Details
| Original language | English |
|---|---|
| Pages (from-to) | 1-17 |
| Number of pages | 17 |
| Journal | Journal of statistical software |
| Volume | 64 |
| Issue number | 9 |
| Publication status | Published - 20 Mar 2015 |
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 semipara-metric 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.
Keywords
- Nonparametric, nparcomp, One-way layout, R
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Mathematics(all)
- Statistics and Probability
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
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In: Journal of statistical software, Vol. 64, No. 9, 20.03.2015, p. 1-17.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - nparcomp
T2 - An R software package for nonparametric multiple comparisons and simultaneous confidence intervals
AU - Konietschke, Frank
AU - Placzek, Marius
AU - Schaarschmidt, Frank
AU - Hothorn, Ludwig A.
N1 - Publisher Copyright: © 2015, American Statistical Association. All rights reserved.
PY - 2015/3/20
Y1 - 2015/3/20
N2 - 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 semipara-metric 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.
AB - 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 semipara-metric 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.
KW - Nonparametric
KW - nparcomp
KW - One-way layout
KW - R
UR - http://www.scopus.com/inward/record.url?scp=84925362224&partnerID=8YFLogxK
U2 - 10.18637/jss.v064.i09
DO - 10.18637/jss.v064.i09
M3 - Article
AN - SCOPUS:84925362224
VL - 64
SP - 1
EP - 17
JO - Journal of statistical software
JF - Journal of statistical software
SN - 1548-7660
IS - 9
ER -