Pseudo R Squared. This website contains more information on these and other pseudo R squared values while this paper provides a good assessment of these and other goodness of fit metrics. R 2 adj 1 ln LLMˆ full-Kln LLMˆ intercept.
Usage PseudoR2x which NULL Arguments. Although theres no commonly accepted agreement on how to assess the fit of a logistic regression there are some approaches. Numerous pseudo r-squared measures have been proposed for generalized linear models involving a comparison of the log-likelihood for the fitted model against the log-likelihood of a nullrestricted model with no predictors normalized to run from zero to one as the fitted model provides a better fit to the data providing a rough analogue to the computation of r-squared in a linear regression.
Ryan on page 266.
A correction known as the Nagelkerke pseudo-R 2 Nagelkerke 1991 adjusts it to range between zero and one by the simple expedient of dividing it by its maximum possible value. My function nagelkerke will calculate the McFadden Cox and Snell and Nagelkereke pseudo-R-squared for glm and other model fits. Usage PseudoR2x which NULL Arguments. This approach is similar to above but the model is penalized penalizing a model for including too many predictors where K is the number of regressors in the model.
