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How to choose the best logistic regression model in R?

Linear regression

Linear discriminant analysis

Binomial regression

Logistic regression

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Open access•Journal Article•DOI Comparison of Methodologies for Probabilistic Quantitative Precipitation Forecasting Scott Applequist, Gregory E. Gahrs, Richard L. Pfeffer, Xufeng Niu - Show less +3 more 01 Aug 2002-Weather and Forecasting 95 Citations	The results indicate that logistic regression performs best among all methodologies.
Journal Article•DOI Modeling categorical variables by logistic regression. Chao-Ying Joanne Peng, Barbara D. Manz, Juanita Keck - Show less +2 more 01 May 2001-American Journal of Health Behavior 37 Citations	Logistic regression is useful for health-related research in which outcomes of interest are often categorical.

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What are the advantages and disadvantages of using logistic regression?5 answersLogistic regression has several advantages. It is a useful technique for modeling binary outcomes and can produce odds ratios that have a meaningful interpretation. It allows for the analysis of multiple explanatory variables together, which helps to avoid confounding effects. Logistic regression is widely used in various fields, including biomedical research, ecology, business, and finance. However, there are also limitations to logistic regression. Overparameterization, where there are too many variables for too few events, can lead to implausibly large odds ratios and uninterpretable confidence intervals. The application of logistic regression in management science is rare, possibly due to a lack of literature addressing its specifics and reporting guidelines. It is important to ensure that relevant assumptions, such as linearity and absence of multicollinearity, are met. Additionally, an adequate number of events per independent variable is needed to avoid overfitting.

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