I'm starting to prefer visualizations of my regression models as opposed to tabular output (OR's, beta-coefficients, 95%CIs). However, I struggle to find a good way to do this when I am undertaking multiple imputation by chained equations (mice). The output of mice (in R) is usually one data frame containing m complete datasets after m imputations. With this, I can run the same model on each of the m complete datasets and then pool the results according to Rubin's Rules. In R there are nice functions to do this with the
mice package. But no really good way to plot this output.
My current strategy is just to pick a random imputed dataset out of the m complete datasets and then do the visualization on that one dataset (and model).
Does anyone out there have ways they typically check these models (with plots) or visualize the data (in exploration)? I'm open to creative suggestions. They need not be in R, however, then maybe you could show me the types of plots you make?