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Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM
Open accessAbstract
Nonparametric statistical inference methods for a modern and robust analysis of longitudinal and multivariate data in factorial experiments are essential for research.While existing approaches that rely on specific distributional assumptions of the data (multivariate normality and/or equal covariance matrices) are implemented in statistical software packages, there is a need for user-friendly software that can be used for the analysis of data that do not fulfill the aforementioned assumptions and provide accurate p value and confidence interval estimates.Therefore, newly developed nonparametric statistical methods based on bootstrap-and permutation-approaches, which neither assume multivariate normality nor specific covariance matrices, have been implemented in the freely available R package MANOVA.RM.The package is equipped with a graphical user interface for plausible applications in academia and other educational purpose.Several motivating examples illustrate the application of the methods.
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“This analysis does not rely on assumptions of multivariate normality or specific covariance structures in the data, making it suitable even for data with non‐normal error terms and/or heteroscedastic variances [58, 59].”
From Interpersonal Synchronization of Autonomic Physiology via Mere Visual Contact (2026) · cites this paper as [58]
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Cites
Simultaneous Inference in General Parametric Models
Hothorn, T., Bretz, F., & Westfall, P. H. (2008) · Biometrical Journal · doi:10.1002/bimj.200810425 · Cited by 14,265
A new method for non-parametric multivariate analysis of variance
Anderson, M. J. (2001) · Austral Ecology · doi:10.1111/j.1442-9993.2001.01070.pp.x · Cited by 13,828 · cited 2×
“Focusing on metric data and mean-based procedures, MANOVA and RM models are typically inferred by means of “classical” procedures such as Wilks’ Lambda, Lawley-Hotelling, Roy’s largest root (Davis, 2002; Johnson and Wichern, 2007; Anderson, 2001) or (generalized) linear mixed models with generalized estimating equatio…”
MethodApplied Multivariate Statistical Analysis
Härdle, W. K., & Simar, L. (2015) · Springer eBooks · doi:10.1007/978-3-662-45171-7 · Cited by 1,653
Applied Multivariate Statistical Analysis
Dunn, C. L., Johnson, R. A., & Wichern, D. W. (1989) · Technometrics · doi:10.2307/1268834 · Cited by 1,624
Noguchi, K., Gel, Y. R., Brunner, E., & Konietschke, F. (2012) · Journal of Statistical Software · doi:10.18637/jss.v050.i12 · Cited by 1,146 · cited 2×
“…rank-based methods for null hypotheses formulated in distribution functions are implemented within the packages npmv for oneand two-way MANOVA (Burchett et al., 2017) and nparLD for several repeated measures designs The R Journal Vol. XX/YY, AAAA 20ZZ ISSN 2073-4859 (Noguchi et al., 2012).”
BackgroundMethodSelf-citationNonparametric analysis of longitudinal data in factorial experiments
Brunner, E., Domhof, S., & Langer, F. (2002) · Cited by 635
Nonparametric Analysis of Longitudinal Data in Factorial Experiments
Ghosh, S. (2003) · Technometrics · doi:10.1198/tech.2003.s161 · Cited by 585 · cited 1×
“…the The R Journal Vol. XX/YY, AAAA 20ZZ ISSN 2073-4859 well-established ANOVA-type statistic (ATS) QN = NX ′ •TX• (3) by Brunner (2001) is implemented together with the enhanced F-approximation of the statistic proposed in Brunner et al. (1997, 2002) and implemented in the SAS PROC Mixed procedure.”
MethodBox-Type Approximations in Nonparametric Factorial Designs
Brunner, E., Dette, H., & Munk, A. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10473671 · Cited by 272 · cited 2×
“…the The R Journal Vol. XX/YY, AAAA 20ZZ ISSN 2073-4859 well-established ANOVA-type statistic (ATS) QN = NX ′ •TX• (3) by Brunner (2001) is implemented together with the enhanced F-approximation of the statistic proposed in Brunner et al. (1997, 2002) and implemented in the SAS PROC Mixed procedure.”
MethodExact and asymptotically robust permutation tests
Chung, E., & Romano, J. P. (2013) · The Annals of Statistics · doi:10.1214/13-aos1090 · Cited by 239 · cited 2×
“, 2018) and ffmanova (Øyvind Langsrud and Mevik, 2019) contain interesting permutation and rotation tests, which, however, require certain invariances resulting in model restrictions (see, e.g., the discussion in Huang et al., 2006; Chung and Romano, 2013) .”
BackgroundNonparametric Inference for Multivariate Data: The R Package npmv
Burchett, W., Ellis, A. R., Harrar, S. W., & Bathke, A. C. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i04 · Cited by 203 · cited 5×
“, 2006; Chung and Romano, 2013) . Most of these procedures, however, rely on specific distributional assumptions (such as multivariate normality) and/or specific covariance or correlation structures (e.g., homogeneity between groups or, for RM, compound symmetry; possibly implying equal correlation between measurement…”
BackgroundMethodInfluential
Research works printed but not linked: 4
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Devarajan, A. K., Muthukrishanan, G., Truu, J., Truu, M., Ostonen, I., Subramanian, S., Panneerselvam, P., & Gopalasubramaniam, S. K. (2021) · Plants · doi:10.3390/plants10020387 · Cited by 85
Heavy metal and pesticide levels in dairy products: Evaluation of human health risk
Năstăsescu, V., Mititelu, M., Γούμενου, Μ., Docea, A. O., Renieri, E. A., Udeanu, D. I., Oprea, E., Arsene, A. L., Dinu‐Pîrvu, C. E., & Ghica, M. (2020) · Food and Chemical Toxicology · doi:10.1016/j.fct.2020.111844 · Cited by 84
Höhn, C., Hahn, M. A., Lendner, J. D., & Hoedlmoser, K. (2024) · eNeuro · doi:10.1523/eneuro.0259-23.2024 · Cited by 76
“Since in most cases at least one assumption for parametrical testing was violated, we decided to compute more conservative semiparametrical analyses with the MANOVA.RM package (Friedrich et al., 2019).”
MethodVoormolen, D. C., Zeldovich, M., Haagsma, J. A., Polinder, S., Friedrich, S., Maas, A. I. R., Wilson, L., Steyerberg, E. W., Čović, A., Anđelić, N., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9051525 · Cited by 64
Self-citationGeng, Y., Wang, J., Sun, Z., Ji, C., Huang, M., Zhang, Y., Xu, P., Li, S., Pawlett, M., & Zou, J. (2020) · Geoderma · doi:10.1016/j.geoderma.2020.114730 · Cited by 61
Metadata
- Authors
- Friedrich, S., Konietschke, F., & Pauly, M.
- Affiliations
- TU Dortmund University (DE)
- Venue
- The R Journal
- Publisher
- The R Foundation
- Published
- 2019
- Volume
- 11
- Issue
- 2
- Pages
- 380
- ISSN
- 2073-4859 (ISSN-L)
- OpenAlex
- W2998668315
- Cited by
- 131
- References
- 42
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 19 · 2023: 31 · 2022: 17 · 2021: 19 · 2020: 13
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- The R Journal (publishedVersion, cc-by)
- Lincoln (University of Nebraska) (submittedVersion, cc-by)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, other-oa)
- OPUS (Augsburg University) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (DFG‐PA 2409/3‐1)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Data Analysis with R
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- repeated measures designs, longitudinal data, bootstrapping, permutation tests, multivariate normality, covariance structure, R package, graphical user interface, confidence intervals, p-value estimation
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