An R package for plotting partially pooled estimates for mixed-effects models
Jul 21, 2017
2 minute read

I came across this excellent post from Tristan Mahr on plotting partially pooled estimates for mixed-effects models and was inspired to create an R package for it based on the code in the post. I found mixed models made more sense to me when I thought of them in terms of partial pooling, and I had been looking for ways to plot partially pooled estimates both as a more accessible way into telling others about mixed effects models and as a way to assess the fit of and better understand mixed effects models when I came across Mahr’s post.


As a bit of background (this is from the Description for the package):

Scholars have defined mixed effects models as a compromise between complete pooling estimates (i.e., those from a linear model ignoring a grouping factor), no pooling estimates (i.e., a linear model for which each level of the grouping factor is dummy-coded), and partially pooled estimates (i.e., those from mixed effects models, for which group-specific estimates are shrunken to (or gather strength from) the overall estimates (Gelman & Hill, 2007).


The package, tentatively and not very interestingly named lme4plotpartial, is on GitHub here. It loads a function, plot_partial_pooling(), that plots the complete pooling, no pooling, and partially pooled estimates for a mixed effects model.


For now, an example, from Mahr’s post (here, dplyr and lme4 are loaded for data procesing and a data file, respectively):

library(dplyr, warn.conflicts = FALSE)

sleepstudy <- sleepstudy %>% 
  as_tibble() %>% 
  mutate(Subject = as.character(Subject))

plot_partial_pooling(sleepstudy, y_var = Reaction, x_var = Days, group = Subject)

Future improvements

In terms of potential improvements, an idea that would vastly expand functionality is to parse a lme4() formula to specify the variables.

As a bit of reflection: The best part about creating this package, to me, was that after making I messaged Tristan, who then took time to (immensely) improve on it (in changes that are now reflected in the version of it available on GitHub). It was a cool example of the benefits of sharing (in my case, even in-progress work), because others can then use it.

Check it out and let us know what you think.