Multivariate Exploratory Data Analysis
Compute col contributions
Compute col coordinates
Compute col squared cosines
Compute col inertia
Compute row contributions
Compute row coordinates
Compute row squared cosines
Compute row inertia
Data standardization for CA
Compute eigenvalues and eigenvectors for CA
Perform CA with FactoMineR's style
Perform MFA with FactoMineR's style
Perform PCA with FactoMineR's style
Compute eigenvalues and eigenvectors
Compute individual contributions
Compute coordinates for individuals
Compute individual squared cosines
Data standardization for PCA
Compute variable contributions
Compute variable coordinates
Compute variable correlation
Compute variable squared cosines
Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) when variables are categorical, Multiple Factor Analysis (MFA) when variables are structured in groups.
Useful links