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R squared in logistic regression – The Stats Geek

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In previous posts I’ve looked at R squared in linear regression, and argued that I think it is more appropriate to think of it is a measure of explained variation, rather than goodness of fit. Of course not all outcomes/dependent variables can be reasonably modelled using linear regression. Perhaps the second most common type of regression model is logistic regression, which is appropriate for binary outcome data. How is R squared calculated for a logistic regression model? Well it turns out that it is not entirely obvious what its definition should be. Over the years, different researchers have proposed different measures for logistic regression, with the objective usually that the measure inherits the properties of the familiar R squared from linear regression. In this post I’m going to focus on one of them, which is McFadden’s R squared, and it is the default ‘pseudo R2’ value reported by the Stata package. There are certain drawbacks to this measure – if you want to read more about

In previous posts I’ve looked at R squared in linear regression, and argued that I think it is more appropriate to think of it is a measure of explained variation, rather than goodness of fit. Of course not all outcomes/dependent variables can be reasonably modelled using linear regression. Perhaps the second most common type of regression model is logistic regression, which is appropriate for binary outcome data. How is R squared calculated for a logistic regression model? Well it turns out that it is not entirely obvious what its definition should be. Over the years, different research

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