| domir-package {domir} | R Documentation |
Tools to Support Relative Importance Analysis
Description
Methods to apply dominance analysis-based relative importance analysis for predictive modeling functions.
Details
This package supports the determination of importance for inputs (i.e., independent variables, predictors, features, parameter estimates; called 'names' in the package) using dominance analysis (Azen & Budescu, 2004; Budescu, 1993).
Dominance analysis resolves the indeterminancy of ascribing the value returned by a predictive modeling function to inputs/names when it is not possible to do so analytically. The most common use case for the application of dominance analysis is in comparing inputs/names in terms of their contribution to a predictive model's fit statistic or metric.
Dominance analysis is a common, and generally well accepted, method for determining the relative importance of inputs/names that is, in part, a conceptual extension of the well-known Shapley value decomposition (e.g., Grömping, 2007; Lipovetsky & Conklin, 2001).
Author(s)
Joseph Luchman jluchman@gmail.com
References
Azen, R., & Budescu, D. V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8(2), 129-148. doi:10.1037/1082-989X.8.2.129
Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3), 542-551. doi:10.1037/0033-2909.114.3.542
Grömping, U. (2007). Estimators of relative importance in linear regression based on variance decomposition. The American Statistician, 61(2), 139-147. doi:10.1198/000313007X188252
Lipovetsky, S, & and Conklin, M. (2001). Analysis of regression in game theory approach. Applied Stochastic Models in Business and Industry, 17(4), 319-330. doi:10.1002/asmb.446