Type: | Package |
Title: | Global Sensitivity Analysis Tool |
Version: | 1.0.0 |
Maintainer: | Camila Garcia-Echeverri <cagarciae@unal.edu.co> |
Description: | A tool to sensitivity analysis using SOBOL (Sobol, 1993) and AMA (Dell'Oca et al. 2017 <doi:10.5194/hess-21-6219-2017>) indices. It allows to identify the most sensitive parameter or parameters of a model. |
Depends: | R (≥ 3.4) |
Imports: | stats, e1071, utils |
Suggests: | knitr, rmarkdown |
License: | GPL-2 |
Encoding: | UTF-8 |
LazyData: | true |
RoxygenNote: | 7.1.0 |
VignetteBuilder: | knitr |
NeedsCompilation: | no |
Packaged: | 2020-07-09 20:41:30 UTC; camil |
Author: | Camila Garcia-Echeverri
|
Repository: | CRAN |
Date/Publication: | 2020-07-10 09:40:03 UTC |
AMA indices
Description
This function calculates the AMA indices: AMAE, AMAV, AMAV and AMAK.
Usage
AMA(data_Bstat, CM, pp_names, steps = 100)
Arguments
data_Bstat |
a data frame of dimensions t x 6, here t is the number of temporary steps and each column corresponds to a statistical measure: mean, variance, skewness, kurtosis and excess kurtosis. |
CM |
A list of arrays, each array corresponds to the conditional moments calculated with the mean, variance, skewness, kurtosis. Each array has dimensions of steps, t, p. |
pp_names |
vector that contains the names of the parameters (pp) |
steps |
number of divisions of the parametric range |
Value
A list of four matrices, which corresponds to AMAE, AMAV, AMAR and AMAK indices. Each matrix has dimensions of t x pp.
Author(s)
Camila Garcia-Echeverri <cagarciae@unal.edu.co>
Maria Cristina Areas-Bautista <mcarenasb@unal.edu.co>
Hydrodynamics of the natural media research group - HYDS National University of Colombia - Bogota
References
Dell’Oca, A., Riva, M., & Guadagnini, A. (2017). Moment-based metrics for global sensitivity analysis of hydrological systems. Hydrology and Earth System Sciences, 21(12), 6219–6234. https://doi.org/10.5194/hess-21-6219-2017
Examples
data("data_Bstat", "CM", "pp_names")
AMA_indices <- AMA(data_Bstat, CM, pp_names, steps= 15)
Basic statistical measures of a mathematical model results
Description
This function calculates the mean, variance, skewness, kurtosis and excess kurtosis of a model output, this output can be given for different temporal periods (days, months or years).
Usage
Bstat(out_set)
Arguments
out_set |
matrix of dimensions n x t, where n equals the number of runs and t is equal to the number of temporary steps. |
Value
a data frame of dimensions t x 6, here t is the number of temporary steps and each column corresponds to a statistical measure: mean, variance, skewness, kurtosis and excess kurtosis.
Author(s)
Camila Garcia-Echeverri <cagarciae@unal.edu.co>
Hydrodynamics of the natural media research group - HYDS National University of Colombia - Bogota
Examples
data("out_set")
data_Bstat <- Bstat(out_set)
@title First four conditional moments of example data
Description
@description Data generated by Cond_Moments example
Usage
CM
Format
A list
- CM
A list of arrays, each array has dimensions of steps, t, pp
Author(s)
Camila Garcia-Echeverri
Conditional statistical moments of a model output
Description
This function evaluates the first four statistical moments after grouping the model output by different parametric ranges.
Usage
Cond_Moments(parameters_set, out_set, pp_names, steps = 100)
Arguments
parameters_set |
matrix of dimensions n x pp, where n is the number of runs and pp is the number of parameters. |
out_set |
matrix of dimensions n x t, where n is the number of runs and t is the number of temporary steps. |
pp_names |
vector that contains the names of the parameters. |
steps |
number of divisions of the parametric range. |
Value
A list of arrays, each array has dimensions of steps, t, pp.
Author(s)
Camila Garcia-Echeverri <cagarciae@unal.edu.co>
Maria Cristina Areas-Bautista <mcarenasb@unal.edu.co>
Hydrodynamics of the natural media research group - HYDS National University of Colombia - Bogota
Examples
data("parameters_set", "out_set", "pp_names")
CM <- Cond_Moments(parameters_set, out_set, pp_names, steps=15)
Global Sensitivity Analysis tool
Description
This function performs the global sensitivity analysis starting from the gross results of the model.
