R data reduction and visualisation

R routines performing a principal components analysis (PCA) on behavioral data, and regression analysis with the outputed principal components. To help you with the components interpretation, some rotation method are also an option, alongside clutering tecnics. Those routines contains also useful visualization functions for correlation matrix plot, regression results plot, principal components projections plot.

Prerequisites

Data table

You must have in the .xlsx format or .csv format, a structured table containing for all your subjects their clinical description. The table can contanining categorical variables, or continous variable. It is advised that you at least one column dedicated to the subject names, it will be useful for plotting purpose for example to pinpoint a subject data point.

Software

You will need to have on your machine the following software:

• R, at least the 3.6 version

R libraies

In R, the following libraries are mandatory:

• factoextra,
• FactoMineR,
• readxl,
• psych,
• corrplot,
• easyGgplot2,
• ggrepel,
• GPArotation
• NbClust

Scripts

• PCA_multiples_technics: this script perform a Principal Components Analysis, using the classical SVD decomposition, and, depending if the interpretation of the components are easy, you can rotate them using the psych packages. Rotating the principal components can sometimes help, but depending of the rotation methods, resulting components can no longer be orthogonal.
• cluster_on_pca: This simple R function, takes a PCA object from the FactoMineR package, and with a k-means clustering, segment into k categories the projected data points. It can help to interpret the principal components meaning, along with your clinical data.
• plot_linear_model_pcs: Fit a linear model between the outputed Principal Components, and behavioral data. It's a very common technics, to perform a linear regression on the newly created variable as long as you can explain them ;)

Contributors

Dhaif BEKHA