#lme4 — Public Fediverse posts

What model #statistics should one report after using multiple #imputation and #multilevel regressions, and how are they obtained? I'm using the #mice package in #rstats, and #lme4 on each imputed dataset. When pooling results, summary() yields what I need for each model term, but nothing for the whole model. If I didn't impute but deleted listwise, I would normally report AIC, BIC, Loglik. These are all in the mipo object, for each result for each imputed dataset, but they're not pooled. I'm sure I'm missing something here. Does anyone know an example article where such results are presented neatly?

I have written a book draft of an introduction to #multilevel modeling, entitled #Multilevel Thinking: https://agrogan1.github.io/multilevel-thinking/. Comments, questions and corrections are appreciated, as are suggestions for a possible publisher.

While applicable to many different software programs, the book is currently centered around the use of #Stata, but I hope to extend it to use of #rstats (#lme4) and #julialang

Our research group is doing a seminar the coming week, and I've been asked to hold a one-hour intro to quantitative diary studies and #multilevel analyses for new #phd students unfamiliar with this way of collecting data. I'd like to highlight benefits, when it's appropriate, show an example using #rstats and #lme4, and start a discussion around how they can do this themselves in their projects.

Does anyone have suggestions for stuff to include, or clever ways to convey it? Resources?