|Perform global sensitivity analysis by computing first- and total-order Sobol indices (requires Statistics and Machine Learning Toolbox)|
|Perform multiparametric global sensitivity analysis (requires Statistics and Machine Learning Toolbox)|
|Simulate SimBiology model|
|Prepare model object for accelerated simulations|
|Create SimFunction object|
|Generate parameters by sampling covariate model (requires Statistics and Machine Learning Toolbox software)|
|Sample error based on error model and add noise to simulation data|
|Create configuration set object and add to model object|
|Get configuration set object from model object|
|Get 3-D sensitivity matrix from |
|Object containing first- and total-order Sobol indices|
|Object containing multiparametric global sensitivity analysis (MPGSA) results|
|Specify sensitivity analysis options|
|Solver settings information for model simulation|
|Specify model solver options|
|Options for logged species|
|Dimensional analysis and unit conversion options|
|Function-like interface to execute SimBiology models|
|SimFunctionSensitivity object, subclass of |
Perform sensitivity analysis to find important model parameters.
This example uses the model described in Model of the Yeast Heterotrimeric G Protein Cycle to illustrate SimBiology® sensitivity analysis options.
Sensitivity analysis lets you explore the effects of variations in model quantities (species, compartments, and parameters) on a model response.
Simulate dynamic models using various solvers.
Accelerate the simulation or analysis by converting the model to compiled C code.