An Object-Oriented Solution for Robust Factor Analysis

Taking advantage of the S4 class system of the programming environment R, which facilitates the creation and maintenance of reusable and modular components, an objectoriented solution for robust factor analysis is developed. The solution resides in the package robustfa. In the solution, new S4 classes "Fa", "FaClassic", "FaRobust", "FaCov", "SummaryFa" are created. The robust factor analysis function FaCov() can deal with maximum likelihood estimation, principal component analysis, and principal factor analysis methods. Consider the factor analysis methods (classical or robust), the data input (data or the scaled data), and the running matrix (covariance or correlation) all together, there are 8 combinations. We recommend classical and robust factor analysis using the correlation matrix of the scaled data as the running matrix for theoretical and computational reasons. The design of the solution follows common statistical design patterns. The application of the solution to multivariate data analysis is demonstrated on the hbk data and the stock611 data which themselves are parts of the package robustfa.