MVN: An R Package for Assessing Multivariate Normality

By Selçuk Korkmaz, Dincer Goksuluk, Gokmen Zararsiz. The R Journal, 2014

1,151 citations recorded by the source corpus.

Assessing the assumption of multivariate normality is required by many parametric mul- tivariate statistical methods, such as MANOVA, linear discriminant analysis, principal component analysis, canonical correlation, etc. It is important to assess multivariate normality in order to proceed with such statistical methods. There are many analytical methods proposed for checking multivariate normality. However, deciding which method to use is a challenging process, since each method may give different results under certain conditions. Hence, we may say that there is no best method, which is valid under any condition, for normality checking. In addition to numerical results, it is very useful to use graphical methods to decide on multivariate normality. Combining the numerical results from several methods with graphical approaches can be useful and provide more reliable decisions. Here, we present an R package, MVN, to assess multivariate normality. It contains the three most widely used multivariate normality tests, including Mardia's, Henze-Zirkler's and Royston's, and graphical approaches, including chi-square Q-Q, perspective and contour plots. It also includes two multivariate outlier detection methods, which are based on robust Mahalanobis distances. Moreover, this package offers functions to check the univariate normality of marginal distributions through both tests and plots. Furthermore, especially for non-R users, we provide a user-friendly web application of the package. This application is available at http://www.biosoft.hacettepe.edu.tr/MVN/.

Subfields: Plant Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/12cf11e0f4e57416567d6bb339aa81604900d986

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Citations in the corpus

Testing the effects of diversity on ecosystem multifunctionality using a multivariate model (2015, methodology): “Multivariate normality of the residuals from the final model was tested using Mardia’s multivariate normality test in the MVN package (Korkmaz et al. 2014) in the software R version 3.1.2 (R Core Team 2014).”
Leader Narcissism Predicts Malicious Envy and Supervisor-Targeted Counterproductive Work Behavior: Evidence from Field and Experimental Research (2018, methodology): “Three tests of multivariate normality were calculated in the MVN package in R (Korkmaz et al. 2014): Mardia’s test statistic based on multivariate skew and kurtosis, Henze-Zirkler’s test based on Mahalanobis distances, and Royston’s test based on the Shapiro–Wilk and ShapiroFrancia statistic.”
Kinematics in Young Star Clusters and Associations with Gaia DR2 (2018, background): “Henze & Zirkler (1990) provide a test of multivariate normality, implemented in the R package MVN (Korkmaz et al. 2014).”
Multivariate goodness-of-fit tests based on Wasserstein distance (2020, methodology): “This formulation is the basis for the multi-scale algorithm for the squared Euclidean distance (p = 2) developed in Mérigot (2011), with further improvements in Lévy (2015) and Kitagawa, Mérigot and Thibert (2017).”
An Improved Evaluation Framework for Generative Adversarial Networks (2018, methodology): “In addition, we used mardia test [38] in the R package MVN [39] to directly study the multivariate normality.”
Assessing Univariate and Multivariate Normality, A Guide For Non-Statisticians (2016, background): “Several tests have been proposed for testing multivariate normality (Selcuk et al., 2015).”
Box–Cox‐chord transformations for community composition data prior to beta diversity analysis (2018, methodology): “An alternative method is the Henze-Zirker test of multinormality, available in function hzTest() of the R package MVN (Korkmaz et al. 2014).”