Perform multiparametric global sensitivity analysis (requires Statistics and Machine Learning Toolbox)
performs a multiparametric global sensitivity analysis (MPGSA) [1] of
mpgsaResults
= sbiompgsa(modelObj
,params
,classifiers
)classifiers
with respect to model parameters
params
on a SimBiology model modelObj
.
params
are model quantities (sensitivity inputs) and
classifiers
define the expressions of model responses (model
outputs).
uses parameter mpgsaResults
= sbiompgsa(modelObj
,samples
,classifiers
)samples
to perform a multiparametric global sensitivity
analysis of classifiers
.
uses model simulation data mpgsaResults
= sbiompgsa(simdata
,samples
,classifiers
)simdata
to perform a multiparametric global
sensitivity analysis of classifiers
.
uses additional options specified by one or more name-value pair arguments. Available
name-value pairs differ depending on which syntax you are using.mpgsaResults
= sbiompgsa(___,Name,Value
)
[1] Tiemann, Christian A., Joep Vanlier, Maaike H. Oosterveer, Albert K. Groen, Peter A. J. Hilbers, and Natal A. W. van Riel. “Parameter Trajectory Analysis to Identify Treatment Effects of Pharmacological Interventions.” Edited by Scott Markel. PLoS Computational Biology 9, no. 8 (August 1, 2013): e1003166. https://doi.org/10.1371/journal.pcbi.1003166.
Observable
| sbiosobol
| SimBiology.gsa.MPGSA
| ecdf
(Statistics and Machine Learning Toolbox) | kstest2
(Statistics and Machine Learning Toolbox)