Concise API Reference

This page documents the sensitivity analysis methods supported by SALib.

FAST - Fourier Amplitude Sensitivity Test

SALib.sample.fast_sampler.sample(problem, N, M=4, seed=None)[source]

Generate model inputs for the extended Fourier Amplitude Sensitivity Test (eFAST).

Returns a NumPy matrix containing the model inputs required by the extended Fourier Amplitude sensitivity test. The resulting matrix contains N * D rows and D columns, where D is the number of parameters. The samples generated are intended to be used by SALib.analyze.fast.analyze().

Parameters
  • problem (dict) – The problem definition

  • N (int) – The number of samples to generate

  • M (int) – The interference parameter, i.e., the number of harmonics to sum in the Fourier series decomposition (default 4)

References

1

Cukier, R.I., Fortuin, C.M., Shuler, K.E., Petschek, A.G., Schaibly, J.H., 1973. Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I theory. Journal of Chemical Physics 59, 3873–3878. https://doi.org/10.1063/1.1680571

2

Saltelli, A., S. Tarantola, and K. P.-S. Chan (1999). “A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output.” Technometrics, 41(1):39-56, doi:10.1080/00401706.1999.10485594.

SALib.analyze.fast.analyze(problem, Y, M=4, num_resamples=100, conf_level=0.95, print_to_console=False, seed=None)[source]

Performs the extended Fourier Amplitude Sensitivity Test (eFAST) on model outputs.

Returns a dictionary with keys ‘S1’ and ‘ST’, where each entry is a list of size D (the number of parameters) containing the indices in the same order as the parameter file.

Notes

Compatible with:

fast_sampler : SALib.sample.fast_sampler.sample()

Parameters
  • problem (dict) – The problem definition

  • Y (numpy.array) – A NumPy array containing the model outputs

  • M (int) – The interference parameter, i.e., the number of harmonics to sum in the Fourier series decomposition (default 4)

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

References

1

Cukier, R. I., C. M. Fortuin, K. E. Shuler, A. G. Petschek, and J. H. Schaibly (1973). “Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients.” J. Chem. Phys., 59(8):3873-3878, doi:10.1063/1.1680571.

2

Saltelli, A., S. Tarantola, and K. P.-S. Chan (1999). “A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output.” Technometrics, 41(1):39-56, doi:10.1080/00401706.1999.10485594.

3

Pujol, G. (2006) fast99 - R sensitivity package https://github.com/cran/sensitivity/blob/master/R/fast99.R

Examples

>>> X = fast_sampler.sample(problem, 1000)
>>> Y = Ishigami.evaluate(X)
>>> Si = fast.analyze(problem, Y, print_to_console=False)

RBD-FAST - Random Balance Designs Fourier Amplitude Sensitivity Test

SALib.sample.latin.sample(problem, N, seed=None)[source]

Generate model inputs using Latin hypercube sampling (LHS).

Returns a NumPy matrix containing the model inputs generated by Latin hypercube sampling. The resulting matrix contains N rows and D columns, where D is the number of parameters.

Parameters
  • problem (dict) – The problem definition

  • N (int) – The number of samples to generate

References

1

McKay, M.D., Beckman, R.J., Conover, W.J., 1979. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245. https://doi.org/10.2307/1268522

2

Iman, R.L., Helton, J.C., Campbell, J.E., 1981. An Approach to Sensitivity Analysis of Computer Models: Part I—Introduction, Input Variable Selection and Preliminary Variable Assessment. Journal of Quality Technology 13, 174–183. https://doi.org/10.1080/00224065.1981.11978748

SALib.analyze.rbd_fast.analyze(problem, X, Y, M=10, num_resamples=100, conf_level=0.95, print_to_console=False, seed=None)[source]

Performs the Random Balanced Design - Fourier Amplitude Sensitivity Test (RBD-FAST) on model outputs.

Returns a dictionary with keys ‘S1’, where each entry is a list of size D (the number of parameters) containing the indices in the same order as the parameter file.

