rlme: An R Package for Rank-Based Estimation and Prediction in Random Effects Nested Models

Authors

Yusuf K. Bilgic, SUNY Geneseo
Herbert Susmann, SUNY Geneseo

Date of this Version

12-2013

Document Type

Article

Citation

The R Journal (December 2013) 5(2); Editor: Hadley Wickham

Comments

Copyright 2013, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

There is a lack of robust statistical analyses for random effects linear models. In practice, statistical analyses, including estimation, prediction and inference, are not reliable when data are unbalanced, of small size, contain outliers, or not normally distributed. It is fortunate that rank-based regression analysis is a robust nonparametric alternative to likelihood and least squares analysis. We propose an R package that calculates rank-based statistical analyses for two- and three-level random effects nested designs. In this package, a new algorithm which recursively obtains robust predictions for both scale and random effects is used, along with three rank-based fitting methods.