Lai Jiangshan, Tang Jing, Li Tingyuan, Zhang Aiying, Mao Lingfeng
College of Ecology and Environment, Nanjing Forestry University, Nanjing, 210037, China.
Research Center of Quantitative Ecology, Nanjing Forestry University, Nanjing 210037, China.
Plant Divers. 2024 Jun 14;46(4):542-546. doi: 10.1016/j.pld.2024.06.002. eCollection 2024 Jul.
Generalized Additive Models (GAMs) are widely employed in ecological research, serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors. Nevertheless, evaluating the relative importance of predictors with concurvity (analogous to collinearity) on response variables in GAMs remains a challenge. To address this challenge, we developed an R package named . calculates individual values for predictors, based on the concept of 'average shared variance', a method previously introduced for multiple regression and canonical analyses. Through these individual s, which add up to the overall , researchers can evaluate the relative importance of each predictor within GAMs. We illustrate the utility of the package by evaluating the relative importance of emission sources and meteorological factors in explaining ozone concentration variability in air quality data from London, UK. We believe that the package will improve the interpretation of results obtained from GAMs.
广义相加模型（GAMs）在生态学研究中被广泛应用，是生态学家探索响应变量与预测变量之间复杂非线性关系的有力工具。然而，评估具有共曲线性（类似于共线性）的预测变量对GAMs中响应变量的相对重要性仍然是一项挑战。为应对这一挑战，我们开发了一个名为 的R包。该包基于“平均共享方差”的概念为预测变量计算个体 值，“平均共享方差”是先前为多元回归和典型分析引入的一种方法。通过这些相加得到总体 的个体 值，研究人员可以评估GAMs中每个预测变量的相对重要性。我们通过评估排放源和气象因素在解释英国伦敦空气质量数据中臭氧浓度变异性方面的相对重要性，来说明 包的实用性。我们相信 包将改善对从GAMs获得的结果的解释。