Notes

Compatible with:

all samplers

Parameters
  • problem (dict) – The problem definition

  • X (numpy.array) – A NumPy array containing the model inputs

  • Y (numpy.array) – A NumPy array containing the model outputs

  • M (int) – The interference parameter, i.e., the number of harmonics to sum in the Fourier series decomposition (default 10)

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

References

1

S. Tarantola, D. Gatelli and T. Mara (2006) “Random Balance Designs for the Estimation of First Order Global Sensitivity Indices”, Reliability Engineering and System Safety, 91:6, 717-727

2

Elmar Plischke (2010) “An effective algorithm for computing global sensitivity indices (EASI) Reliability Engineering & System Safety”, 95:4, 354-360. doi:10.1016/j.ress.2009.11.005

3

Jean-Yves Tissot, Clémentine Prieur (2012) “Bias correction for the estimation of sensitivity indices based on random balance designs.”, Reliability Engineering and System Safety, Elsevier, 107, 205-213. doi:10.1016/j.ress.2012.06.010

4

Jeanne Goffart, Mickael Rabouille & Nathan Mendes (2015) “Uncertainty and sensitivity analysis applied to hygrothermal simulation of a brick building in a hot and humid climate”, Journal of Building Performance Simulation. doi:10.1080/19401493.2015.1112430

Examples

>>> X = latin.sample(problem, 1000)
>>> Y = Ishigami.evaluate(X)
>>> Si = rbd_fast.analyze(problem, X, Y, print_to_console=False)

Method of Morris

SALib.sample.morris.sample(problem: Dict, N: int, num_levels: int = 4, optimal_trajectories: Optional[int] = None, local_optimization: bool = True, seed: Optional[int] = None) numpy.ndarray[source]

Generate model inputs using the Method of Morris

Returns a NumPy matrix containing the model inputs required for Method of Morris. The resulting matrix has \((G+1)*T\) rows and \(D\) columns, where \(D\) is the number of parameters, \(G\) is the number of groups (if no groups are selected, the number of parameters). \(T\) is the number of trajectories \(N\), or optimal_trajectories if selected. These model inputs are intended to be used with SALib.analyze.morris.analyze().

Parameters
  • problem (dict) – The problem definition

  • N (int) – The number of trajectories to generate

  • num_levels (int, default=4) – The number of grid levels (should be even)

  • optimal_trajectories (int) – The number of optimal trajectories to sample (between 2 and N)

  • local_optimization (bool, default=True) – Flag whether to use local optimization according to Ruano et al. (2012) Speeds up the process tremendously for bigger N and num_levels. If set to False brute force method is used, unless gurobipy is available

  • seed (int) – Seed to generate a random number

Returns

sample_morris – Returns a numpy.ndarray containing the model inputs required for Method of Morris. The resulting matrix has \((G/D+1)*N/T\) rows and \(D\) columns, where \(D\) is the number of parameters.

Return type

numpy.ndarray

References

1

Ruano, M.V., Ribes, J., Seco, A., Ferrer, J., 2012. An improved sampling strategy based on trajectory design for application of the Morris method to systems with many input factors. Environmental Modelling & Software 37, 103–109. https://doi.org/10.1016/j.envsoft.2012.03.008

2

Morris, M.D., 1991. Factorial Sampling Plans for Preliminary Computational Experiments. Technometrics 33, 161–174. https://doi.org/10.1080/00401706.1991.10484804

3

Campolongo, F., Cariboni, J., Saltelli, A., 2007. An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software, Modelling, computer-assisted simulations, and mapping of dangerous phenomena for hazard assessment 22, 1509–1518. https://doi.org/10.1016/j.envsoft.2006.10.004

SALib.analyze.morris.analyze(problem: Dict, X: numpy.ndarray, Y: numpy.ndarray, num_resamples: int = 100, conf_level: float = 0.95, print_to_console: bool = False, num_levels: int = 4, seed=None) numpy.ndarray[source]

Perform Morris Analysis on model outputs.

Returns a dictionary with keys ‘mu’, ‘mu_star’, ‘sigma’, and ‘mu_star_conf’, where each entry is a list of parameters containing the indices in the same order as the parameter file.

