Work

Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM

Year: 2019

Type: article

Abstract: 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.

Source: The R Journal

DOI: 10.32614/rj-2019-051

Authors: Sarah Friedrich, Frank Konietschke, Markus Pauly

Institutions: TU Dortmund University, Humboldt-Universität zu Berlin, Universitätsmedizin Göttingen, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charité – Universitätsmedizin Berlin

Language: English

Topic: Data Analysis with R

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funder: Deutsche Forschungsgemeinschaft

Award: G2690779432