Blog Archives

Missing values imputation with missMDA

March 7, 2017
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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. Based on dimensionality reduction

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Text Mining on Wine Description

February 21, 2017
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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

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PCA – hierarchical tree – partition: Why do we need to choose for visualizing data?

February 20, 2017
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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. In the figure below, the hierarchical tree

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How to perform PCA with R?

February 17, 2017
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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,

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Exploratory Multivariate Data Analysis with R- enroll now in the MOOC

February 17, 2017
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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 applications:

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Interactive plots in PCA with Factoshiny

February 16, 2017
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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 selection and addition of supplementary information. The main advantage

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MOOC on Exploratory Multivariate Data Analysis – enroll now

February 14, 2017
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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: principal component

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