Notes

Compatible with:

morris : SALib.sample.morris.sample()

Parameters
  • problem (dict) – The problem definition

  • X (numpy.array) – The NumPy matrix containing the model inputs of dtype=float

  • Y (numpy.array) – The NumPy array containing the model outputs of dtype=float

  • num_resamples (int) – The number of resamples used to compute the confidence intervals (default 1000)

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

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

  • num_levels (int) – The number of grid levels, must be identical to the value passed to SALib.sample.morris (default 4)

  • seed (int) – Seed to generate a random number

Returns

Si – A dictionary of sensitivity indices containing the following entries.

  • mu - the mean elementary effect

  • mu_star - the absolute of the mean elementary effect

  • sigma - the standard deviation of the elementary effect

  • mu_star_conf - the bootstrapped confidence interval

  • names - the names of the parameters

Return type

dict

References

1

Morris, M. (1991). “Factorial Sampling Plans for Preliminary Computational Experiments.” Technometrics, 33(2):161-174, doi:10.1080/00401706.1991.10484804.

2

Campolongo, F., J. Cariboni, and A. Saltelli (2007). “An effective screening design for sensitivity analysis of large models.” Environmental Modelling & Software, 22(10):1509-1518, doi:10.1016/j.envsoft.2006.10.004.

Examples

>>> X = morris.sample(problem, 1000, num_levels=4)
>>> Y = Ishigami.evaluate(X)
>>> Si = morris.analyze(problem, X, Y, conf_level=0.95,
>>>                     print_to_console=True, num_levels=4)

Sobol’ Sensitivity Analysis

SALib.sample.saltelli.sample(problem: Dict, N: int, calc_second_order: bool = True, skip_values: Optional[int] = None)[source]

Generates model inputs using Saltelli’s extension of the Sobol’ sequence.

The Sobol’ sequence is a popular quasi-random low-discrepancy sequence used to generate uniform samples of parameter space.

Returns a NumPy matrix containing the model inputs using Saltelli’s sampling scheme. Saltelli’s scheme extends the Sobol’ sequence in a way to reduce the error rates in the resulting sensitivity index calculations. If calc_second_order is False, the resulting matrix has N * (D + 2) rows, where D is the number of parameters. If calc_second_order is True, the resulting matrix has N * (2D + 2) rows. These model inputs are intended to be used with SALib.analyze.sobol.analyze().

Notes

The initial points of the Sobol’ sequence has some repetition (see Table 2 in Campolongo [1]), which can be avoided by setting the skip_values parameter. Skipping values reportedly improves the uniformity of samples. It has been shown, however, that naively skipping values may reduce accuracy, increasing the number of samples needed to achieve convergence (see Owen [2]).

A recommendation adopted here is that both skip_values and N be a power of 2, where N is the desired number of samples (see [2] and discussion in [5] for further context). It is also suggested therein that skip_values >= N.

The method now defaults to setting skip_values to a power of two that is >= N. If skip_values is provided, the method now raises a UserWarning in cases where sample sizes may be sub-optimal according to the recommendation above.

Parameters
  • problem (dict) – The problem definition

  • N (int) – The number of samples to generate. Ideally a power of 2 and <= skip_values.

  • calc_second_order (bool) – Calculate second-order sensitivities (default True)

  • skip_values (int or None) – Number of points in Sobol’ sequence to skip, ideally a value of base 2 (default: a power of 2 >= N, or 16; whichever is greater)

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://doi.org/10.1016/j.cpc.2010.12.039

2

Owen, A. B., 2020. On dropping the first Sobol’ point. arXiv:2008.08051 [cs, math, stat]. Available at: http://arxiv.org/abs/2008.08051 (Accessed: 20 April 2021).

3

Saltelli, A., 2002. Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications 145, 280–297. https://doi.org/10.1016/S0010-4655(02)00280-1

4

Sobol’, I.M., 2001. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation, The Second IMACS Seminar on Monte Carlo Methods 55, 271–280. https://doi.org/10.1016/S0378-4754(00)00270-6

5

Discussion: https://github.com/scipy/scipy/pull/10844 https://github.com/scipy/scipy/pull/10844#issuecomment-672186615 https://github.com/scipy/scipy/pull/10844#issuecomment-673029539

SALib.analyze.sobol.analyze(problem, Y, calc_second_order=True, num_resamples=100, conf_level=0.95, print_to_console=False, parallel=False, n_processors=None, keep_resamples=False, seed=None)[source]

Perform Sobol Analysis on model outputs.

