Sorry if this is a beginner question: I am at the point of creating some output tables to report the results of my experiment; specifically the population-level fixed effects. It is a logistic (bernoulli, link = logit) model in brms, and I would like to report it in the most intuitive way possible (i.e. in probability space instead of log-odds space).
I am unsure of the best way to go about this, as I don’t feel I’ve seen one consistent method in my searches. There are the following functions in the brms package that I think could be useful to me, and I’d like to understand the differences between them, and which ones incorporate a transformation into probability space:
posterior_predict: includes an option
transform, where I can enter
inv_logit_scaledbut this is deprecated? (seen here)
posterior_table, with input from one of the above functions (see here)
For reasons specified here, I cannot plot conditional effects due to a bug in the current version of brms. [Edit: this is a bug with scale() and poly() in general, not brms-specific]
Also note that I have a fairly complex model, and to my understanding I would need to include all the variables and interactions each time I want to use
exp(). But perhaps there is something I am not understanding here.
Maybe it is not possible, or not meaningful, to have such a table? Should I report my results in log-odds space instead?