SALib.analyze.radial_ee module#

SALib.analyze.radial_ee.analyze(problem: Dict, X: array, Y: array, sample_sets: int, num_resamples: int = 100, conf_level: float = 0.95, print_to_console: bool = False, seed: int | None = None) → Dict[source]#

Radial Elementary Effects Analysis.

Calculates mu, mu_star, sigma and mu_star_conf as with Morris OAT.

  • mu metric indicates the mean of the distribution

  • mu_star metric indicates the mean of the distribution of absolute values

  • sigma is the standard deviation of the distribution

Notes

Compatible with:

References

[1]

Campolongo, F., Saltelli, A., Cariboni, J., 2011. From screening to quantitative sensitivity analysis: A unified approach. Computer Physics Communications 182, 978–988. https://www.sciencedirect.com/science/article/pii/S0010465510005321 DOI: 10.1016/j.cpc.2010.12.039

[2]

Campolongo, F., Cariboni, J., Saltelli, A., 2007. An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software 22, 1509–1518. https://doi.org/10.1016/j.envsoft.2006.10.004

Parameters:

  • problem (dict) – The SALib problem specification

  • X (np.array) – An array containing the model inputs of dtype=float

  • Y (np.array) – An array containing the model outputs of dtype=float

  • sample_sets (int) – The number of sample sets used to create result set Y

  • num_resamples (int) – The number of resamples to calculate mu_star_conf (default 100)

  • conf_level (float) – The confidence interval level (default 0.95)

  • print_to_console (bool) – Print results directly to console (default False)

  • seed (int) – Seed value to use for np.random.seed

Returns:

Si

Return type:

dict