Should I include covariates (i.e. age and sex) in the principal component regression as predictors? Or do I not need to do that because they were accounted for in the PCA?
Any help would be greatly appreciated.
Edit: Principal component analysis was used on the highly correlated independent variables (a,b,c,d,e,f,g). Also covariates age and sex were added in the principal component. from that, I got 4 principal components that accounted for most of the variation in the data. The rest of the components were not included in my analysis because they did not contribute much to the variance in the data, that included age and sex. Now I would like to perform a regression analysis on the principal components that explain most of the variance in the data. Should I only include PC1 and PC2 or should I also include PC1, PC2, age and sex? Would that be redundant to add them into the model once again?