The Nonlinear ARX plot displays the characteristics of model
nonlinearities as a function of one or two regressors. The model nonlinearity
(`model.Nonlinearity`

) is a nonlinearity estimator
function, such as `wavenet`

, `sigmoidnet`

, `treepartition`

,
and uses model regressors as its inputs. The value of the nonlinearity
is plotted by projecting its response in 2 or 3-dimensional space.
The plot uses one or two regressors as the plot axes for 2- or 3-D
plots, respectively and a center point (cross-section location) for
the other regressors.

Examining a nonlinear ARX plot can help you gain insight into
which regressors have the strongest effect on the model output. Understanding
the relative importance of the regressors on the output can help you
decide which regressors should be included in the nonlinear function.

Furthermore, you can create several nonlinear models for the
same data set using different nonlinearity estimators, such a `wavenet`

network
and `treepartition`

, and then compare the nonlinear
surfaces of these models. Agreement between nonlinear surfaces increases
the confidence that these nonlinear models capture the true dynamics
of the system.

In the plot window, you can choose:

The regressors to use on the plot axes, and specify
the center points for the other regressors in the configuration panel. For
multi-output models, each output is plotted separately.

The output to view from the drop-down list located
at the top of the plot.

To learn more about configuring the plot, see Tips.