The Sensitivity Analysis Library provides functions that help you create sensitivity analyses of models containing array-valued inputs.

The functions in this library allow you to compute the sensitivity of a result to each cell of each array-valued uncertain input. It is similar to Make Importance (on the Object menu), except that Make Importance varies each chance array input as a whole, while the functions in this library vary each individual cell of array-valued input variables.

Download

To use, download the library from Sensitivity Analysis Library.

The Sensitivity Functions Examples.ana model demonstrates the use of this library.

Webinar on Using It

You can watch recordings of two webinars that cover the topic of setting up and graphing sensitivity analyses:

[1]
[2]

The first webinar is a pre-requisite for the second. The last part of the second webinar introduces this library and demonstrates its usage.

How to Use

The library looks for chance nodes that are ancestors of the result being analyzed. You will want to make sure that the inputs you want to vary are defined as chance nodes, and are probabilistic. See the above diagram as an example.

To implement a sensitivity analysis based on this library, you will typically add four nodes to your model. In the above diagram, these are Importance_details, Input_number, Input_descriptor, and Importance graph. Each of these four is defined using a function from the library.

Importance_details does almost all the work. It locates all the chance input variables, flattens them to the cell level, assembles their input values, and computes the sensitivity of the result to each of input cells.
Input_number: Because the number of input cells is likely to be huge, we usually want to restrict attention to a small subset of input cells -- those input cells that the output is most sensitive to. Inside Input_number, we specify how many inputs we want to include. The result is just a sequence 1..N.
Input_descriptor: Assembles meaningful names for each input cell. To meaningfully identify each input cell, we need to indicate the variable name along with the coordinate of the cell. These input names use a Subscript-like description.
Importance_graph: This is the final result graph.