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Principal component methods are used to summarize and visualize the information contained in a large multivariate data sets. Here, we provide practical examples and course videos to compute and interpret principal component methods (PCA, CA, MCA, MFA, etc) using R software. The following figure illustrates the type of analysis to be performed depending on the type of variables contained in the data set. The Book: Practical Guide to Principal Component Methods in R Practical guide: R code and interpretation We’ll mainly use two R packages: FactoMineR: for computing principal component methods; factoextra: for extracting, visualizing and interpreting the results. This section is organized as follow: BASICS Introduction to R R packages for principal component methods CLASSICAL METHODS PCA - Principal Component Analysis, for analyzing a data set containing continuous variables CA - Correspondence Analysis, for analyzing the association between two categorical variables. MCA - Multiple Correspondence Analysis, for analyzing a data set containing more than 2 categorical variables. ADVANCED METHODS FAMD - Factor Analysis of Mixed Data, for analyzing a data set containing both quantitative and qualitative variables. MFA - Multiple Factor Analysis, for analyzing a data set containing variables structured into groups. CLUSTERING HCPC - Hierarchical Clustering on Principal Components More examples Principal Component Analysis in R: prcomp vs princomp PCA in R Using Ade4: Quick Scripts Correspondence Analysis in R: Million Ways Correspondence Analysis: Theory and Practice Course videos PCA in R Using FactoMineR: Quick Scripts and Videos CA in R Using FactoMineR: Quick Scripts and Videos MCA in R Using FactoMineR: Quick Scripts and Videos FAMD in R Using FactoMineR: Quick Scripts and Videos MFA in R Using FactoMineR: Quick Scripts and Videos HCPC Using FactoMineR: Videos See also Multidimensional Scaling Essentials: Algorithms and R Code
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Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML40% discount code: serranoytA conceptual description of principal compone...
