Model evaluation methods based on the analogue of squared residuals do not work well when the outcome variable is discrete and ordered. A popular pseudo-R^2 measure due to McFadden (1973) is given by:
$$R^2=1-\log{L_{fit}}/\log{L_0}$$
where \(\log{L_{fit}}\) is the log-likelihood for the fitted model and \(\log{L_0}\) is the log-likelihood from an intercept only model that estimates the probability of each alternative to be the sample average. This function calculates this term for objects of class oglmx.
McFaddensR2.oglmx(object)object of type oglmx
numeric value between 0 and a theoretical maximum of 1.