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Data analysis methods summary
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Principal component analysis (part1: data - practicalities)
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Principal component analysis (part 2/3: studying individuals and variables)
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Principal component analysis (part 3/3: interpretation aids)
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Principal component analysis (PCA) with R
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PCA with FactoMineR
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Factoshiny: interactive graphs in exploratory multivariate data analysis
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Handling missing values in PCA
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Correspondence Analysis (part 1/5): Introduction
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Correspondence Analysis (part 2/5): Visualizing the row and column clouds
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Correspondence Analysis (part 3/5): Inertia and percentage of inertia
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Correspondence Analysis (part 4/5): Simultaneous representation
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Correspondence Analysis (part 5/5): Interpretation aids
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Correspondence Analysis with FactoMineR
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Text mining with correspondence analysis
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Mutltiple Correspondence Analysis (Part 1/4: Data - issues)
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Multiple Correspondence Analysis (part 2/4: Visualizing the point cloud of individuals)
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Multiple Correspondence Analysis (Part 3/4: Visualizing the cloud of categories)
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Multiple Correspondence Analysis (Part 4/4: Interpretation aids)
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Multiple Correspondence Analysis with FactoMineR
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Multiple correspondence analysis on an incomplete dataset
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Handling missing values in MCA
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Clustering (part 1/4: introduction)
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Clustering (part 2/4: example and how to choose the number of clusters)
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Clustering (part 3/4: the partitioning method K-means)
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Clustering (part 4/4: characterizing clusters)
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Clustering with FactoMineR
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Factorial Analysis on Mixed Data
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Tools to interpret the results obtained by principal component methods (PCA, MCA, MFA)
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Multiple Factor Analysis (MFA) with R using FactoMineR
Handling missing values in PCA
Transcript
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