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A simple function for full-subsets multiple regression in ecology with R.

Rebecca Fisher1,2, Shaun K Wilson2,3, Tsai M Sin4

  • 1Australian Institute of Marine Science UWA Oceans Institute Crawley WA Australia.

Ecology and Evolution
|July 11, 2018
PubMed
Summary

This study introduces an R function for building and comparing ecological predictor models using information theoretic approaches. It simplifies exploring variable importance and predictive power with a wider range of predictors.

Keywords:
collinearitycomplete‐subsets modelinggamgeneralized additive modelsinformation theoretic approachesmultimodel inferencemultiple regression

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Area of Science:

  • Ecology
  • Statistics
  • Environmental Science

Background:

  • Information theoretic approaches are valuable for variable selection in ecological and environmental studies.
  • Existing methods may limit the number of predictors that can be explored.

Purpose of the Study:

  • To present a new R function for constructing, fitting, and comparing full subsets of ecological predictor models.
  • To facilitate broader exploration of variable importance and predictive power.

Main Methods:

  • The function utilizes generalized additive (mixed) models (GAMs) with smoothers.
  • It accommodates continuous and factor predictors, including interactions and nonlinear terms.
  • Automatic removal of models with correlated predictors is included.

Main Results:

  • The R function enables the construction of comprehensive model sets without needing a fully fitted starting model.
  • It supports complex model structures, including interactions and nonlinear relationships.
  • Case studies demonstrate the function's utility for exploratory ecological analysis.

Conclusions:

  • The developed R function offers a flexible and efficient tool for information theoretic model selection in ecology.
  • It expands the potential for exploring a greater number of predictors and complex relationships.
  • This approach enhances the utility of model selection for ecological and environmental research.