Multiple Factor Analysis (MFA) with R using FactoMineR

This video shows how to perform a multiple factor analysis that handles several groups of continuous and/or categorical variables. And how can we improve the...

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Exploratory multivariate analysis with R and FactoMineR

Data analysis methods summary

Principal component analysis (part1: data - practicalities)

Principal component analysis (part 2/3: studying individuals and variables)

Principal component analysis (part 3/3: interpretation aids)

Principal component analysis (PCA) with R

PCA with FactoMineR

Factoshiny: interactive graphs in exploratory multivariate data analysis

Handling missing values in PCA

Correspondence Analysis (part 1/5): Introduction

Correspondence Analysis (part 2/5): Visualizing the row and column clouds

Correspondence Analysis (part 3/5): Inertia and percentage of inertia

Correspondence Analysis (part 4/5): Simultaneous representation

Correspondence Analysis (part 5/5): Interpretation aids

Correspondence Analysis with FactoMineR

Text mining with correspondence analysis

Mutltiple Correspondence Analysis (Part 1/4: Data - issues)

Multiple Correspondence Analysis (part 2/4: Visualizing the point cloud of individuals)

Multiple Correspondence Analysis (Part 3/4: Visualizing the cloud of categories)

Multiple Correspondence Analysis (Part 4/4: Interpretation aids)

Multiple Correspondence Analysis with FactoMineR

Multiple correspondence analysis on an incomplete dataset

Handling missing values in MCA

Clustering (part 1/4: introduction)

Clustering (part 2/4: example and how to choose the number of clusters)

Clustering (part 3/4: the partitioning method K-means)

Clustering (part 4/4: characterizing clusters)

Clustering with FactoMineR

Factorial Analysis on Mixed Data

Tools to interpret the results obtained by principal component methods (PCA, MCA, MFA)

Multiple Factor Analysis (MFA) with R using FactoMineR

Multiple Factor Analysis (MFA) with R using FactoMineR

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Exploratory multivariate analysis with R and FactoMineR

Data analysis methods summary

Principal component analysis (part1: data - practicalities)

Principal component analysis (part 2/3: studying individuals and variables)

Principal component analysis (part 3/3: interpretation aids)

Principal component analysis (PCA) with R

PCA with FactoMineR

Factoshiny: interactive graphs in exploratory multivariate data analysis

Handling missing values in PCA

Correspondence Analysis (part 1/5): Introduction

Correspondence Analysis (part 2/5): Visualizing the row and column clouds

Correspondence Analysis (part 3/5): Inertia and percentage of inertia

Correspondence Analysis (part 4/5): Simultaneous representation

Correspondence Analysis (part 5/5): Interpretation aids

Correspondence Analysis with FactoMineR

Text mining with correspondence analysis

Mutltiple Correspondence Analysis (Part 1/4: Data - issues)

Multiple Correspondence Analysis (part 2/4: Visualizing the point cloud of individuals)

Multiple Correspondence Analysis (Part 3/4: Visualizing the cloud of categories)

Multiple Correspondence Analysis (Part 4/4: Interpretation aids)

Multiple Correspondence Analysis with FactoMineR

Multiple correspondence analysis on an incomplete dataset

Handling missing values in MCA

Clustering (part 1/4: introduction)

Clustering (part 2/4: example and how to choose the number of clusters)

Clustering (part 3/4: the partitioning method K-means)

Clustering (part 4/4: characterizing clusters)

Clustering with FactoMineR

Factorial Analysis on Mixed Data

Tools to interpret the results obtained by principal component methods (PCA, MCA, MFA)

Multiple Factor Analysis (MFA) with R using FactoMineR

Multiple Factor Analysis (MFA) with R using FactoMineR

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