This workshop will cover how to use the marginaleffects and emmeans packages in R to explore the results of linear and generalized linear models. When models include many categorical predictors or interaction terms, the reported estimates of the model coefficients can be difficult to interpret, but these tools can provide insights for interpreting the model.
Estimated marginal means are model predictions based on a set of combinations of predictor variables. This workshop will cover how to use these estimates to interpret model output in tabular and graphical form. We will also cover post-hoc comparisons such and the Tukey Test for all pairwise comparisons, as well as custom contrasts and difference-of-differences. These packages also allow for testing and comparison of slopes by group (such as in an ancova model), and aids in interpretation of output when the response has been transformed, or for generalized linear models (such as logistic or Posison regression).
This workshop assumes familiarity with linear models (regression and anova) as well as logistic regression.
Methods will be demonstrated in a hands-on fashion using R, with the help of the marginaleffects, emmeans, and ggplot2 packages.
For information on how to install R and R Studio, please visit: https://cscu.cornell.edu/installr.