MVN: a web-tool for assessing multivariate normality
Assessing the assumption of multivariate normality is required by many parametric multivariate 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 a web-tool application to assess multivariate normality. This application uses the MVN package from R. This tool 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 (Multivariate analysis tab). It also includes two multivariate outlier detection methods, which are based on robust Mahalanobis distances (Outlier detection tab). Moreover, this web-tool performs the univariate normality of marginal distributions through both tests and plots (Univariate analysis tab). More detailed information about the tests, graphical approaches and their implementations through this web-tool and MVN package can be found in the paper of the package.
MVN R package: http://cran.r-project.org/web/packages/MVN/index.html
MVN web-tool: http://www.biosoft.hacettepe.edu.tr/MVN/
First, install following R packages:
install.packages("devtools") require(devtools) install_version("shiny", version = "0.10.1", repos = "http://cran.us.r-project.org") install.packages("mvoutlier") install.packages("nortest") install.packages("robustbase") install.packages("asbio") install.packages("moments") install.packages("MASS") install.packages("shiny") install.packages("plyr") install.packages("MVN") install.packages("psych")
Then, run following code in R console: