IDEAS home Printed from https://ideas.repec.org/c/boc/bocode/s458728.html
 

ODA: Stata module for conducting Optimal Discriminant Analysis (Windows only)

Author

Listed:
  • Ariel Linden

    (Linden Consulting Group, LLC)

Programming Language

Stata

Abstract

Optimal Discriminant Analysis (ODA) is a machine learning algorithm that was introduced over 25 years ago to offer an alternative analytic approach to conventional statistical methods commonly used in research (Yarnold & Soltysik 1991). Its appeal lies in its simplicity, flexibility and accuracy as compared with conventional statistical methods (Yarnold & Soltysik 2005, 2016). oda is a wrapper program for the Optimal Discriminant Analysis (ODA) software (Yarnold & Soltysik 2005, 2016). Therefore, ODA must be installed in order for the oda Stata package to work. ODA software is available at https://odajournal.com/resources/

Suggested Citation

  • Ariel Linden, 2020. "ODA: Stata module for conducting Optimal Discriminant Analysis (Windows only)," Statistical Software Components S458728, Boston College Department of Economics, revised 13 Feb 2020.
  • Handle: RePEc:boc:bocode:s458728
    Note: This module should be installed from within Stata by typing "ssc install oda". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/o/oda.ado
    File Function: program code
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/o/oda.sthlp
    File Function: help file
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:bocode:s458728. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/debocus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.