Regression with empirical variable selection: description of a new method and application to ecological datasets.

ABSTRACT: Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most

SUBMITTER: Goodenough AE 

PROVIDER: S-EPMC3316704 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature