Multivariate Exploratory Data Analysis and Data Mining
Analysis of variance with the contrasts sum (the sum of the coefficien...
Function to better position the labels on the graphs
Correspondence Analysis (CA)
Correspondence Analysis on Generalised Aggregated Lexical Table (CaGal...
Categories description
Calculate the RV coefficient and test its significance
Continuous variable description
Construct confidence ellipses
Description of frequencies
Dimension description
Dual Multiple Factor Analysis (DMFA)
Draw confidence ellipses in CA
Estimate the number of components in Principal Component Analysis
Multivariate Exploratory Data Analysis and Data Mining with R
Factor Analysis for Mixed Data
Generalised Procrustes Analysis
Make graph of variables
Hierarchical Clustering on Principle Components (HCPC)
Hierarchical Multiple Factor Analysis
Linear Model with AIC or BIC selection, and with the contrasts sum (th...
Multiple Correspondence Analysis (MCA)
Perform pairwise means comparisons
Multiple Factor Analysis (MFA)
Principal Component Analysis (PCA)
Draw the Correspondence Analysis (CA) graphs
Draw the Correspondence Analysis on Generalised Aggregated Lexical Tab...
Plots for description of clusters (catdes)
Draw the Dual Multiple Factor Analysis (DMFA) graphs
Draw the Multiple Factor Analysis for Mixt Data graphs
Draw the General Procrustes Analysis (GPA) map
Draw an interactive General Procrustes Analysis (GPA) map
Plots for Hierarchical Classification on Principle Components (HCPC) r...
Draw the Hierarchical Multiple Factor Analysis (HMFA) graphs
Draw the Multiple Correspondence Analysis (MCA) graphs
Draw the means comparisons
Draw the Multiple Factor Analysis (MFA) graphs
Plot an interactive Multiple Factor Analysis (MFA) graph
Draw the Principal Component Analysis (PCA) graphs
Draw confidence ellipses around the categories
Predict projection for new rows with Correspondence Analysis
Predict projection for new rows with Factor Analysis of Mixed Data
Predict method for Linear Model Fits
Predict projection for new rows with Multiple Correspondence Analysis
Predict projection for new rows with Multiple Factor Analysis
Predict projection for new rows with Principal Component Analysis
Scatter plot and additional variables with quality of representation c...
Print the AovSum results
Print the Correspondance Analysis (CA) results
Print the Correspondence Analysis on Generalised Aggregated Lexical Ta...
Print the catdes results
Print the condes results
Print the Multiple Factor Analysis of mixt Data (FAMD) results
Print the Generalized Procrustes Analysis (GPA) results
Print the Hierarchical Clustering on Principal Components (HCPC) resul...
Print the Hierarchical Multiple Factor Analysis results
Print the LinearModel results
Print the Multiple Correspondance Analysis (MCA) results
Print the Multiple Factor Analysis results
Print the Principal Component Analysis (PCA) results
Reconstruction of the data from the PCA, CA or MFA results
Select variables in multiple linear regression
Simulate by bootstrap
Printing summeries of ca objects
Printing summaries of CaGalt objects
Printing summeries of FAMD objects
Printing summeries of MCA objects
Printing summaries of MFA objects
Printing summeries of PCA objects
Singular Value Decomposition of a Matrix
Make a disjunctive table when missing values are present
Make a disjonctif table
Text mining
Print in a file
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) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).