ABSTRACT
A key facet of data analysis entails checking the adequacy of models. It is important to learn whether one’s results are being strongly influenced by one or two cases, whether one’s results might be especially sensitive to certain modeling choices and assumptions (e.g., choice of link function, distributional assumptions regarding random effects), and whether one’s model fails to capture important features of the data (e.g., nonlinear relationships between key predictors and the outcome of interest).
