Currently, the sections on other distribution and mixed effects rely heavily on itsadug::plot_smooth(), with the idea to plot smooth terms at the response scale.
I understand that this intends to make life easier for the participants of the workshop, but it actually has several problematic aspects:
In the common case that other continuous covariates are present, their value has to be fixed, and this is not discussed in the presentation.
Whoever works with generalized models and / or mixed models should be comfortable interpreting the effects on the link rather than the response scale. I think we should help participants work on that skill instead of avoiding it.
If plots at the response scale are needed, these can be easily produced with the combination of predict() and plot(), which appears to be the standard workflow in these cases. This also works for random effects, as these can be excluded from predictions.
In short, I think the current implementation may cause more problems (for the participants) down the line than it solves.
Currently, the sections on other distribution and mixed effects rely heavily on
itsadug::plot_smooth(), with the idea to plot smooth terms at the response scale.I understand that this intends to make life easier for the participants of the workshop, but it actually has several problematic aspects:
predict()andplot(), which appears to be the standard workflow in these cases. This also works for random effects, as these can be excluded from predictions.In short, I think the current implementation may cause more problems (for the participants) down the line than it solves.
This could be addressed when working on #14 .