Separation is best fixed with penalized regression, which adds a “penalty” to the log-likelihood to prevent infinite coefficients. Poor data quality is a common (and easy-to-fix) cause of non-convergence. It makes coefficients unstable (large standard errors) and can cause IRLS to oscillate between values instead of converging. Separation causes IRLS to “blow up” coefficients to infinity (since the model tries to assign perfect prediction). Non-convergence is almost always a data or model problem, not a flaw in R.

You can turn this frustrating warning into an opportunity to build a better model. Finally, check if the model predicts well (not just fits the training data). First, ensure there’s no warning when fitting the model. Once you’ve fixed the problem, you need to confirm convergence and validate your model (to ensure it’s not just “converged” but also useful). Firth’s method works by penalizing the likelihood with the Jeffreys prior, which shrinks coefficients away from infinity. The most popular method for logistic regression is Firth’s penalized likelihood.

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