Practical Guide To Principal Component Methods ... 🆕

: It is structured with short, self-contained chapters and "R lab" sections that walk through real-world applications and tested code examples. Core Methods Covered

: Principal Component Analysis (PCA) for quantitative variables. Practical Guide To Principal Component Methods ...

: The book heavily utilizes the author's own factoextra R package , which creates elegant, ggplot2 -based graphs to help interpret results. : It is structured with short, self-contained chapters

: Simple Correspondence Analysis (CA) for two variables and Multiple Correspondence Analysis (MCA) for more than two. : It is structured with short

: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA) for datasets with both continuous and categorical variables.