Lets say I have a brms model of y ~ a*b + (1|group) and I'd really like to compare it against an alternative model y ~ a + b + (1|group) (in my example a is time and b is experimental condition).
Using hypothesis I could write:
hypothesis(m1, c("a:b = 0"))
However the Evidence ratio value returned for this point null hypothesis is often weird in that it is very dependent on the priors used. When relatively uninformative priors are used then -- even when a fairly precise and >zero estimate for a:b is found -- the ER can still be > 1 or > 3. This is because the prior placed so much weight far away from zero.
I realise I could try using the bridgesampling package to calculate a Bayes Factor but have always found this quite slow and cumbersome. I wonder instead whether it would be reasonable to compare the ERs for these two hypotheses:
a:b = 0 vs a:b > 0
That is, if I calculated ER(a:b > 0)/ER(a:b = 0) then does this give the a Bayes factor in favour of a non-zero and positive effect, as compared with a zero effect?
