![]() Unfortunately, there are many different ways to calculate an R 2 for logistic regression, and no consensus on which one is best. Along the way, I’m going to retract one of my long-standing recommendations regarding these measures. In this post, I’m going to focus on R 2 measures of predictive power. In a later post, I’ll discuss the second approach to model fit, and I’ll explain why I don’t like the Hosmer-Lemeshow goodness-of-fit test. ![]() The other is to test whether the model needs to be more complex, specifically, whether it needs additional nonlinearities and interactions to satisfactorily represent the data. One is to get a measure of how well you can predict the dependent variable based on the independent variables. One of the most frequent questions I get about logistic regression is “How can I tell if my model fits the data?” There are two general approaches to answering this question.
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