AN S-PLUS FUNCTIONAL DATA ANALYSIS MODULE

功能数据出现在医学研究的许多领域，例如生长模式、步态、黑色素瘤发病率、CD4计数和许多其他研究。事实上，在时间(或空间)上收集的任何一组测量，包括生存分析中的时间依赖协变量，都可以被认为是功能性数据。将这类数据视为函数而不是互不相连的点有许多优势，其中最重要的可能是能够例行公事地将派生信息纳入分析。在历史上，函数数据的分析通常采用多元或时间序列的方法，但这些方法对于不同对象的不规则间隔数据或在不同时间测量的数据都很弱。最近的进展使分析这类数据成为可能。在这里，我们建议实现一个用于功能数据分析的S+模块。该模块将是Ramsay和Silverman(1997)开发的探索性方法的商业实现，扩展到自适应回归样条和基于局部多项式核的平滑器。我们还计划将该方法扩展到包括广义线性模型和生存分析模型。新模块将无缝地将功能数据分析方法集成到S+中。拟议的商业应用：随着计算机融入日常生活，研究人员收集功能数据的能力变得越来越普遍。目前还没有可用于处理功能数据的商业产品。与现有技术相比，所提出的方法具有显著的优势。用于实施这些模型的精心设计和全面的模块将找到现成的市场。

Functional data arise in many fields of medical research, with examples from studies of growth patterns, gait, melanoma incidence rates, CD4 counts and many others. Indeed, any set of measurements gathered over time (or space), including time dependent covariates in survival analysis, may be thought of as functional data. There are many advantages to viewing such data as functions rather than disconnected points, perhaps the most important being the ability to routinely include derivative information into the analysis. Historically, functional data has been analyzed using multivariate or time series methods, but these methods are weak for irregularly spaced data or data measured at different times for different subjects. Recent advances make it possible to analyze such data as functions. Here we propose to implement an S-Plus module for functional data analysis. The module will be a commercial implementation of the exploratory methods developed by Ramsay and Silverman (1997), with extensions to adaptive regression splines and local polynomial kernel based smoothers. We also plan to extend the methodology to include generalized linear models and survival analysis models. The new module will seamlessly integrate functional data analysis methods into the S-Plus. PROPOSED COMMERCIAL APPLICATIONS: As computers become integrated into daily life, the ability of researchers to collect functional data is becoming more common. There are currently no commercial products available for handling functional data. The proposed methods have significant advantages over existing techniques. A well designed and comprehensive module for implementing these models will find a ready market.