Interactions are often included in generalized linear models (GLM). Interpreting these interactions in the transformed scale of the linear equation is like interpreting an interaction in an OLS regression. However, this is rarely the scale in which results are discussed, and interpreting interactions in the non-transformed scale is not straightforward. Focusing on a continuous by categorical interaction in a logistic regression, we present code for visualizing a marginal effect in the probability scale. This visualization is a useful tool for understanding a model effect that is difficult to intuit from the standard model output. By presenting the results in the researcher’s scale of interest, it allows the researcher to better communicate the model results
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