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:

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:

• Sensory jurys data: the data set  –  Rmarkdown– the script with the outputs
• Sensory groups data: Rmarkdown – the script with the outputs

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:

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:

Data – issues

Visualizing the point cloud of individuals

Visualizing the cloud of categories

Interpretation aids

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

Materials

Here is the material used in the videos:

• Hobbies data: the data set– Rmarkdown – the script with the outputs
• Tea data: the data set  –  Rmarkdown– the script with the outputs

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:

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:

• Nobel data: the data set – Rmarkdown – the script with the outputs
• Birth data: the data set – Rmarkdown – the script with the outputs

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:

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:

• Wines data: the data set  –  Rmarkdown– the script with the outputs
• Decathlon data: the data set– Rmarkdown – the script with the outputs

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.

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 of this package is that you don’t need to know the lines of code, and moreover that you can modify the graphical options and see instantly how the graphs are improved. You can visualize this video to see how to use Factoshiny.