The R package "sensobol" provides several functions to conduct variance-based uncertainty and sensitivity analysis, from the estimation of sensitivity indices to the visual representation of the results. It implements several state-of-the-art first and total-order estimators and allows the computation of up to third-order effects, as well as of the approximation error, in a swift and user-friendly way. Its flexibility makes it also appropriate for models with either a scalar or a multivariate output. We illustrate its functionality by conducting a variance-based sensitivity analysis of three classic models: the Sobol' (1998) G function, the logistic population growth model of Verhulst (1845), and the spruce budworm and forest model of Ludwig, Jones and Holling (1976).
翻译:R 包“ sensobol” 提供若干功能, 进行基于差异的不确定性和敏感性分析, 从估计敏感度指数到对结果的直观表示, 执行几个最先进的第一和全顺序估计器, 并允许以快速和方便用户的方式计算最高至三阶效应和近似误差, 其灵活性也使其适合具有标度或多变量输出的模型。 我们通过对三种经典模型进行基于差异的敏感性分析来说明其功能: Sobol (1998年) G 函数、 Verhulst (1845年) 的物流人口增长模型、 以及 Ludwig、 Jones 和 Holling (1976年) 的温泉芽和森林模型。