Comparison of Principal Component Regression and Partial Least Squares Regression by R

Principle component regression (PCR) and partial least squares regression (PLSR) are two methodologies commonly used to solve dimension reduction and the multi-co linearity problem. The goal of this paper is to analyze and predict dependent variable from predictor variables using the methods of PCR and PLSR by an example. The results show that the main difference between PCR and PLSR is that PCR often needs more components than PLSR to achieve the same prediction error and prediction ability.