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

Abstract

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 <i>gam.hp</i>. <i>gam.hp</i> calculates individual <i>R</i> 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 <i>R</i> ²s, which add up to the overall <i>R</i> ², researchers can evaluate the relative importance of each predictor within GAMs. We illustrate the utility of the <i>gam.hp</i> 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 <i>gam.hp</i> package will improve the interpretation of results obtained from GAMs.