## HCPC Using FactoMineR: Video

This page presents series of course videos on clustering methods for analyzing multivariate data. The main objective is either (i) to identify groups of individuals with a similar profile,... [Read more]

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Good afternoon, your sample illuatration is great.

I just follow your step to test on my sample but got the following errors. My sample is also text file. Any idea? thank you.

> m <- as.matrix(dtm)

Er... [Read more]

By dennis

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This page presents a hand-curated list of **courses** on **principal component methods** by the experts. Principal component methods are used to summarize and visualize a multivariate data set containing a large number of variables.

Depending on the type of variables (categorical or quantitative) in the data set, you should apply a specific principal component method. This course presents the 5 essential principal component methods and clustering.

### Contents

1) Principal Component Analysis (PCA)

PCA is used to analyze data sets containing quantitative variables.

2) Correspondence Analysis (CA)

CA is used to analyze a contingency table formed by two categorical variables.

3) Multiple Correspondence Analysis (MCA)

MCA is used to analyze data sets containing qualitative variables; for example: survey data.

4) Factor Analysis of Mixed Data (FAMD)

FAMD is use to analyze data sets containing a mix of both quantitative and qualitaive variables.

5) Multiple Factor Analysis (MFA)

MFA is used to analyze a complex data table in which individuals are described by several sets of variables (quantitative and /or qualitative) structured into groups.

6) HCPC: Hierarchical Clustering on Principal Components

The HCPC method is used to combine principal component methods (PCA, CA, MCA, FAMD, MFA) and clustering methods ( hierarchical clustering and k-means clustering). This is helpful, for example, when you want to perform clustering on categorical variable. In this case, you first need to compute (M)CA on your data set. Next you can compute clustering on the (M)CA results using the HCPC algorithm.

7) See also: Factoextra R Package: Easy Multivariate Data Analyses and Elegant Visualization

### Related Books

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Depending on the type of variables (categorical or quantitative) in the data set, you should apply a specific principal component method. This course presents the 5 essential principal component methods and clustering.

1) Principal Component Analysis (PCA)

PCA is used to analyze data sets containing quantitative variables.

2) Correspondence Analysis (CA)

CA is used to analyze a contingency table formed by two categorical variables.

3) Multiple Correspondence Analysis (MCA)

MCA is used to analyze data sets containing qualitative variables; for example: survey data.

4) Factor Analysis of Mixed Data (FAMD)

FAMD is use to analyze data sets containing a mix of both quantitative and qualitaive variables.

5) Multiple Factor Analysis (MFA)

MFA is used to analyze a complex data table in which individuals are described by several sets of variables (quantitative and /or qualitative) structured into groups.

6) HCPC: Hierarchical Clustering on Principal Components

The HCPC method is used to combine principal component methods (PCA, CA, MCA, FAMD, MFA) and clustering methods ( hierarchical clustering and k-means clustering). This is helpful, for example, when you want to perform clustering on categorical variable. In this case, you first need to compute (M)CA on your data set. Next you can compute clustering on the (M)CA results using the HCPC algorithm.

7) See also: Factoextra R Package: Easy Multivariate Data Analyses and Elegant Visualization

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In this article, you’ll learn how MFA (Multiple Factor Analysis) works, as well as, how to easily compute and interpret MFA in R using the FactoMineR package.
Recall that MFA is a multivariate... [Read more]

Sources :
François Husson

Factor analysis of mixed data (FAMD) is dedicated to analyze a data set containing both categorical and continuous variables.
This article provides a quick start R code and video showing a... [Read more]

This article presents quick start R code and video series for computing MCA (Multiple Correspondence Analysis) in R, using the FactoMineR package. Recall that MCA is used for analyzing... [Read more]

Sources :
François Husson

This page shows quick start R code to compute correspondence analysis- CA in R using the FactoMineR package.
Additionaly, we present series of course videos on correspondence analysis, which is a... [Read more]

Sources :
François Husson

This article starts by providing a quick start R code for computing PCA in R, using the FactoMineR, and continues by presenting series of PCA video courses (by François Husson).
Recall that PCA... [Read more]

Sources :
François Husson