FPCA Procedure

The FPCA procedure performs functional principal component analysis (FPCA) on dense, regularly spaced functional data. This technique is widely used to analyze variations in functional data, reduce dimensionality, and extract meaningful patterns. PROC FPCA decomposes functions into orthonormal basis functions (eigenfunctions) and associated scores, providing a compact representation of the data.

FPCA is useful in a variety of applications, including signal processing, climate analysis, and biomedical studies. The primary outputs of this type of analysis include eigenvalues, eigenvectors (eigenfunctions), and functional principal component scores, which help analysts summarize and interpret the functional data structure.