ggeffects: Tidy Data Frames of Marginal Effects from Regression Models

Results of regression models, like estimates, are typically presented as tables that are easy to understand. Sometimes pure estimates are not helpful and difficult to interpret. This is especially true for interaction terms in logistic regression or even more complex models, or transformed terms (quadratic or cubic terms, polynomials, splines), where the estimates are no longer interpretable in a direct way. In such cases, marginal effects are far easier to understand. In particular, the visualization of marginal effects makes it possible to intuitively get the idea of how predictors and outcome are associated, even for complex models.