TY - JOUR
AU - 
AB - 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: relative importance, hierarchical partitioning, linear model, relaimpo, hier.part, variance decomposition, R . 1. Introduction “Relative importance” refers to the quantification of an individual regressor’s contribution to a multiple regression model. Assessment of relative importance in linear models is simple, as long as all regressors are uncorrelated: Each regressor’s contribution is just the R from 2 2 univariate regression, and all univariate R -values add up to the full model R . In sciences with predominance of 
TI - Relative Importance for Linear Regression inR: The Packagerelaimpo
JF - Journal of Statistical Software
DO - 10.18637/jss.v017.i01
DA - 2006-01-01
UR - https://www.deepdyve.com/lp/unpaywall/relative-importance-for-linear-regression-inr-the-packagerelaimpo-IpuY3zbwEs
DP - DeepDyve
ER -