How to assess the fit of multilevel logit models with Stata?

Stata 14 includes the multilevel model for binary (melogit) and ordinal logits (meologit). Unfortunately, except for the global Wald test of the estimated fixed effects, both models do not provide any fit measure to assess its practical significiance. Therefore, I developed an ado-file to calculate McFadden's and McKelvey and Zavoina's pseudo-R²s. It estimates the intraclass correlation (ICC) of the dependent variable for the actual sample to assess the maximum of the contextual effect. Since the early 1990s, a lot of Monte Carlo simulation studies (Hagle and Mitchell 1992; Veall and Zimmermann 1992, 1993, 1994; Windmeijer 1995; DeMaris 2002) proved that McKelvey and Zavoina pseudo-R² is the best one to assess the fit of binary and ordinal logit models. My ado-file calculates this fit in two complementary ways: first, for the fixed effects only, and second, for the fixed and random effects together. The estimation of McFadden's pseudo-R² uses two different zero models: first, the random-intercept-only model (RIOM) knowing the contextual units, and second, the fixed-intercept-only model (FIOM) ignoring the contextual units completely. For each of them, it calculates the global likelihood-ratio-chi2 test statistic whether all fixed effects or all fixed and random effects are zero in the population. An empirical study of drug consumption in European countries demonstrates the usefulness of my fit_meologit_2lev.ado or fit_meologit_3lev.ado files for multilevel binary and ordinal logit models.