Free software tool to evaluate relative importance of predictors in generalized additive models

Generalized additive models (GAMs) are commonly used in ecological research for their ability to model complex nonlinear relationships. However, assessing predictor importance in the presence of concurvity is difficult due to overlapping variance among predictors.

To that end, a team of researchers from Nanjing Forestry University and Guangzhou Climate and Agro-meteorology Center in China created a new computer software package that calculates individual R² values for predictors based on the concept of ‘average shared variance’, a method previously introduced for multiple regression and canonical analyses.

“This newly developed gam.hp R package calculates individual R² values for predictors in GAMs based on the 'average shared variance' concept,” shares Jiangshan Lai, lead and co-corresponding author of the study. “ It allows for the equitable distribution of shared R² among related predictors, providing a measure of each predictor's unique and shared contribution to the model's fit.”

Notably, the gam.hp R package is free to use, with the details published in the KeAi journal Plant Diversity.

The authors demonstrate the utility of gam.hp R package by analyzing air quality data in London, specifically looking at the relative importance of emission sources and meteorological factors in explaining ozone concentration variability.

“The findings recommend prioritizing the control of NOx emissions during ozone pollution episodes in London, followed by efforts to reduce CO emissions and enhance the accuracy of wind speed (WS) forecasts,” explains Lai.

This methodology supports the formulation of more refined and effective strategies for ozone pollution control by government bodies, considering various influencing factors.

“We would like to see more researchers incorporate the gam.hp package into their studies. Use this package if its outcome meets your analytical expectations; otherwise, its usage is not mandatory,” quips Lai.

Contact the author:

Jiangshan Lai, lai@njfu.edu.cn

The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 100 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).

Funder:  

This research was supported by the National Natural Science Foundation of China (32271551), National Key Research and Development Program of China (2023YFF0805803) and the Metasequoia funding of Nanjing Forestry University

Journal:

Plant Diversity

DOI: 

https://doi.org/10.1016/j.pld.2024.06.002

Article title:

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

Conflict of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.