12. Multivariate Data

Descriptive statistics for multivariate data, methods for transforming the data, and some useful graphics tools such as scatterplot matrices and correlograms are described in this chapter. Examples illustrate common tasks with multivariate data such as methods of transformation, centering or scaling data to equalize variances, and computing eigenvalues and eigenvectors of the sample covariance matrix. Some of the operations are also illustrated using the tidyverse dplyr functions. Principal Components Analysis (PCA) can be implemented using the prcomp() or princomp() functions, and the chapter explains and interprets the output with relevant graphics, screeplots and biplots. The chapter concludes with a look at hierarchical cluster analysis implemented with the hclust() function.