Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 98.66%. Comparing base (d831b30) to head (16f1338).

@@            Coverage Diff             @@
##           master     #419      +/-   ##
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+ Coverage   98.62%   98.66%   +0.04%     
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  Files          35       35              
  Lines        5798     5830      +32     
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+ Hits         5718     5752      +34     
+ Misses         80       78       -2     

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Fantastic, thank you! I will doubtlessly have a use case for this in the next two weeks, and I will report if there's any issues.

@behramulukir, sorry for the basic question, but how do I get to the function? I don't see it when I install the latest development version of bayesplot.

Did you install the correct branch @mhollanders? As the changes are not merged to the master branch yet, you should install the branch I am working on. You can do it with devtools::install_github("stan-dev/bayesplot", ref = "ppc-rootogram-grouped")

Thank you, and pardon for my ignorance. It works great!

No worries! I'm glad that you liked it

Thanks @behramulukir and thanks @mhollanders for testing it out!

Something to note here is that in ppc_bars_grouped, force_axes_in_facets function is used to make sure all facets have axes. This was resulting in errors when used with ppc_rootogram_grouped(style="discrete") since -Inf results in NaN when used with scale_y_sqrt(). It was still used in other rootogram styles, but so far, I hadn't managed to get it working for discrete style. I'll mark this PR as ready once I find a solution to that.

Does 0 work instead of -Inf? If 0 works here we could add an argument to force_axes_in_facets() to change between -Inf and 0 (leaving the default as -Inf so it stays the same for the other plots).

It's tangentially related but I might as well piggy-back on to this post: are there any PPC tips for zero-inflated count data? This output is for occupancy models, where zero-inflation is modeled at the level of the site and species. So rare species will have lots of 0s. I'm attaching two plots, one for the aggregated (predicted) counts per species and site (Q), and one for the predicted counts per species, site, and replicate survey. (y) The excess zeros make them hard to interpret!