## Interact with Plots in the Sensitivity Analyzer

This topic shows how to interact with and interpret plots generated in the
**Sensitivity Analyzer** app.

### Parameter Set Plots

After you have generated parameter values for sensitivity analysis, you can plot the generated parameter set. For information about parameter generation, see Generate Parameter Samples for Sensitivity Analysis.

The app displays the generated parameter set and the corresponding parameter set table. The number of rows in the parameter set table correspond to the number of samples you specified. To plot the generated parameters in the app:

Select the generated parameter set in the

**Parameter Sets**area of the app.On the

**Plots**tab, select**Scatter Plot**.Alternatively, right-click the parameter set, and select

**Plot**in the drop-down menu.The diagonal subplots display the histograms of generated parameter values. The off-diagonal subplots are pair-wise scatter plots of the parameters. The number of data points in each scatter plot equals the number of rows in the parameter set table.

You can inspect the histograms to ensure that the generated parameter values match the desired parameter distributions within the constraints of a finite sample size. Inspect the off-diagonal scatter plots to ensure that any specified correlations between parameters are present. For more information, see Inspect the Generated Parameter Set.

To access additional plot features, right-click in the white area of any scatter plot.

You can choose from the following options:

**Variables**— Select the parameters to plot.**Groups**— Select grouping variables for the plots, and configure how the groups are displayed.To select a parameter as a grouping variable, click

**Groups**>**New Grouping Variable**. For example, the following plot is generated when the grouping variable is`Gain`

.The app creates three groups based on low, medium, and high values of the grouping variable. The app computes these grouping values, but you can change them in the

**Manage Groups**dialog box. The second diagonal plot shows the distribution of the gain values in the low (blue), medium (red), and high (yellow) groups. The other diagonal plots show the distribution of the remaining parameters when the corresponding gain value is low, medium, or high. The off-diagonal scatter plots show points belonging to the same group using the same color.You can demarcate the groups based on marker size and marker type instead of color, add more groups corresponding to the grouping variable, and change the grouping values. You can also add more grouping variables. To do so, click

**Groups**>**Manage Groups**.In the

**Manage Groups**dialog box, you configure how the groups are displayed. You can perform tasks such as:Select the plotting

**Style**as either`Color`

,`MarkerSize`

, or`MarkerType`

. In the plots, the app uses the selected style to demarcate the groups corresponding to a grouping variable.Select whether a grouping variable is

**Active**. If a grouping variable is inactive, the scatter plot points are not demarcated in groups corresponding to that variable. To delete a grouping variable, click in the corresponding**Remove**column.Add more grouping variables using the

**Create Grouping Variable**drop-down list.For a grouping variable, specify the range of values for each group in the

**Bin/Value**column. For example, currently the dialog box shows that the`Gain`

values in the groups are:**Low**— below 0.7736**Medium**— 0.7736–0.8265**High**— above 0.8265

To change the

**Low**group values to be 0.79 or lower, type`0.79`

in the corresponding row of the**Bin/Value**column.Add more groups corresponding to a grouping variable. For example, to add a group with values from 0.8265 through 0.9, type

`0.9`

in**New Bin/Value**, and click**Add Group**.

**Upper triangle plot**— Plot the off-diagonal subplots above the diagonal in addition to the existing plots.**Marginal Box Plots**— Requires Statistics and Machine Learning Toolbox™ software. Plot box plots for each of the parameters in the parameter set, and choose the position of the plots.**Histograms**— Plot the probability distribution of the parameters, and choose the position of the plots.**Kernel Density Plots**— Requires Statistics and Machine Learning Toolbox software. Plot the probability distribution of the parameters using a kernel density estimator, and choose the position of the plots. For more information, see Kernel Distribution (Statistics and Machine Learning Toolbox).**Overlay linear fit**— Plot the best-fit line on the scatter subplots. You can choose to plot the best-fit lines for one, all, a row, or a column of scatter subplots.**Enable brushing/data selection**— Enable selection of data points in the scatter subplots.When you highlight parameter values in one plot, the values corresponding to other parameters from the same row in the parameter set table are also highlighted. In addition, the rows in the parameter set table that correspond to these values are highlighted.

To remove the highlighting, invert the selection to all other data points in the plot, or disable the feature, right-click the highlighted data points, and choose from the context-menu.

**Pop-out plot**— View a subplot in a new window.

### Requirement Plots

After you have specified design requirements, you can plot the requirements and associated model response. For information about specifying the requirements, see Specify Time-Domain Requirements and Specify Frequency-Domain Requirements.

The specified requirements are displayed in the **Requirements**
area of the app. To plot the requirement in the app, right-click the requirement,
and select **Plot**.

Alternatively, select the requirement, and in the **Plots** tab
of the app, select the plot type. A plot is generated and a new tab associated with
the plot appears in the app. In the new tab, you can perform additional tasks such
as preprocessing imported
data (for signal matching requirement only), zooming, and plotting the
associated model response. The model response is the signal or system on which the
requirement is applied. The response is plotted using the parameter values specified
in the model workspace and is not updated during evaluation.

