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Troubleshooting Logistic Regression: Addressing Model Convergence and Data Separation Issues

Is your logistic regression model failing to converge or showing signs of separation? This guide discusses challenges like quasi-complete separation and issues with maximum likelihood estimation. We delve into common warning signs that indicate the validity of your model fit is questionable, along with insightful quotes from experts like Paul Allison. Possible solutions include collecting more data, addressing missing values, or adjusting problematic categories. Learn how to leverage model fit statistics to compare nested models effectively.

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Troubleshooting Logistic Regression: Addressing Model Convergence and Data Separation Issues

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Presentation Transcript

  1. Does your logistic regression model suck?

  2. PERFECTION!

  3. This is bad Model Convergence Status Quasi-complete separation of data points detected. Warning: The maximum likelihood estimate may not exist. Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable.

  4. Complete separation

  5. If you don’t go to church you will never die

  6. Quasi-complete separation Like complete separation BUT one or more points where the points have both values 1 1 2 1 3 1 4 1 4 0 5 0 6 0

  7. there is not a unique maximum likelihood estimate

  8. Depressing words from Paul Allison “For any dichotomous independent variable in a logistic regression, if there is a zero in the 2 x 2 table formed by that variable and the dependent variable, the ML estimate for the regression coefficient does not exist.”

  9. What the hell happened?

  10. Solution? • Collect more data. • Figure out why your data are missing and fix that. • Delete the category that has the zero cell.. • Delete the variable that is causing the problem

  11. Model Fit Statistics

  12. Bonus fact! • You can compare nested models using the model fit statistics and test if one model is superior to the other

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