An S-Plus Functional Data Analysis Module

S-Plus 功能数据分析模块

• 批准号: 6622233
• 负责人: JAMES SCHIMERT
• 金额: $ 37.36万
• 依托单位: INSIGHTFUL CORPORATION
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-07-01 至 2005-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6622233
• 关键词: clinical research; computer data analysis; computer program /software; computer system design /evaluation; method development; statistics /biometry; time resolved data

Functional data arise in many fields of medical research, with examples from studies of growth patterns, gait, melanoma incidence rates, CD4 counts and many other areas. 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 of viewing such data as functions rather than disconnected points, perhaps the most important being the ability to routinely including derivative information into the analysis. Historically, functional data has been analyzed using multi-variate or time series methods, but these methods do not work well 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. This module will e a commercial implementation of the exploratory methods developed by Ramsay and Silverman (1997), with many extensions, including new methods for generalized linear models, survival analysis, and non-linear least square models, and extension to functions with different bases. The new module will seamlessly integrate functional data analysis methods into S-Plus. PROPOSED COMMERCIAL APPLICATIONS: As computers become integrated into daily lie, 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 method for implementing these models will find a ready market.

功能数据出现在医学研究的许多领域中，其中包括生长模式，步态，黑色素瘤发病率，CD4计数和许多其他领域的研究。实际上，随着时间的时间（或空间）收集的任何一组测量值，包括生存分析中的时间依赖性协变量，都可以视为功能数据。将数据视为函数而不是断开点有许多优势，也许最重要的是可以将衍生信息定期纳入分析的能力。从历史上看，已经使用多变量或时间序列方法对功能数据进行了分析，但是这些方法对于在不同受试者的不同时间测量的不规则间隔数据或数据都无法正常工作。最近的进步使得在这里分析数据作为功能成为可能，我们建议实施一个S-Plus模块以进行功能数据分析。该模块将对Ramsay和Silverman（1997）开发的探索方法进行商业实施，其中包括许多扩展，包括用于通用线性模型，生存分析和非线性最小二平方模型的新方法，以及具有不同基础的功能的扩展。新模块将无缝将功能数据分析方法无缝地整合到S-Plus中。拟议的商业应用：随着计算机的日常谎言，研究人员收集功能数据的能力变得越来越普遍。当前没有用于处理功能数据的商业产品。所提出的方法比现有技术具有显着优势。实施这些模型的精心设计和全面的方法将找到一个现成的市场。