### Evaluated Result Scatter Plots

After you have evaluated your design requirements, an evaluation results table lists the samples in the parameter set and the corresponding evaluated requirement (cost function) values. For requirements that involve a bound, a positive requirement value indicates that your requirement was violated, while a negative value indicates that the requirement was satisfied for that sample of parameter values.

An evaluation result plot is also generated. The scatter subplots display the evaluated requirement (cost function value) as a function of each parameter in the parameter set. The number of points in each scatter plot equals the number of rows in the parameter set. The last column of subplots displays histograms of the probability distribution of the evaluated cost function values.

Use this plot to visually analyze the relation
between parameters and requirements. For example, in this case, the
`SignalMatching`

requirement looks monotonically related to the
`Gain`

parameter.

You can also plot best-fit lines on the scatter subplots. To do so, and to access additional plot features, right-click in the white area of any scatter subplot.

You can choose from the following options:

**X-Variables**— Select the parameters and requirements to use as x-variables in the scatter subplots.**Y-Variables**— Select the parameters and requirements to use as y-variables in the scatter subplots.**Grouping**— Select grouping variables for the subplots, and configure how the groups are displayed.To select a parameter or evaluated requirement as a grouping variable, click

**Groups**>**New Grouping Variable**. For example, the following plot is generated when the grouping variable is`Gain`

. The app creates three groups based on low, medium, and high values of the grouping variable. The app computes these grouping values, but you can change them in the**Manage Groups**dialog box. The scatter subplots display the evaluated requirement values when the corresponding gain value is low (blue), medium (red), and high (yellow). The histogram plots the probability distribution of the evaluated requirement corresponding to the groups.You can demarcate the groups based on marker size and marker type instead of color, add more groups corresponding to the grouping variable, and change the grouping values. You can also add more grouping variables. To do so, click

**Groups**>**Manage Groups**. For more information, see Parameter Set Plots.**Marginal Box Plots**— Requires Statistics and Machine Learning Toolbox software. Plot box plots for each of the parameters in the parameter set, and choose the position of the plots.**Histograms**— Plot the probability distribution of the parameters, and choose the position of the plots.**Kernel Density Plots**— Requires Statistics and Machine Learning Toolbox software. Plot the probability distribution of the parameters using a kernel density estimator, and choose the position of the plots. For more information, see Kernel Distribution (Statistics and Machine Learning Toolbox).**Overlay linear fit**— Plot the best-fit line on the scatter subplots. You can choose to plot the best-fit lines for one, all, a row, or a column of scatter plots.**Enable brushing/data selection**— Enable selection of data points in the scatter subplots.When you highlight parameter values in one scatter subplot, the values corresponding to other parameters from the same row in the evaluated results table are also highlighted. In addition, the rows in the evaluated results table that correspond to these values are highlighted.

To remove the highlighting, invert the selection to all other data points in the plot, or disable the feature, right-click the highlighted data points, and choose from the context-menu.

**Pop-out plot**— View a subplot in a new window.

### Evaluated Result Contour Plots

After you have evaluated your design requirements, an evaluation results table lists the samples in the parameter set and the corresponding evaluated requirement (cost function) values. For information about evaluation, see Evaluate Design Requirements.

You can plot a contour plot of the evaluated results. To do so, select the
evaluated result in the **Results** area of the app, and choose a
contour plot in the **Plots** tab of the app.

Use this plot to visually analyze the relation between parameters and design
requirements. Select the parameters to plot in the **X parameter**
and **Y parameter** drop-down lists. The evaluated requirement
value is plotted as a function of these parameters.

### Statistical Analysis Tornado Plots

After you have evaluated the design requirements for each parameter, you can
perform statistical analysis to analyze how the parameters of your Simulink^{®} model influence the requirements.

To generate a tornado plot ranking the influence of parameters on requirements:

In the

**Statistics**tab of the app, select the evaluation results you want to analyze in the**Evaluation Results to Analyze**list.Specify the statistical analysis methods.

You can choose to calculate a correlation coefficient, standardized regression coefficient, and partial correlation coefficient (requires Statistics and Machine Learning Toolbox software).

For more information, see Analyze Relation Between Parameters and Design Requirements.

For each of these methods, specify what data to use for the analysis. You can choose from

**Linear**(Pearson),**Ranked**(Spearman), and**Kendall**analysis types.**Kendall**is applicable when the analysis method is**Correlation**, and requires Statistics and Machine Learning Toolbox software.You can compute all applicable combinations of analysis methods and types.

Calculate the coefficients, and generate a tornado plot.

Click

**Compute Statistics**.

The resulting tornado plot displays the calculated coefficients for each specified analysis method and type. The coefficients are plotted in order of influence of parameters on the cost function. The parameter with the greatest magnitude of influence on the cost function is displayed on the top, giving the plot a tornado shape. When more than one type of coefficient is calculated, the tornado plot sorts the parameters based on the first calculated coefficient. The coefficients are calculated in the following order:

Correlation

Rank correlation

Kendall correlation

Partial correlation

Rank partial correlation

Standardized regression

Rank Standardized Regression

In this tornado plot, the parameters are sorted based on the Correlation
coefficient. For all calculated coefficients, the `Gain`

parameter
has the most influence on the design requirement cost function.