Returns a dictionary with keys ‘S1’, ‘S1_conf’, ‘ST’, and ‘ST_conf’, where each entry is a list of size D (the number of parameters) containing the indices in the same order as the parameter file. If calc_second_order is True, the dictionary also contains keys ‘S2’ and ‘S2_conf’.

Notes

Compatible with:

saltelli : SALib.sample.saltelli.sample()

Parameters
  • problem (dict) – The problem definition

  • Y (numpy.array) – A NumPy array containing the model outputs

  • calc_second_order (bool) – Calculate second-order sensitivities (default True)

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

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

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

  • keep_resamples (bool) – Whether or not to store intermediate resampling results (default False)

References

1

Sobol, I. M. (2001). “Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates.” Mathematics and Computers in Simulation, 55(1-3):271-280, doi:10.1016/S0378-4754(00)00270-6.

2

Saltelli, A. (2002). “Making best use of model evaluations to compute sensitivity indices.” Computer Physics Communications, 145(2):280-297, doi:10.1016/S0010-4655(02)00280-1.

3

Saltelli, A., P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and S. Tarantola (2010). “Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index.” Computer Physics Communications, 181(2):259-270, doi:10.1016/j.cpc.2009.09.018.

Examples

>>> X = saltelli.sample(problem, 512)
>>> Y = Ishigami.evaluate(X)
>>> Si = sobol.analyze(problem, Y, print_to_console=True)

Delta Moment-Independent Measure

SALib.sample.latin.sample(problem, N, seed=None)[source]

Generate model inputs using Latin hypercube sampling (LHS).

Returns a NumPy matrix containing the model inputs generated by Latin hypercube sampling. The resulting matrix contains N rows and D columns, where D is the number of parameters.

Parameters
  • problem (dict) – The problem definition

  • N (int) – The number of samples to generate

References

1

McKay, M.D., Beckman, R.J., Conover, W.J., 1979. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245. https://doi.org/10.2307/1268522

2

Iman, R.L., Helton, J.C., Campbell, J.E., 1981. An Approach to Sensitivity Analysis of Computer Models: Part I—Introduction, Input Variable Selection and Preliminary Variable Assessment. Journal of Quality Technology 13, 174–183. https://doi.org/10.1080/00224065.1981.11978748

SALib.analyze.delta.analyze(problem: Dict, X: numpy.ndarray, Y: numpy.ndarray, num_resamples: int = 100, conf_level: float = 0.95, print_to_console: bool = False, seed: Optional[int] = None) Dict[source]

Perform Delta Moment-Independent Analysis on model outputs.

Returns a dictionary with keys ‘delta’, ‘delta_conf’, ‘S1’, and ‘S1_conf’, where each entry is a list of size D (the number of parameters) containing the indices in the same order as the parameter file.

Notes

Compatible with:

all samplers

Parameters
  • problem (dict) – The problem definition

  • X (numpy.matrix) – A NumPy matrix containing the model inputs

  • Y (numpy.array) – A NumPy array containing the model outputs

  • num_resamples (int) – The number of resamples when computing confidence intervals (default 10)

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

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

References

1

Borgonovo, E. (2007). “A new uncertainty importance measure.” Reliability Engineering & System Safety, 92(6):771-784, doi:10.1016/j.ress.2006.04.015.

2

Plischke, E., E. Borgonovo, and C. L. Smith (2013). “Global sensitivity measures from given data.” European Journal of Operational Research, 226(3):536-550, doi:10.1016/j.ejor.2012.11.047.

Examples

>>> X = latin.sample(problem, 1000)
>>> Y = Ishigami.evaluate(X)
>>> Si = delta.analyze(problem, X, Y, print_to_console=True)

Derivative-based Global Sensitivity Measure (DGSM)

SALib.analyze.dgsm.analyze(problem, X, Y, num_resamples=100, conf_level=0.95, print_to_console=False, seed=None)[source]

Calculates Derivative-based Global Sensitivity Measure on model outputs.

