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[求助]
求教：有大神会敏感性分析（GSUA global sensitivity and uncertainty analyses）吗？
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2015年12月投了一篇文章到SR，2016年1月27日回复major revision。下面是一个审稿人的要求，要求用基于方差的敏感性分析这个方法来做分析，并作出比较好看的2D图来。求大神赐教啊，这该怎么实现？我看了一下该审稿人的意见，主要就是用Global sensitivity analysisi:The Primer这本书里头4.8章节的方法，作出Table4.1和Figure4.1这样的图表来就可以了。离4周内上传修订稿的时间只有十来天了，急求，多谢大家了！ Reviewer #2 (Remarks to the Author): General Comments: I like the topic and the methods used in the paper however: - I see the lack of global sensitivity and uncertainty analyses (GSUA) and a conversation about management implications that we can extract from the model/GSUA. - It is certainly not true anymore that network theory has not been applied in the field of epidemiology. - I do not think it is necessary to mention the name of software / models used in the abstract. The abstract must focus on the scientific and practical innovation of the paper rather than on the methodological details, particularly if they are about already existing models - it would really useful to plot the probability distribution function of network properties as well as their variations over space and time. Nice 2D plots can be created in my opinion Specific Comments: GSUA is very important because it given an idea of what is driving the output in term of model input factor importance and interaction, and how that can be used for management. GSUA is a variance-based method for analyzing data and models given an objective function. It is a bit unclear how many realizations of the model have been run and how the authors maximized prediction accuracy. Are the values of the input factors taken to maximize predictions? GSUA (see references below) typically assigns probability distribution functions to all model factors and propagate that into model outputs. That is useful for assessing input factor importance and interaction, regimes, and scaling laws between model input factors and outcomes. This differs from traditional sensitivity analysis methods (that are even missing here) Variance-based methods (see Saltelli and Convertino below) are a class of probabilistic approaches which quantify the input and output uncertainties as probability distributions, and decompose the output variance into parts attributable to input variables and combinations of variables. The sensitivity of the output to an input variable is therefore measured by the amount of variance in the output caused by that input. Variance-based methods allow full exploration of the input space, accounting for interactions, and nonlinear responses. For these reasons they are widely used when it is feasible to calculate them. Typically this calculation involves the use of Monte Carlo methods, but since this can involve many thousands of model runs, other methods (such as emulators) can be used to reduce computational expense when necessary. Note that full variance decompositions are only meaningful when the input factors are independent from one another. If that is not the case information theory based GSUA is necessary (see Ludtke et al. ) Thus, I really would like to see GSUA done because it (i) informs about the dynamics of the processes investigated and (ii) is very important for management purposes. 这个审稿人还附了三个文献：（我把这三个文献也放在附件了） Convertino et al. Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MaxEnt Journal Environmental Modelling & Software archive Volume 51, January, 2014 Pages 296-309 Saltelli A, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, Stefano Tarantola Global Sensitivity Analysis: The Primer ISBN: 978-0-470-05997-5 Ludtke et al. (2007), Information-theoretic Sensitivity Analysis: a general method for credit assignment in complex networks J. Royal Soc. Interface |
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- 附件 2 : information-theoretic_Sensitivity_Analysis__a_general_method_for_credit_assignment_in_complex_networks.pdf
- 附件 3 : Untangling_drivers_of_species_distributions_Global_sensitivity_and_uncertainty_analyses_of_MaxEnt.pdf
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