Mastering Machine Learning with R - Second Edition

Orthogonal rotation and interpretation

As we discussed previously, the point behind rotation is to maximize the loadings of the variables on a specific component, which helps in simplifying the interpretation by reducing/eliminating the correlation among these components. The method to conduct orthogonal rotation is known as "varimax". There are other non-orthogonal rotation methods that allow correlation across factors/components. The choice of the rotation methodology that you will use in your profession should be based on the pertinent literature, which exceeds the scope of this chapter. Feel free to experiment with this dataset. I think that when in doubt, the starting point for any PCA should be orthogonal rotation.

For this process, ...