首页|Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package
Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package
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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 pre-dictors.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 R2 values for predictors,based on the concept of'average shared variance',a method previously introduced for multiple regression and canonical an-alyses.Through these individual R2s,which add up to the overall R2,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 concentra-tion variability in air quality data from London,UK.We believe that the gam.hp package will improve the interpretation of results obtained from GAMs.
Average shared varianceCoefficient of determinationCommonality analysisGAMsHierarchical partitioningIndividual R2
Jiangshan Lai、Jing Tang、Tingyuan Li、Aiying Zhang、Lingfeng Mao
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College of Ecology and Environment,Nanjing Forestry University,Nanjing,210037,China
Research Center of Quantitative Ecology,Nanjing Forestry University,Nanjing 210037,China
University of Chinese Academy of Sciences,Beijing 100049,China
Guangzhou Climate and Agro-meteorology Center,Guangzhou 511430,China
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