Authors: | Jeff T. Terpstra, Joseph W. McKean | ||||
Title: | Rank-Based Analysis of Linear Models Using R | ||||
Abstract: | It is well-known that Wilcoxon procedures out perform least squares procedures when the data deviate from normality and/or contain outliers. These procedures can be generalized by introducing weights; yielding so-called weighted Wilcoxon (WW) techniques. In this paper we demonstrate how WW-estimates can be calculated using an L1 regression routine. More importantly, we present a collection of functions that can be used to implement a robust analysis of a linear model based on WW-estimates. For instance, estimation, tests of linear hypotheses, residual analyses, and diagnostics to detect differences in fits for various weighting schemes are discussed. We analyze a regression model, designed experiment, and autoregressive time series model for the sake of illustration. We have chosen to implement the suite of functions using the R statistical software package. Because R is freely available and runs on multiple platforms, WW-estimation and associated inference is now universally accessible. | ||||
Page views:: 11488. Submitted: 2004-04-06. Published: 2005-07-01. |
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Paper: |
Rank-Based Analysis of Linear Models Using R
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DOI: |
10.18637/jss.v014.i07
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![]() This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license. |