Post #2154511

2026-04-02 20:37 UTC

@emjonaitis how many/what types of predictors do you have? If you really want to zoib that's just a really intense inference procedure. Options: do "standard" beta regression which is maybe a little more efficiently implemented with ML (rather than Bayesian) estimation? It commonly shrinks 0 and 1 a little bit towards 0.5 as a preprocessing step so it'll be a bit less exact but it may not matter at all with this much data Another option:

Replies (2)

@erikjan@fosstodon.org 2026-04-02 20:41

@emjonaitis procedures like the dbreg package (https://grantmcdermott.com/dbreg/) basically do the following: summarize the rows into buckets of profiles, and then perform weighted regression (weighted by how many people fall in each profile) This might be possible for you? Even though there are many rows there might be an order of magnitude fewer unique rows. You would need to bin your continuous predictors, so this is also approximate And not sure if this will even work with the nested structure

Open ##2154512
@emjonaitis@mathstodon.xyz 2026-04-02 20:42

@erikjan I gravitated toward zoib because of its ability to handle clustered data. I don't believe that betareg has that capability. The fixed portion of the model is simple (the effects of interest are basically a group effect and a group x time interaction) but I don't want to disregard the longitudinal aspect of the data by e.g. selecting one random obs per cluster, because the trajectories are of interest to the investigators.

Open ##2154513