Practical Guide To Principal Component Methods in R

This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R.

You will learn:

• Principal Component Analysis (PCA) for summarizing a large dataset of continuous variables
• Simple Correspondence Analysis (CA) for large contingency tables formed by two categorical variables
• Multiple Correspondence Analysis (MCA) for a data set with more than 2 categorical variables
• Methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA).
• Hierarchical Clustering on Principal Components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables

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