Usage
GSAtool(
parameters_set,
out_set,
pp_names,
steps = 100,
save = FALSE,
dir = NULL
)
Arguments
parameters_set |
matrix of dimensions n x pp, where n is the number of runs and pp is the number of parameters. |
out_set |
matrix of dimensions n x t, where n is the number of runs and t is the number of temporary steps. |
pp_names |
a strings vector with the names of the parameters of the model |
steps |
number of divisions of the parametric range. |
save |
T to save the results in .csv files, by default save=F. |
dir |
a directory to save the results |
Value
a list containing two outputs: SOBOL and AMA indices.
Author(s)
Camila Garcia-Echeverri <cagarciae@unal.edu.co>
Maria Cristina Areas-Bautista <mcarenasb@unal.edu.co>
Hydrodynamics of the natural media research group - HYDS National University of Colombia - Bogota
References
Dell’Oca, A., Riva, M., & Guadagnini, A. (2017). Moment-based metrics for global sensitivity analysis of hydrological systems. Hydrology and Earth System Sciences, 21(12), 6219–6234. https://doi.org/10.5194/hess-21-6219-2017
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. https://doi.org/10.1016/S0378-4754(00)00270-6
Examples
data("parameters_set", "out_set", "pp_names")
GSA_results <- GSAtool(parameters_set, out_set, pp_names, steps = 15, save=FALSE)
SOBOL indices
Description
This function calculates the first order and total SOBOL indices.
Usage
SOBOL(data_var, CM_mean, CM_var, pp_names)
Arguments
data_var |
a vector containing the variance of the model output for each modelling time step. |
CM_mean |
An array containing the conditional mean of each parameter of the model. This array has dimensions of steps x t x pp, where steps is the number of divisions of the parametric range, t is the number of temporary steps and pp the number of parameters of the model. |
CM_var |
An array containing the conditional variance of each parameter of the model. This array has dimensions of steps x t x pp, where steps is the number of divisions of the parametric range, t is the number of temporary steps and pp the number of parameters of the model. |
pp_names |
a strings vector with the names of the parameters of the model. |
Value
a list containing two matrices. The first contains the first order sobol, the second sobol_total.
Author(s)
Camila Garcia-Echeverri <cagarciae@unal.edu.co>
Maria Cristina Areas-Bautista <mcarenasb@unal.edu.co>
Hydrodynamics of the natural media research group - HYDS National University of Colombia - Bogota
References
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. https://doi.org/10.1016/S0378-4754(00)00270-6
Examples
data("data_Bstat", "CM", "pp_names")
SOBOL_indices <- SOBOL(data_Bstat[,3], CM$CM_mean, CM$CM_var , pp_names)
@title First four conditional moments of example data
Description
@description Data generated with the example of the function Cond_Moments
Usage
data_Bstat
Format
A data.frame
- data_Bstat
a data frame of dimensions t x 6
Author(s)
Camila Garcia-Echeverri
Source
Function Bstat
@title Results of a sample model
Description
@description Output generated with an example mathematical model.
Usage
out_set
Format
A matrix
- out_set
a matrix of dimensions 500 x 365 (pp x t), runs of the model x temporary steps (365 days)
References
Arenas-Bautista, M. C. (2020). Integration of Hydrological and Economical Aspects for Water Management in Tropical Regions. Case Study: Middle Magdalena Valley, Colombia. National University of Colombia.
@title Set of parameters randomly generated
Description
@description It contains 10 parameters
Usage
parameters_set
Format
A matrix
- parameters_set
a matrix of dimensions 500 x 10 (n x pp),runs of the model x number of parameters
References
Arenas-Bautista, M. C. (2020). Integration of Hydrological and Economical Aspects for Water Management in Tropical Regions. Case Study: Middle Magdalena Valley, Colombia. National University of Colombia.
@title Example - parameters names
Description
@description 10 parameters names.
Usage
pp_names
Format
A value
- pp_names
a vector of characters
Author(s)
CGE
References
Arenas-Bautista, M. C. (2020). Integration of Hydrological and Economical Aspects for Water Management in Tropical Regions. Case Study: Middle Magdalena Valley, Colombia. National University of Colombia.
Save GSA results
Description
This function helps to save the results in .csv format
Usage
save_results(
SOBOL = NULL,
SOBOL_total = NULL,
amae = NULL,
amav = NULL,
amar = NULL,
amak = NULL,
dir
)
Arguments
SOBOL |
SOBOL index |
SOBOL_total |
SOBOL_total |
amae |
AMAE index |
amav |
AMAV index |
amar |
AMAR index |
amak |
AMAK index |
dir |
a directory to save the results |
Author(s)
Camila Garcia-Echeverri <cagarciae@unal.edu.co>
Hydrodynamics of the natural media research group - HYDS National University of Colombia - Bogota