indices calculated by the probabilistic method.\\ | A [[Correlation_table|Correlation table]] is created for the selected outputs, displaying the sensitivity indices calculated by the probabilistic method. Select the Transformation from the drop down list under the table and put it on Rank.\\ |
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| //Now, select C<sub>beef</sub> and click [[Correlation_pie_chart|Correlation pie chart]]//\\ | //Now, select C<sub>beef</sub> and click [[Correlation_pie_chart|Correlation pie chart]]//\\ |
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| A [[Correlation_pie_chart|Correlation pie chart]] is created and for C<sub>milk</sub> and displays the first order sensitivity index of all parameters on the selected output. The same can be done for C<sub>beef</sub>\\ | A [[Correlation_pie_chart|Correlation pie chart]] is created and for C<sub>milk</sub> and displays the first order sensitivity index of all parameters on the selected output. The same can be done for C<sub>beef</sub>. Right click on the Chart and click on Edit. In the Data tab, select the Transformation and put it on Rank.\\ |
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| As seen in the chart and table, C_beef is most sensitive to TF_beef and C_milk the most sensitive to F_milk (both having a first order index of approximately 0.80. F_air also contribute a first order index of approximately 0.10. All other numbers are zero or very close to zero. The coefficient of determination R2 is close to 1 (0.96) and is an indication that the output samples are nearly linear in terms of the inputs samples. This implies that there should not be any significant contribution of higher order sensitivity indices (measuring interacting parameters). This conclusion is also supported by the very small (2%) //unexplained// part of the pie chart (for a perfect linear relationship, all first order sensitivity indices should sum to 1 leaving zero unexplained part).\\ | As seen in the chart and table, C_beef is most sensitive to TF_beef and C_milk the most sensitive to F_milk (both having a first order index of approximately 0.80. F_air also contribute a first order index of approximately 0.10. All other numbers are zero or very close to zero. The coefficient of determination R2 is close to 1 (0.96) and is an indication that the output samples are nearly linear in terms of the inputs samples. This implies that there should not be any significant contribution of higher order sensitivity indices (measuring interacting parameters). This conclusion is also supported by the very small (2%) //unexplained// part of the pie chart (for a perfect linear relationship, all first order sensitivity indices should sum to 1 leaving zero unexplained part).\\ |
