The Nagelkerke adjustment scales the Cox and Snell value to ensure a maximum of 1, making it more comparable to the traditional R-squared for communication purposes. 4 are rare in practice.
Pseudo R2 Stepwise Regression: Optimizing Model Fit with McFadden’s R2
McFadden’s R-squared is defined as 1 minus the ratio of the log-likelihood of the fitted model to the log-likelihood of the null model (a model with only the intercept). Key Formulas and Their Interpretation Several popular formulas exist for calculating pseudo R-squared, each comparing the log-likelihood of the fitted model to a different baseline.
For instance, when conducting a stepwise regression, observing the increase in McFadden’s R-squared provides a quantitative measure of how much better the model fits the data with the inclusion of a specific predictor. Formula Interpretation Upper Bound McFadden Ratio of model to null log-likelihood Less than 1 Cox & Snell Proportion of uncertainty explained Less than 1 Nagelkerke Scaled to reach 1.
Pseudo R2 Stepwise Regression: Optimizing Model Fit with McFadden’s R-squared
However, it is crucial to view this metric in conjunction with other diagnostics, such as the Hosmer-Lemeshow test and classification tables, to avoid over-reliance on a single number. Consequently, there is no total sum of squares to partition.
More About Pseudo r2
Looking at Pseudo r2 from another angle can help expand the discussion and give readers a second clear paragraph under the same section.
More perspective on Pseudo r2 can make the topic easier to follow by connecting earlier points with a few simple takeaways.
