gpc_sobol_sensitivities
- pyapprox.analysis.gpc_sobol_sensitivities(pce, variable, max_order=2)[source]
Compute variance based sensitivity metrics from a polynomial chaos expansion
- Parameters:
- pce :class:`pyapprox.surrogates.polychaos.gpc.PolynomialChaosExpansion`
The polynomial chaos expansion
- max_orderinteger
The maximum interaction order of Sobol indices to compute. A value of 2 will compute all pairwise interactions, a value of 3 will compute indices for all interactions involving 3 variables. The number of indices returned will be nchoosek(nvars+max_order,nvars). Warning when nvars is high the number of indices will increase rapidly with max_order.
- Returns:
- result
pyapprox.analysis.sensitivity_analysis.SensitivityResult Result object with the following attributes
- main_effectsnp.ndarray (nvars, nqoi)
The variance based main effect sensitivity indices
- total_effectsnp.ndarray (nvars, nqoi)
The variance based total effect sensitivity indices
- sobol_indicesnp.ndarray (nchoosek(nvars+max_order,nvars), nqoi)
The variance based Sobol sensitivity indices
- sobol_interaction_indicesnp.ndarray(nvars, nchoosek(nvars+max_order, nvars))
Indices specifying the variables in each interaction in
sobol_indices
- result