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nparcomp: An R Software Package for Nonparametric Multiple Comparisons and Simultaneous Confidence Intervals

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  • Konietschke, Frank
  • Placzek, Marius
  • Schaarschmidt, Frank
  • Hothorn, Ludwig A.

Abstract

One-way layouts, i.e., a single factor with several levels and multiple observations at each level, frequently arise in various fields. Usually not only a global hypothesis is of interest but also multiple comparisons between the different treatment levels. In most practical situations, the distribution of observed data is unknown and there may exist a number of atypical measurements and outliers. Hence, use of parametric and semiparametric procedures that impose restrictive distributional assumptions on observed samples becomes questionable. This, in turn, emphasizes the demand on statistical procedures that enable us to accurately and reliably analyze one-way layouts with minimal conditions on available data. Nonparametric methods offer such a possibility and thus become of particular practical importance. In this article, we introduce a new R package nparcomp which provides an easy and user-friendly access to rank-based methods for the analysis of unbalanced one-way layouts. It provides procedures performing multiple comparisons and computing simultaneous confidence intervals for the estimated effects which can be easily visualized. The special case of two samples, the nonparametric Behrens-Fisher problem, is included. We illustrate the implemented procedures by examples from biology and medicine.

Suggested Citation

  • Konietschke, Frank & Placzek, Marius & Schaarschmidt, Frank & Hothorn, Ludwig A., 2015. "nparcomp: An R Software Package for Nonparametric Multiple Comparisons and Simultaneous Confidence Intervals," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i09).
    • Handle: RePEc:jss:jstsof:v:064:i09
    DOI: http://hdl.handle.net/10.18637/jss.v064.i09
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    References listed on IDEAS

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    1. Bretz, Frank, 2006. "An extension of the Williams trend test to general unbalanced linear models," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1735-1748, April.
    2. Browne, Richard H., 2010. "The t-Test p Value and Its Relationship to the Effect Size and P(X>Y)," The American Statistician, American Statistical Association, vol. 64(1), pages 30-33.
    3. Konietschke Frank & Brunner Edgar & Bösiger Sandra & Hothorn Ludwig A., 2013. "Are Multiple Contrast Tests Superior to the ANOVA," The International Journal of Biostatistics, De Gruyter, vol. 9(1), pages 1-11, August.
    4. Neubert, Karin & Brunner, Edgar, 2007. "A studentized permutation test for the non-parametric Behrens-Fisher problem," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 5192-5204, June.
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