Blog Archives

All you need to know on Multiple Factor Analysis …

March 15, 2020
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All you need to know on Multiple Factor Analysis …

Multiple facrtor analysis deals with dataset where variables are organized in groups. Typically, from data coming from different sources of variables. The method highlights a common structure of all the groups, and the specificity of each group. It allows to compare the results of several PCAs or MCAs in a unique frame of reference. The

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All you need to know on clustering with Factoshiny…

March 11, 2020
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All you need to know on clustering with Factoshiny…

The function Factoshiny of the package Factoshiny proposes a complete clustering strategy that allows you: to draw a hierarchical tree and a partition to describe and characterize the clusters by quantitative and categorical variables to consider lots of individuals thanks to the complementarity of Kmeans and clustering algorithms to consider categorical variables or contingency tables Implementation

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All you need to know to analyse a survey with MCA …

March 8, 2020
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All you need to know to analyse a survey with MCA …

All you need to do with MCA to analyse a survey is in Factoshiny! MCA – Multiple Correspondence Analysis – is a method for exploring and visualizing data obtained from a survey or a questionnaire, i.e. datasets with categorical variables. The function Factoshiny of the package Factoshiny allows you to perform MCA in a really

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All you need to know on Correspondence Analysis …

March 3, 2020
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All you need to know on Correspondence Analysis …

Correspondence Analysis – CA – is an exploratory multivariate method for exploring and visualizing contingency tables, i.e. tables on which a chi-squared test can be performed. CA is particularly useful in text mining. The function Factoshiny of the package Factoshiny allows you to perform CA in an easy way. You can include extras information, manage

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All you need to know on PCA …

February 28, 2020
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All you need to know on PCA …

All you need to do with PCA is in Factoshiny! PCA – Principal Component Analysis – is a well known method for exploring and visualizing data. The function Factoshiny of the package Factoshiny allows you to perform PCA in a really easy way. You can include extras information such as categorical variables, manage missing data,

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Factoshiny: an updated version on CRAN!

February 12, 2020
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Factoshiny: an updated version on CRAN!

The newest version of R package Factoshiny (2.2) is now on CRAN! It gives a graphical user interface that allows you to implement exploratory multivariate analyses such as PCA, correspondence analysis, multiple factor analysis or clustering. This interface allows you to modify the graphs interactively, it manages missing data, it gives the lines of code

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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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