I have some features (predictor variables) that I want to regress on a response. The predictor features are correlated with each other. Hence, for performing regression I need to do a principal component analysis first, compute new uncorrelated variables and then perform linear regression.
I also calculated partial correlations of each predictor variable with response by controlling for some reported confounding factors.
My question is - do variables showing high partial correlations with response, also explain a high proportion of variance explained by a principal component? Is it necessary? If not how to compare PCA and partial correlation results? Which is better? I'm performing PCA-based regression for the first time and am a complete novice. Please help.