Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To analyze how the parameters and states (collectively referred
to as *parameters*) of a Simulink^{®} model
influence the design requirement on the model signals, you first generate
samples of the parameters. You then define the cost function by creating
a design requirement on the model signals, and evaluate the cost function
for each sample. Finally, you analyze the relationship between the
parameter variations and the cost function values. You can perform
this analysis in the following ways:

View a plot of the cost function evaluations against the parameter samples to identify trends. This method is informal and provides visual intuition about how the various parameters affect the cost function.

In the Sensitivity Analysis tool, after the evaluation is complete,
an evaluation result scatter plot is generated in the tool. The plot
displays the evaluated cost function value as a function of each parameter
in the parameter set. The last column subplot displays the probability
distribution of the evaluated cost function values. You can add a
best-fit line to the scatter subplots by right-clicking in the plot,
and selecting **Overlay linear fit** in the
context menu. In this plot, the best-fit line indicates that the `Gain`

parameter
has a lot of influence on the requirement.

You can also plot a contour plot of the evaluated results. To learn more about these plots, see Interact with Plots in the Sensitivity Analysis Tool. For an example, see Identify Key Parameters for Estimation (GUI).

At the command line, you can use tools such as:

`sdo.scatterPlot`

— Scatter plot of the parameter samples against the cost function evaluation`surf`

,`mesh`

,`contour`

— 3-D plot of samples of two parameters against the cost function evaluation

For an example, see Identify Key Parameters for Estimation (Code).

In addition to visually analyzing the effect of parameters on the cost function, you can also compute statistics to quantify the relation.

Obtain summary statistics about the relationship between cost function evaluations and parameters samples. Available analysis methods include:

Method | Description |
---|---|

Correlation | Use to analyze how a model parameter and the cost function output are correlated. |

Partial Correlation (requires Statistics and Machine Learning Toolbox™ software) | Use to analyze how a model parameter and the cost function are correlated, removing the effects of the remaining parameters. |

Standardized Regression | Use when you expect that the model parameters linearly influence the cost function. |

For each of these methods, you specify what data to use for the analysis by choosing from the following analysis types:

Linear analysis, also referred to as

*Pearson*analysis — Uses raw data for analysis. Use linear analysis when you expect a linear relation between the parameters and cost function, and when the residuals about the best-fit line are expected to be normally distributed. Linear analysis is also recommended when the number of samples, and so the number of residual points is large.Ranked analysis, also referred to as

*Spearman*analysis and*ranked transformation*— Uses ranks of data for analysis. Use ranked analysis when you expect a nonlinear monotonic relation between the parameters and the cost function and when the residuals about the best-fit line are not normally distributed. Ranked analysis is also recommended when the number of samples, and so the number of residual points is small.Linear analysis retains information about intervals between data values, whereas ranked analysis does not. Suppose that you had the following data set:

x _{1}x _{2}y 9 20 340 5 60 106 2.3 50.4 870.5 Here

*x*and_{1}*x*are model parameters, and_{2}*y*is the cost function. Each row represents a sample and the associated cost function evaluation.The data is ranked on a per column basis. For example, when you rank the data in column 1 (

*x*), which contains the entries 9, 5, and 2.3, the ranked data is equal to 3, 2, and 1. The ranked data set for the samples of_{1}*x*,_{1}*x*and_{2}*y*are as follows:x _{1}x _{2}y 3 1 2 2 3 1 1 2 3 The ranked data set can be used for correlation, partial correlation, or standardized regression analysis.

Kendall — Kendall’s tau rank correlation coefficient is calculated.

Applicable when the analysis method is Correlation. Requires Statistics and Machine Learning Toolbox software.

Calculates the correlation coefficients, *R*.
Use this method to analyze how a model parameter and the cost function
outputs are correlated.

*R* is calculated as follows:

$$\begin{array}{ll}R(i,j)\hfill & =\frac{C(i,j)}{\sqrt{C(i,i)C(j,j)}}\hfill \\ C\hfill & =cov(x,y)\hfill \\ \hfill & =E[(x-{\mu}_{x})(y-{\mu}_{y})]\hfill \\ {\mu}_{x}\hfill & =E[x]\hfill \\ {\mu}_{y}\hfill & =E[y]\hfill \end{array}$$

`x`

contains *Ns* samples
of *Np* model parameters. `y`

contains *Ns* rows,
each row corresponds to the cost function evaluation for a sample
in `x`

.

*R* values are in the [-1 1] range. The (*i*,*j*)
entry of *R* indicates the correlation between *x*(*i*)
and *y*(*j*).

`R(i,j) > 0`

— Variables have positive correlation. The variables increase together.`R(i,j) = 0`

— Variables have no correlation.`R(i,j) < 0`

— Variables have negative correlation. As one variable increases, the other decreases.

Calculates the partial correlation coefficients, *R*.
This method requires Statistics and Machine
Learning Toolbox software. Use this method
to analyze how a model parameter and the cost function are correlated,
adjusting to remove the effect of the other parameters.

*R* is calculated using `partialcorri`

from
the Statistics and Machine
Learning Toolbox software.

Calculates the standardized regression coefficients, *R*.
Use this method when you expect that the model parameters linearly
influence the cost function.

*R* is calculated as follows:

$$R={b}_{x}\frac{{\sigma}_{x}}{{\sigma}_{y}}$$

Consider a single sample (*x _{1}*,...,

In the Sensitivity Analysis tool, after you have evaluated the
design requirements, specify the analysis methods and types
in the **Statistics** tab of the tool.

Select the evaluation results you want to analyze in the **Evaluation
Results to Analyze** list. After that, you specify the analysis
methods and types, and click **Compute Statistics**.
You can compute all applicable combinations of analysis methods and
types.

The results of the analysis are returned in the `StatsResult`

variable,
in the **Results** area of the tool. In this case,
the `StatsResult`

variable includes the linear (Pearson)
correlation coefficients and linear standardized regression coefficients
calculated between the cost function and each parameter. To see the
coefficients, right-click `StatsResult`

, and select **Open** in
the context-menu.

A tornado plot is generated that displays the results of the
analysis in order of influence of parameters on the cost function.
The parameter that most influences the cost function is displayed
on the top. As was seen in the results scatter plot, in this tornado
plot the `Gain`

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

To learn more about tornado plots, see Interact with Plots in the Sensitivity Analysis Tool. For an example, see Identify Key Parameters for Estimation (GUI).

At the command line, specify the analysis methods and types
using `sdo.analyze`

. This function
performs linear correlation analysis by default. To specify other
analysis methods, use `sdo.AnalyzeOptions`

. For an example, see Identify Key Parameters for Estimation (Code).

`sdo.AnalyzeOptions`

| `sdo.analyze`

| `sdo.evaluate`

| `sdo.sample`