PCA and illustrative variables add-in

[#1]

The goal of PCA is to reduce the dimensionality in a set of correlated variables into a smaller set of uncorrelated variables that explain the majority of the variation in the original variables.

Illustrative variables have no impact on the construction of the new components. They are used to help interpret the components and relate them to other variables.

Fig1 - example of illustratives variables on the two components axis