Returns a dictionary with keys ‘vi’, ‘vi_std’, ‘dgsm’, and ‘dgsm_conf’, where each entry is a list of size D (the number of parameters) containing the indices in the same order as the parameter file.

Notes

Compatible with:

finite_diff : SALib.sample.finite_diff.sample()

Parameters
  • problem (dict) – The problem definition

  • X (numpy.matrix) – The NumPy matrix containing the model inputs

  • Y (numpy.array) – The NumPy array containing the model outputs

  • num_resamples (int) – The number of resamples used to compute the confidence intervals (default 1000)

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

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

References

1

Sobol, I. M. and S. Kucherenko (2009). “Derivative based global sensitivity measures and their link with global sensitivity indices.” Mathematics and Computers in Simulation, 79(10):3009-3017, doi:10.1016/j.matcom.2009.01.023.

Examples

>>> X = finite_diff.sample(problem, 1000)
>>> Y = Ishigami.evaluate(X)
>>> Si = dgsm.analyze(problem, Y, print_to_console=False)

Fractional Factorial

SALib.sample.ff.sample(problem, seed=None)[source]

Generates model inputs using a fractional factorial sample

Returns a NumPy matrix containing the model inputs required for a fractional factorial analysis. The resulting matrix has D columns, where D is smallest power of 2 that is greater than the number of parameters. These model inputs are intended to be used with SALib.analyze.ff.analyze().

The problem file is padded with a number of dummy variables called dummy_0 required for this procedure. These dummy variables can be used as a check for errors in the analyze procedure.

This algorithm is an implementation of that contained in Saltelli et al [Saltelli et al. 2008]

Parameters

problem (dict) – The problem definition

Returns

sample

Return type

numpy.array

References

1

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S., 2008. Global Sensitivity Analysis: The Primer. Wiley, West Sussex, U.K. https://dx.doi.org/10.1002/9780470725184

SALib.analyze.ff.analyze(problem, X, Y, second_order=False, print_to_console=False, seed=None)[source]

Perform a fractional factorial analysis

Returns a dictionary with keys ‘ME’ (main effect) and ‘IE’ (interaction effect). The techniques bulks out the number of parameters with dummy parameters to the nearest 2**n. Any results involving dummy parameters could indicate a problem with the model runs.

Notes

Compatible with:

ff : SALib.sample.ff.sample()

Parameters
  • problem (dict) – The problem definition

  • X (numpy.matrix) – The NumPy matrix containing the model inputs

  • Y (numpy.array) – The NumPy array containing the model outputs

  • second_order (bool, default=False) – Include interaction effects

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

Returns

Si – A dictionary of sensitivity indices, including main effects ME, and interaction effects IE (if second_order is True)

Return type

dict

References

1

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S., 2008. Global Sensitivity Analysis: The Primer. Wiley, West Sussex, U.K. https://dx.doi.org/10.1002/9780470725184

Examples

>>> X = sample(problem)
>>> Y = X[:, 0] + (0.1 * X[:, 1]) + ((1.2 * X[:, 2]) * (0.2 + X[:, 0]))
>>> analyze(problem, X, Y, second_order=True, print_to_console=True)

PAWN Sensitivity Analysis

SALib.analyze.pawn.analyze(problem: Dict, X: numpy.ndarray, Y: numpy.ndarray, S: int = 10, print_to_console: bool = False, seed: Optional[int] = None)[source]

Performs PAWN sensitivity analysis.

Calculates the min, mean, median, max, and coefficient of variation (CV).

CV is (standard deviation / mean), and so lower values indicate little change over the slides, and larger values indicate large variations across the slides.

Notes

Compatible with:

all samplers

This implementation ignores all NaNs.

