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 twoand 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.
Bayesian, Econometrics, Environmetrics, OfficialStatistics, Psychometrics, SocialSciences, SpatioTemporal
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For attribution, please cite this work as
Bilgic & Susmann, "The R Journal: rlme: An R Package for Rank-Based Estimation and Prediction in Random Effects Nested Models", The R Journal, 2013
BibTeX citation
@article{RJ-2013-027,
author = {Bilgic, Yusuf K. and Susmann, Herbert},
title = {The R Journal: rlme: An R Package for Rank-Based Estimation and Prediction in Random Effects Nested Models},
journal = {The R Journal},
year = {2013},
note = {https://doi.org/10.32614/RJ-2013-027},
doi = {10.32614/RJ-2013-027},
volume = {5},
issue = {2},
issn = {2073-4859},
pages = {71-79}
}