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GLMCORR: Stata module for correlation measure of predictive power for GLMs

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Nicholas J. Cox () (Durham University)

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Abstract

glmcorr calculates the correlation and the jackknifed correlation between the response and the fitted or predicted response together with the root mean square error after glm. Zheng and Agresti (Statistics in Medicine 2000) discuss this correlation as a general measure of predictive power for GLMs.

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File URL: http://fmwww.bc.edu/repec/bocode/g/glmcorr.ado
File Format: text/plain
File Function: program code
Download Restriction: no
File URL: http://fmwww.bc.edu/repec/bocode/g/glmcorr.hlp
File Format: text/plain
File Function: help file
Download Restriction: no

Publisher Info
Software component provided by Boston College Department of Economics in its series Statistical Software Components with number S428101.

Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
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Programming language: Stata
Requires: Stata version 7.0
Date of creation: 08 Oct 2002
Date of revision: 04 Aug 2004
Handle: RePEc:boc:bocode:s428101

Note: This module may be installed from within Stata by typing "ssc install glmcorr". Windows users should not attempt to download these files with a web browser.
Contact details of provider:
Postal: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA
Phone: 617-552-3670
Fax: +1-617-552-2308
Email:
Web page: http://fmwww.bc.edu/EC/
More information through EDIRC

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Web: http://repec.org/docs/ssc.php

For technical questions regarding this item, or to correct its listing, contact: (Christopher F Baum).

Related research
Keywords: generalized linear model glm diagnostics

Statistics
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This page was last updated on 2008-10-11.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.