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 groups of variables can be continuous, categorical or can be a contingency table.

Implementation with R software

See this video and the audio transcription of this video:

MFA_img

Course videos

Theorectical and practical informations on Multiple Factor Analysis are available in these 4 course videos:

  1. Introduction
  2. Weighting and global PCA
  3. Study of the groups of variables
  4. Complements: qualitative groups, frenquency tables

Here are the slides and the audio transcription of the course.

Materials

Here is the material used in the videos:

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 with R software

See this video and the audio transcription of this video:

CLASSIFFacto

Course videos

Theorectical and practical informations on clustering are available in these 4 course videos (here are the slides and the audio transcription of the courses):

 

clustering1

Introduction

 

 

 

 

Materials

Here is the material used in the videos:

 

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 easy way. You can include extras information such as quantitative variables, manage missing data, draw and improve the graphs interactively, draw confidence ellipses, have several numeric indicators as outputs, perform clustering on the MCA results, and even have an automatic interpretation of the results. Finally, the function returns the lines of code to parameterize the analysis and redo the graphs, which makes the analysis reproducible.

Implementation with R software

See this video and the audio transcription of this video:

ACM_img

The lines of code to do a MCA:

install.packages(Factoshiny)
library(Factoshiny)
data(tea)
result <- Factoshiny(tea)

 

Course videos

Theorectical and practical informations on Multiple Correspondence Analysis are available in these 4 course videos:

Here are the slides and the audio transcription of the course.

Materials

Here is the material used in the videos:

Management of missing data

This video gives more information on the management of missing data in MCA.

If you want to see more methods on Exploratory Data Analysis, follow this link.

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 missing data, draw and improve the graph interactively, have several numeric indicators as outputs, perform clustering on the CA results, and even have an automatic interpretation of the results. Finally, the function returns the lines of code to parameterize the analysis and redo the graphs, which makes the analysis reproducible.

Implementation with R software

See this video and the audio transcription of this video:

CA_img

The lines of code to do a Correspondence Analysis:

install.packages(Factoshiny)
library(Factoshiny)
data(children)
result <- Factoshiny(children)

 

Course videos

Theorectical and practical informations on Correspondence Analysis are available in these 6 course videos:

  1. Introduction
  2. Visualizing the row and column clouds
  3. Inertia and percentage of inertia
  4. Simultaneous representation
  5. Interpretation aids
  6. Text mining with correspondence analysis

Here are the slides and the audio transcription of the course.

Materials

Here is the material used in the videos:

Follow this link if you want to see more methods on Exploratory Data Analysis.

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, draw and improve the graphs interactively, have several numeric indicators as outputs, perform clustering on the PCA results, and even have an automatic interpretation of the results. Finally, the function returns the lines of code to parameterize the analysis and redo the graphs, which makes the analysis reproducible.

See this video and the audio transcription of this video:

PCAFacto

The lines of code to do a PCA:

install.packages(Factoshiny)
library(Factoshiny)
data(decathlon)
result <- Factoshiny(decathlon)

Theorectical and practical informations on PCA are available in these 3 course videos:

  1. Data – practicalities
  2. Studying individuals and variables
  3. Interpretation aids

Here are the slides and the audio transcription of the course.

Here is the material used in the videos:

And here is a video that gives more information on the management of missing data.

Enjoy to make beautiful visualizations of your data!

If you want to see more methods on Exploratory Data Analysis, follow this link.

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 to parameterize the analysis and redo the graphs (reproducibility) and it proposes an automatic report on the results of the analysis.

remove.packages("Factoshiny")
install.packages("Factoshiny")

Try it! Only 1 function to retain: the Factoshiny function (same name as the packages):

library(Factoshiny)
data(decathlon)
result <- Factoshiny(decathlon)

Here is a video that shows how to perform PCA with Factoshiny.

PCAfactoshiny

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 2nd of March 2020.

image_cours

This MOOC focuses on 5 essential and basic methods, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical and clustering. An extension to Multiple Factor Analysis (MFA) will give you the opportunity to analyse more complex dataset that are structured by groups.

This course is application-oriented and many examples and numerous exercises are done with FactoMineR (a package of the free R software) will make the participant efficient and reliable face to data analysis.

See you soon.