biostudies-literatureUnknown8(12)Fisher RFull-subsets information theoretic approaches are becoming an increasingly popular tool for exploring predictive power and variable importance where a wide range of candidate predictors are being considered. Here, we describe a simple function in the statistical programming language R that can be used to construct, fit, and compare a complete model set of possible ecological or environmental predictors, given a response variable of interest and a starting generalized additive (mixed) model fit. Main advantages include not requiring a complete model to be fit as the starting point for candidate model set construction (meaning that a greater number of predictors can potentially be explored than might be available through functions such as dredge); model sets that include interactions betweenEcology and evolution6104-6113https://www.ebi.ac.uk/biostudies/studies/S-EPMC6024142biostudies-literatureA simple function for full-subsets multiple regression in ecology with R.PMC6024142Fisher RWilson SKLanglois TJLee ACSin TMfalseA simple function for full-subsets multiple regression in ecology with R.Full-subsets information theoretic approaches are becoming an increasingly popular tool for exploring predictive power and variable importance where a wide range of candidate predictors are being considered. Here, we describe a simple function in the statistical programming language R that can be used to construct, fit, and compare a complete model set of possible ecological or environmental predictors, given a response variable of interest and a starting generalized additive (mixed) model fit. Main advantages include not requiring a complete model to be fit as the starting point for candidate model set construction (meaning that a greater number of predictors can potentially be explored than might be available through functions such as dredge); model sets that include interactions between2018-01-01T00:00:00Z2018 Jun2025-04-25T21:08:47.829Z2019-03-26T23:45:48ZS-EPMC60241422998844110.1002/ece3.4134