Multivariate analysis is that branch of statistics concerned with examination of several variables simultaneously. This post demonstrates the practical application of several multivariate techniques, using the R programming language and computing environment. Topics include ecological and geographic distance matrices, cluster analysis, the Mantel test, multidimensional scaling, principle components analysis, and discriminant function analysis.
The main body of this post is hosted on GitHub Pages. To read the full post, click this link: Multivariate Analysis with R.
R (r-project.org) is a programming language and software platform for statistical computing and graphics, widely used in academia and industry (see Introduction to R). RStudio is an integrated development environment for R. RStudio makes R easier to use, and it also enables the creation and rendering of plain-text documents that contain embedded R code. With RStudio, you can encapsulate the code and data for your analysis within the text of your paper, fostering research transparency and replicability of results. An increasing number of scholarly journals are requiring that authors submit such replication materials as a condition of publication (see, for example, The AJPS Replication Policy: Innovations and Revisions), and are providing guidelines for data archiving in support of reproducible research (e.g., Reproducible research and Biostatistics and The Role of Data Repositories in Reproducible Research).
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