Blog Archives

Enroll now in the MOOC on Exploratory Multivariate Data Analysis with R

March 4, 2018
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Enroll now in the MOOC on Exploratory Multivariate Data Analysis with R

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 5th of March 2018. This MOOC focuses on 5 essential and basic methods, those with the largest potential in terms of

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Can we believe in the imputations?

August 5, 2017
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Can we believe in the imputations?

A popular approach to deal with missing values is to impute the data to get a complete dataset on which any statistical method can be applied. Many imputation methods are available and provide a completed dataset in any cases, whatever the number of individuals and/or variables, the percentage of missing values, the pattern of missing

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Multiple imputation for continuous and categorical data

August 5, 2017
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Multiple imputation for continuous and categorical data

“The idea of imputation is both seductive and dangerous” (R.J.A Little & D.B. Rubin). Indeed, a predicted value is considered as an observed one and the uncertainty of prediction is ignored, conducting to bad inferences with missing values. That is why Multiple Imputation is recommended. The missMDA package quickly generates several imputed datasets with quantitative variables and/or categorical

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Clustering with FactoMineR

August 4, 2017
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Clustering with FactoMineR

Here is a course with videos that present Hierarchical clustering and its complementary with principal component methods. Four videos present a course on clustering, how to determine the number of clusters, how to describe the clusters and how to perform the clustering when there are lots of individuals and/or lots of variables. Then  you will

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Multiple Factor Analysis to analyse several data tables

July 18, 2017
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Multiple Factor Analysis to analyse several data tables

How to take into account and how to compare information from different information sources? Multiple Factor Analysis is a principal Component Methods that deals with datasets that contain quantitative and/or categorical variables that are structured by groups. Here is a course with videos that present the method named Multiple Factor Analysis. Multiple Factor Analysis (MFA)

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Multiple Correspondence Analysis with FactoMineR

July 18, 2017
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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

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Course on Multiple Correspondence Analysis with FactoMineR

July 13, 2017
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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

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Correspondence Analysis with FactoMineR

July 13, 2017
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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

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PCA course using FactoMineR

July 13, 2017
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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

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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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