Does your logistic regression model suck PERFECTION This

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Does your logistic regression model suck?

Does your logistic regression model suck?

PERFECTION!

PERFECTION!

This is bad Model Convergence Status Quasi-complete separation of data points detected. Warning: The

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.

Complete separation X Group 0 0 1 0 2 0 3 0 4 1

Complete separation X Group 0 0 1 0 2 0 3 0 4 1 5 1 6 1 7 1

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

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

Quasi-complete separation Like complete separation BUT one or more points where the points have

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

there is not a unique maximum likelihood estimate

there is not a unique maximum likelihood estimate

Depressing words from Paul Allison “FOR ANY DICHOTOMOUS INDEPENDENT VARIABLE IN A LOGISTIC REGRESSION,

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. ”

What the hell happened?

What the hell happened?

Solution? • Collect more data. • Figure out why your data are missing and

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

Model Fit Statistics

Model Fit Statistics

Bonus fact! • You can compare nested models using the model fit statistics and

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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