Adjusted McFadden Pseudo R-Squared
Like its ordinary least squares analog, the adjusted McFadden Pseudo \(R^2\) penalizes the McFadden pseudo \(R^2\) as more terms are added to the model,
\[R_{Adj}^2 = 1.0 - \frac{\ln(L)-K}{\ln(L_0)},\]
where \(K\) is the number of estimated parameters in the model. This count includes the intercept and other estimated degrees of freedom in the model.
References