ASSIST: A Suite of S-plus Functions "Implementing Spline Smoothing Techniques"

• Activity Number: 362
• Type: Contributed
• Date/Time: Wednesday, August 14, 2002 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Computing*
• Author(s): Yuedong Wang*+ and Chunlei Ke
• Affiliation(s): University of California, Santa Barbara and St. Jude Medical
• Keywords: smoothing spline ; statistical software ; non-parametric models

Abstract

We present a suite of user friendly S-plus functions for fitting various smoothing spline models, including: (a) non-parametric regression models for independent and correlated Gaussian data, and for independent binomial, Poisson and Gamma data; (b) semi-parametric linear mixed-effects models; (c) non-parametric nonlinear regression models; (d) semi-parametric nonlinear regression models; and (e) semi-parametric nonlinear mixed-effects models. The general form of smoothing splines based on reproducing kernel Hilbert spaces is used to model non-parametric functions. Thus, these S-plus functions deal with many different situations in a unified fashion. Some well-known special cases are polynomial splines, periodic splines, spherical splines, thin-plate splines, l-splines, generalized additive models, smoothing spline ANOVA models, projection pursuit models, multiple index models, varying coefficient models, functional linear models, and self-modeling nonlinear regression models. One goal of this software development is to collect existing programs and make them user friendly so that more researchers can use them with ease. We have also written several new programs.