Regression Analysis with R

Principal Component Analysis

Principal Component Analysis (PCA) generates a new set of variables, among them uncorrelated, called principal components; each main component is a linear combination of the original variables. All principal components are orthogonal to each other, so there is no redundant information. The principal components as a whole, constitute an orthogonal basis for the data space. The goal of PCA is to explain the maximum amount of variance with the fewest number of principal components. It is a form of multidimensional scaling. It is a linear transformation of the variables into a lower dimensional space that retains the maximum amount of information about the variables. A principal component is therefore a combination ...