WiBB: An integrated method for quantifying the relative importance of predictive variables

This dataset contains simulated datasets, empirical data, and R scripts
described in the paper: “Li, Q. and Kou, X. (2021) WiBB: An integrated
method for quantifying the relative importance of predictive variables.
Ecography (DOI: 10.1111/ecog.05651)”. A fundamental goal of scientific
research is to identify the underlying variables that govern crucial
processes of a system. Here we proposed a new index, WiBB, which
integrates the merits of several existing methods: a model-weighting
method from information theory (Wi), a standardized regression coefficient
method measured by ß* (B), and bootstrap resampling technique (B). We
applied the WiBB in simulated datasets with known correlation structures,
for both linear models (LM) and generalized linear models (GLM), to
evaluate its performance. We also applied two other methods, relative sum
of wight (SWi), and standardized beta (ß*), to evaluate their performance
in comparison with the WiBB method on ranking predictor importances under
various scenarios. We also applied it to an empirical dataset in a plant
genus Mimulus to select bioclimatic predictors of species’ presence across
the landscape. Results in the simulated datasets showed that the WiBB
method outperformed the ß* and SWi methods in scenarios with small and
large sample sizes, respectively, and that the bootstrap resampling
technique significantly improved the discriminant ability. When testing
WiBB in the empirical dataset with GLM, it sensibly identified four
important predictors with high credibility out of six candidates in
modeling geographical distributions of 71 Mimulus species. This integrated
index has great advantages in evaluating predictor importance and hence
reducing the dimensionality of data, without losing interpretive power.
The simplicity of calculation of the new metric over more sophisticated
statistical procedures, makes it a handy method in the statistical
toolbox.