Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package  

作　　者：Jiangshan Lai Jing Tang Tingyuan Li Aiying Zhang Lingfeng Mao 

机构地区：[1]College of Ecology and Environment,Nanjing Forestry University,Nanjing,210037,China [2]Research Center of Quantitative Ecology,Nanjing Forestry University,Nanjing 210037,China [3]University of Chinese Academy of Sciences,Beijing,100049,China [4]Guangzhou Climate and Agro-meteorology Center,Guangzhou 511430,China [5]Guangdong Ecological Meteorological Center,Guangzhou 510640,China

出　　处：《Plant Diversity》2024年第4期542-546,共5页植物多样性（英文版）

基　　金：supported by the National Natural Science Foundation of China (32271551);National Key Research and Development Program of China (2023YFF0805803);the Metasequoia funding of Nanjing Forestry University。

摘　　要：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 gam.hp.gam.hp calculates individual R^(2) values for predictors,based on the concept of'average shared variance',a method previously introduced for multiple regression and canonical analyses.Through these individual R^(2)s,which add up to the overall R^(2),researchers can evaluate the relative importance of each predictor within GAMs.We illustrate the utility of the gam.hp 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 gam.hp package will improve the interpretation of results obtained from GAMs.

关 键 词：Average shared variance Coefficient of determination Commonality analysis GAMs Hierarchical partitioning Individual R^(2) 

分 类 号：O212.1[理学—概率论与数理统计] X171.1[理学—数学]