posterior_smooths as a substitute/work-around for
hypothesis() for models with splines (Using **hypothesis()** with **s()** from **brms**).
However, I can‘t get to grips with this approach.
I try to explain what I want to achieve:
seed <- 0
dat <- mgcv::gamSim(1, n = 200, scale = 2)
fit <- brm(y ~ s(x0), data = dat)
From this plot, how do I calculate the posterior probability for e.g. that y increases by more than 2 between x0=0 and x0=0.5?
I think I got it figured out now.
newdata <- data.frame(x0 = c(0, 0.5))
post <- posterior_smooths(fit, smooth = "s(x0)", newdata = newdata)
sum(post[,2] - post[,1] > 2 )/length(post[,1])
Could someone please confirm that I got it right? Thanks.
yes that does look correct to me. instead of sum() / length() you can also just use mean()