how do tell if its better to standardize your data matrix first when you do principal component analysis in R ?

Question

Hi guys ,

Im trying to do principal component analysis in R . There is 2 ways of doing it , I believe. One is doing principal component analysis right away the other way is standardizing the matrix first using s = scale(m)and then apply principal component analysis.
How do I tell what result is better ? What values in particular should i look at . I already managed to find the eigenvalues and eigenvectors , the proportion of variance for each eigenvector using both methods.

I noticed that the proportion of the variance for the first pca without standardizing had a larger value . Is there a meaning to it ? Isnt this always the case?

At last , if I am supposed to predict a variable ie weight should I drop the variable ie weight from my data matrix when I do principal component analysis ?

Answer 1

Are your variables measured on a common scale? If yes, then don't scale. If no, then it's probably a good idea to scale.

If you are trying to predict the value of another variable, PCA is probably not the correct tool. Maybe you should look at a regression model instead.