Publications by francoishusson
MOOC on Exploratory Multivariate Data Analysis – enroll now
Exploratory multivariate data analysis is studied and teached in a French-way since a long time in France. You can enroll in a MOOC (completely free) on Exploratory Multivariate Data Analysis. The MOOC will start the 27th of February. This MOOC focuses on 4 essential and basic methods, those with the largest potential in terms of applications...
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Interactive plots in PCA with Factoshiny
A beautiful graph tells more than a lenghtly speach!! So it is crucial to improve the graphs obtained by Principal Component Analysis or (Multiple) Correspondence Analysis. The package Factoshiny allows us to easily improve these graphs interactively. The package Factoshiny makes interacting with R and FactoMineR simpler, thus facilitating sel...
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Exploratory Multivariate Data Analysis with R- enroll now in the MOOC
Exploratory multivariate data analysis is studied and has been taught in a “French-way” for a long time in France. You can enroll in a MOOC (completely free) on Exploratory Multivariate Data Analysis. The MOOC will start the 27th of February. This MOOC focuses on 4 essential and basic methods, those with the largest potential in terms of ...
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How to perform PCA with R?
This post shows how to perform PCA with R and the package FactoMineR. If you want to learn more on methods such as PCA, you can enroll in this MOOC (everyting is free): MOOC on Exploratory Multivariate Data Analysis Dataset Here is a wine dataset, with 10 wines and 27 sensory attributes (like sweetness, bitterness, fruity odor, and so on), 2 pref...
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PCA – hierarchical tree – partition: Why do we need to choose for visualizing data?
Principal component methods such as PCA (principal component analysis) or MCA (multiple correspondence analysis) can be used as a pre-processing step before clustering. But principal component methods give also a framework to visualize data. Thus, the clustering methods can be represented onto the map provided by the principal component method....
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Text Mining on Wine Description
Here is an example of text mining with correspondence analysis. Within the context of research into the characteristics of the wines from Chenin vines in the Loire Valley (French wines), a set of 10 dry white wines from Touraine were studied: 5 Touraine Protected Appellation of Origin (AOC) from Sauvignon vines, and 5 Vouvray AOC from Chenin vi...
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Missing values imputation with missMDA
“The best thing to do with missing values is not to have any” (Gertrude Mary Cox) Unfortunately, missing values are ubiquitous and occur for plenty of reasons. One solution is single imputation which consists in replacing missing entries with plausible values. It leads to a complete dataset that can be analyzed by any statistical methods....
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PCA course using FactoMineR
Here is a course with videos that present Principal Component Analysis in a French way. Three videos present a course on PCA, highlighting the way to interpret the data. Then you will find videos presenting the way to implement in FactoMineR, to deal with missing values in PCA thanks to the package missMDA and lastly a video to draw interactive...
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Correspondence Analysis with FactoMineR
Here is a course with videos that present Correspondence Analysis in a French way. Five videos present a course on CA, highlighting the way to interpret the data. Then you will find videos presenting the way to implement in FactoMineR. With this course, you will be stand-alone to perform and interpret results obtain with Correspondence Analysis...
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Course on Multiple Correspondence Analysis with FactoMineR
Here is a course with videos that present Multiple Correspondence Analysis in a French way. The most well-known use of Multiple Correspondence Analysis is: surveys. Four videos present a course on MCA, highlighting the way to interpret the data. Then you will find videos presenting the way to implement MCA in FactoMineR, to deal with missing va...
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