Parameters
  • problem (dict) – The problem definition

  • X (numpy.array) – A NumPy array containing the model inputs

  • Y (numpy.array) – A NumPy array containing the model outputs

  • S (int) – Number of slides (default 10)

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

  • seed (int) – Seed value to ensure deterministic results

References

1

Pianosi, F., Wagener, T., 2015. A simple and efficient method for global sensitivity analysis based on cumulative distribution functions. Environmental Modelling & Software 67, 1–11. https://doi.org/10.1016/j.envsoft.2015.01.004

2

Pianosi, F., Wagener, T., 2018. Distribution-based sensitivity analysis from a generic input-output sample. Environmental Modelling & Software 108, 197–207. https://doi.org/10.1016/j.envsoft.2018.07.019

3

Baroni, G., Francke, T., 2020. An effective strategy for combining variance- and distribution-based global sensitivity analysis. Environmental Modelling & Software, 134, 104851. https://doi.org/10.1016/j.envsoft.2020.104851

4

Baroni, G., Francke, T., 2020. GSA-cvd Combining variance- and distribution-based global sensitivity analysis https://github.com/baronig/GSA-cvd

Examples

>>> X = latin.sample(problem, 1000)
>>> Y = Ishigami.evaluate(X)
>>> Si = pawn.analyze(problem, X, Y, S=10, print_to_console=False)

High-Dimensional Model Representation

SALib.analyze.hdmr.analyze(problem: Dict, X: numpy.ndarray, Y: numpy.ndarray, maxorder: int = 2, maxiter: int = 100, m: int = 2, K: int = 20, R: Optional[int] = None, alpha: float = 0.95, lambdax: float = 0.01, print_to_console: bool = True, seed: Optional[int] = None) Dict[source]

High-Dimensional Model Representation (HDMR) using B-spline functions.

HDMR is used for variance-based global sensitivity analysis (GSA) with correlated and uncorrelated inputs. This function uses as input

  • a N x d matrix of N different d-vectors of model inputs (factors/parameters)

  • a N x 1 vector of corresponding model outputs

Returns: - each factor’s first, second, and third order sensitivity coefficient

(separated in total, structural and correlative contributions),

  • an estimate of their 95% confidence intervals (from bootstrap method)

  • the coefficients of the significant B-spline basis functions that govern output,

  • Y (determined by an F-test of the error residuals of the HDMR model (emulator) with/without a given first, second and/or third order B-spline). These coefficients define an emulator that can be used to predict the output, Y, of the original (CPU-intensive) model for any d-vector of model inputs. For uncorrelated model inputs (columns of X are independent), the HDMR sensitivity indices reduce to a single index (= structural contribution), consistent with their values derived from commonly used variance-based GSA methods.

Notes

Compatible with:

all samplers

Contributed by @sahin-abdullah (sahina@uci.edu)

Parameters
  • problem (dict) – The problem definition

  • X (numpy.matrix) – The NumPy matrix containing the model inputs, N rows by d columns

  • Y (numpy.array) – The NumPy array containing the model outputs for each row of X

  • maxorder (int (1-3, default: 2)) – Maximum HDMR expansion order

  • maxiter (int (1-1000, default: 100)) – Max iterations backfitting

  • m (int (2-10, default: 2)) – Number of B-spline intervals

  • K (int (1-100, default: 20)) – Number of bootstrap iterations

  • R (int (100-N/2, default: N/2)) – Number of bootstrap samples. Will be set to length of Y if K is set to 1.

  • alpha (float (0.5-1)) – Confidence interval F-test

  • lambdax (float (0-10, default: 0.01)) – Regularization term

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

  • seed (bool) – Set a seed value

Returns

Si – Sa: Uncorrelated contribution Sa_conf: Confidence interval of Sa Sb: Correlated contribution Sb_conf: Confidence interval of Sb S: Total contribution of a particular term S_conf: Confidence interval of S ST: Total contribution of a particular dimension/parameter ST_conf: Confidence interval of ST Sa: Uncorrelated contribution select: Number of selection (F-Test) Em: Result set

C1: First order coefficient C2: Second order coefficient C3: Third Order coefficient

Return type

ResultDict,

References

1

Genyuan Li, H. Rabitz, P.E. Yelvington, O.O. Oluwole, F. Bacon, C.E. Kolb, and J. Schoendorf, “Global Sensitivity Analysis for Systems with Independent and/or Correlated Inputs”, Journal of Physical Chemistry A, Vol. 114 (19), pp. 6022 - 6032, 2010, https://doi.org/10.1021/jp9096919

Examples

>>> X = saltelli.sample(problem, 512)
>>> Y = Ishigami.evaluate(X)
>>> Si = hdmr.analyze(problem, X, Y, **options)