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Zoltán BARCZA 2019-02-06 21:40:47 +01:00 committed by GitHub
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RBBGCMuso/R/musoSensi.R
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#' musoSensi #' musoSensi
#' #'
#' This function performs multiple liear regression based global sensitivity analysis based on the output of musoMonte. First the user has to select the parameters of interest with possible minimum and maximum values. After execution musoSensi will then assign weights to the predefined parameters which means that the importance of the parameters will be ranked. The sensitivity analysis calculates the effect of input variability on the output variability in Monte Carlo framework. The result will largely depend on the selected output variable (GPP, evapotranspiration, LAI, soil water content), and on the parameter ranges. Other factors like climate, management and site specific conditions might affect the results. #' This function performs multiple linear regression based global sensitivity analysis using on the output of musoMonte. The algorithm implements the method proposed by Verbeeck et al. 2006 (Tree Physiology 26, 807–817). First the user has to select the parameters of interest with possible minimum and maximum values. After execution musoSensi will then assign weights to the predefined parameters which means that the importance of the parameters will be ranked. The sensitivity analysis calculates the effect of input variability on the output variability in Monte Carlo framework. The result will largely depend on the selected output variable (GPP, evapotranspiration, LAI, soil water content), and on the parameter ranges. Other factors like climate, management and site specific conditions might affect the results.
#' @author Roland HOLLOS #' @author Roland HOLLOS
#' @param monteCarloFile If you run the musoMonte function previously, you do not have to re-run the monteCarlo experiment, simply provide the preservedEpc.csv file to musoSensi with its path. If you do not set this parameter, musoSensi will run the musoMonte function to get all necessary information. #' @param monteCarloFile If you run the musoMonte function previously, you do not have to re-run the monteCarlo experiment, simply provide the preservedEpc.csv file to musoSensi with its path. If you do not set this parameter, musoSensi will run the musoMonte function to get all necessary information.
#' @param outputFile The filename in which the output of the musoSensi function will be saved. By default it is "sensitivity.csv" #' @param outputFile The filename in which the output of the musoSensi function will be saved. By default it is "sensitivity.csv"