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Related Experiment Videos

Computing measures of explained variation for logistic regression models.

M Mittlböck1, M Schemper

  • 1Department of Medical Computer Sciences, University of Vienna, Austria. Martina.Mittlboeck@AKH-Wien.AC.AT

Computer Methods and Programs in Biomedicine
|April 9, 1999
PubMed
Summary
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Logistic regression lacks a standard R2 measure. This study introduces a SAS macro calculating two R2 measures from Pearson and deviance residuals, with adjusted versions to prevent small sample inflation.

Area of Science:

  • Statistics
  • Biostatistics
  • Regression Analysis

Background:

  • The R-squared (R2) metric, representing explained variation, is standard in general linear models.
  • However, logistic regression lacks a universally accepted R2 definition, hindering model comparison and interpretation.
  • Existing R2 measures for logistic regression can be inflated, especially in smaller sample sizes.

Purpose of the Study:

  • To address the absence of a standard R2 measure in logistic regression.
  • To present a SAS macro for calculating two R2 measures based on Pearson and deviance residuals.
  • To provide adjusted versions of these R2 measures to mitigate inflation in small samples.

Main Methods:

  • Development of a SAS macro.
  • Implementation of R2 calculations using Pearson residuals.

Related Experiment Videos

  • Implementation of R2 calculations using deviance residuals.
  • Inclusion of adjusted R2 formulas to correct for sample size.

    • Main Results:

    • The SAS macro successfully computes two distinct R2 measures for logistic regression.
    • Adjusted R2 versions are provided, designed to offer more reliable estimates in smaller datasets.
    • The proposed measures offer a practical solution for quantifying explained variation in logistic models.

    Conclusions:

    • The presented SAS macro provides valuable tools for assessing model fit in logistic regression.
    • The availability of adjusted R2 measures enhances the reliability of explained variation estimates, particularly in small samples.
    • This contributes to more robust interpretation and comparison of logistic regression models.