domir-package {domir} | R Documentation |

## Tools to Support Relative Importance Analysis

### Description

Methods to apply decomposition-based relative importance analysis for
R functions.

### Details

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
(GrÃ¶mping, 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.

### 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

GrÃ¶mping, 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

[Package

*domir* version 1.0.0

Index]