| domir-package {domir} | R Documentation |
Methods to apply decomposition-based relative importance analysis for R functions.
Determining the relative importance of inputs to (i.e., independent variables, predictors, features) to a predictive model is topic of interest to scientists and analysts. Decomposing a returned value, such as a model fit metric or statistic, into parts attributable to each input is a commonly applied method for determining relative importance in predictive models.
This package supports applying decomposition methods using
lapply- or Map-like functions that compute dominance analysis
(Azen & Budescu, 2004; Budescu, 1993)/Shapley value decomposition
(Grömping, 2007; Lipovetsky & Conklin, 2001) based on the values returned
from other, predictive modeling, functions.
The user interface is structured such that domir automates the decomposition of the returned value and comparisons between model inputs and the user provides the analysis pipeline including model inputs, the predictive modeling function into which they are entered, and returned value from the model to decompose.
This package's user interface accepts inputs as names on the right hand
side of a formula which can be passed on to the predictive model
directly or further processed in the analysis pipeline. The interface
is also planned to be extended to Formula from the
package {Formula} as well as list types as inputs.
Joseph Luchman jluchman@gmail.com
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