I understand that if the scale of the different variables varies(for example, some expressed in absolute form while other in percentages), that will cause problem in Principal component analysis (PCA). I read two PCA analysis that first use scale() function to standlize the dataset: 1) from R action 2) from R bloggers.
My question is, if I want to do PCA for variables expressed in terms of percentages. Do I want to still use scale function? It feel like the percentages already were normalized and can be used directly. Can anyone explain why to use scale function?
The variables are social-economic factors, including count of school numbers, population density, housing unit density, green space area percentage, etc. They are mostly expressed in terms of percentages, ranging from 0 to 100%. For the variables not in percentage (e.g., count of school number), I converted them using value/max value, so that they finally all range from 0 to 100%.
Thanks to Whuber's suggestion, I see that: "standardization makes a difference in the results but isn't absolutely necessary".
1) Useful posts to understand PCA correlation covariance