Hi everyone,
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 populationlevel 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 logodds 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:

summary
(seen here) 
as_draws_array
→posterior::summarize_draws
(seen here) 
coef
,fixef
,ranef
→inv_logit_scaled
(see here) 
posterior_predict
: includes an optiontransform
, where I can enterinv_logit_scaled
but this is deprecated? (seen here) 
posterior_epred
→str
(seen here) 
fitted
(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 brmsspecific]
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 hypothesis()
with inv_logit_scaled()
or 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 logodds space instead?