In the R world, lme4 is a great package for mixed model estimation,
and the most widely used for such models. For standard settings, few
tools will do the trick as easily or as quickly, and because of that,
its approach has been emulated in other packages and statistical
programs. However, that ease and efficiency comes at a price of being
able to do more complex models, so at some point you may need to switch
gears. This workshop will demonstrate other ways to potentially get what
you need.
Demonstration will (potentially) include the following, along with some discussion of strengths and/or drawbacks to use.
glmmTMB: heterogeneous variances, auto-correlated residuals, zero-inflated modelsrstanarm,brms: Bayesian approachesmgcv: additive effects, robust models, big datastatsmodels: in case you’re in the Python world.
This workshop assumes basic familiarity with mixed models, and
familiarity with R, especially the lme4 package. However, this is a
brief demonstration with a simple goal of bringing about awareness of
other useful tools, so one can simply attend to hear about them.
Click to download this
repo.
For the workshop, you can run the code in mmm_notebook.Rmd as we go
along. If you just want to follow along/see the content, open mmm.html
in your browser after downloading the repo.