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A graphic and tabular variable deduction method in logistic regression

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  • Guoping Zeng

Abstract

Logistic regression assumes linearity between independent variables and log odds. Currently, there are two methods to check the linearity as part of variable deduction. The first method is to use the well-known Box–Tidwell test, which runs logistic regression with interaction terms for all independent variables. The second method estimates log odds by running logistic regression for all independent variables. However, these two methods have some common issues. Firstly, they don’t treat missing values, leading loss of information. Secondly, they don’t treat outliers. Thirdly, both run logistic regression for all independent variables, which is infeasible for big data. In this article, we propose a graphic and tabular variable deduction method by simultaneously checking the linearity assumption of each independent variable using a logit plot and treating missing values and outliers using a summary table. Our novel method is easy to implement for any real-life predictive modeling using logistic regression.

Suggested Citation

  • Guoping Zeng, 2022. "A graphic and tabular variable deduction method in logistic regression," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(16), pages 5412-5427, August.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:16:p:5412-5427
    DOI: 10.1080/03610926.2020.1839499
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