An S-Plus Functional Data Analysis Module

Information

  • Research Project
  • 6622233
  • ApplicationId
    6622233
  • Core Project Number
    R44CA086539
  • Full Project Number
    5R44CA086539-03
  • Serial Number
    86539
  • FOA Number
  • Sub Project Id
  • Project Start Date
    7/1/2000 - 24 years ago
  • Project End Date
    5/31/2005 - 19 years ago
  • Program Officer Name
    KELTY, MIRIAM F.
  • Budget Start Date
    6/1/2003 - 21 years ago
  • Budget End Date
    5/31/2005 - 19 years ago
  • Fiscal Year
    2003
  • Support Year
    3
  • Suffix
  • Award Notice Date
    5/28/2003 - 21 years ago
Organizations

An S-Plus Functional Data Analysis Module

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.

IC Name
NATIONAL CANCER INSTITUTE
  • Activity
    R44
  • Administering IC
    CA
  • Application Type
    5
  • Direct Cost Amount
  • Indirect Cost Amount
  • Total Cost
    373603
  • Sub Project Total Cost
  • ARRA Funded
  • CFDA Code
    393
  • Ed Inst. Type
  • Funding ICs
    NCI:373603\
  • Funding Mechanism
  • Study Section
    ZRG1
  • Study Section Name
    Special Emphasis Panel
  • Organization Name
    INSIGHTFUL CORPORATION
  • Organization Department
  • Organization DUNS
    150683779
  • Organization City
    SEATTLE
  • Organization State
    WA
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    98109
  • Organization District
    UNITED STATES