Seemingly Unrelated Estimation for Model Comparisons
The suest package lets you compare predictions and marginal effects across regression models. It combines two or more separately fitted models into one object with a joint robust covariance matrix. The combined object works with the marginaleffects package to compare predictions, slopes, and average comparisons across models.
Supported models
Linear, binary, ordinal, multinomial, count, censored, survival, and instrumental-variable models, as well as panel, multilevel, and survey models. See suest() for the full list.
Probability weights (weight_type = "pweight") are supported for linear, binary logit and probit, Poisson, negative-binomial, ordered logit and probit, and multinomial logit models. Joint cluster-robust covariance is available through the cluster argument to suest(). Systems fitted in multiply imputed datasets can be pooled with suest_mi() using Rubin’s rules.
Reference
Mize, Trenton D., Long Doan, and J. Scott Long. 2019. “A General Framework for Comparing Predictions and Marginal Effects Across Models.” Sociological Methodology 49(1):152–189. doi:10.1177/0081175019852763
See also
Useful links: