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 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 analyses. 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 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.

Keywords

• Average shared variance
• Coefficient of determination
• Commonality analysis
• GAMs
• Hierarchical partitioning
• Individual R2