Heatmaps for patterns of association in log-linear models
Abstract
Log-linear models offer a detailed characterization of the association between categorical variables, but the breadth of its outputs is difficult to grasp due to the large number of parameters these models entail. Revisiting seminal findings and data from sociological work on social mobility, I illustrate the use of heatmaps as a visualization technique to convey the complex patterns of association captured by log-linear models. In particular, turning log odd-ratios derived from a model’s predicted counts into heatmaps allows to summarize large amounts of information and facilitates comparison across models’ outcomes.