S+ FDA

S+ functional data analysis. User’s manual for Windows. S+ Functional Data Analysis is the first commercial object oriented package for exploring, modeling, and analyzing functional data. Functional data analysis (FDA) handles longitudinal data and treats each observation as a function of time (or another variable). The functions are related. The goal is to analyze a sample of functions instead of a sample of related points. FDA differs from traditional data analytic techniques in a number of ways. Functions can be evaluated at any point in their domain. Derivatives and integrals, which may provide better information (e.g., graphical) than the original data, are easily computed and used in multivariate and ther functional analytic methods.par The analyst using S+ FDA can handle irregularly spaced data or data with missing values. For large amounts of data, working with a functional representation can save storage. Moreover, S+ FDA provides a variety of analytic techniques for functional data including linear models, generalized linear models, principal components, canonical correlations, principal differential analysis, and clustering.par This book can be considered as a companion to the books of {it J.O. Ramsay} and {it B.W. Silverman}, `Functional data analysis.’ 2nd ed. (2005; Zbl 1079.62006), and `Applied functional data analysis. Methods and case studies.’ (2002; Zbl 1011.62002). This user’s manual also provides the documentation for the S+ FDA library for SPlus.