Relative Importance for Linear Regression inR: The Packagerelaimpo

Abstract

Relative importance is a topic that has seen a lot of interest in recent years, particularly in applied work. The R package relaimpo implements six different metrics for assessing relative importance of regressors in the linear model, two of which are recommended-averaging over orderings of regressors and a newly proposed metric (Feldman 2005) called pmvd. Apart from delivering the metrics themselves, relaimpo also provides (exploratory) bootstrap confidence intervals. This paper offers a brief tutorial introduction to the pack-age. The methods and relaimpo’s functionality are illustrated using the data set swiss that is generally available in R. The paper targets readers who have a basic understanding of multiple linear regression. For the background of more advanced aspects, references are provided.

Keywords

R packageMetric (unit)Computer scienceLinear regressionSet (abstract data type)Linear modelConfidence intervalRegressionStatisticsData miningEconometricsMathematicsMachine learningProgramming languageEngineering

Related Publications

mixtools: AnRPackage for Analyzing Finite Mixture Models

Tatiana Benaglia , Didier Chauveau , David R. Hunter +1 more

The mixtools package for <code>R</code> provides a set of functions for analyzing a variety of finite mixture models. These functions include both traditional methods, su...

2009 Journal of Statistical Software 1244 citations

R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization

A. Colin Cameron , Frank Windmeijer

For regression models other than the linear model, R-squared type goodness-to-fit summary statistics have been constructed for particular models using a variety of methods. The ...

1996 Journal of Business and Economic Stat... 223 citations

MCMC Methods for Multi-Response Generalized Linear Mixed Models: TheMCMCglmmRPackage

Jarrod D. Hadfield

Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in clo...

2010 Journal of Statistical Software 4603 citations

Building Predictive Models in R Using the caret Package

Max Kühn

The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The pa...

2008 Journal of Statistical Software 8313 citations

Refining Bootstrap Simultaneous Confidence Sets

Rudolf Beran

Abstract Simultaneous confidence sets for a collection of parametric functions may be constructed in several different ways. These ways include: (a) the exact pivotal method tha...

1990 Journal of the American Statistical A... 44 citations

Publication Info

Year: 2006
Type: article
Volume: 17
Issue: 1
Citations: 2086
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Relative Importance for Linear Regression inR: The Packagerelaimpo

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

2086

OpenAlex

Cite This

APA Style

                            
                                    Ulrike Grömping
                                
                            (2006). 
                            Relative Importance for Linear Regression in<i>R</i>: The Package<b>relaimpo</b>. 
                            Journal of Statistical Software
                            , 17
                            (1)
                            .
                            https://doi.org/10.18637/jss.v017.i01

Identifiers

DOI: 10.18637/jss.v017.i01

Relative Importance for Linear Regression in<i>R</i>: The Package<b>relaimpo</b>