I am using brms (2.1.2, windows, rstudio) and am running a prior predictive check for data from an experiment, and was playing around with the parameters, specifically the slope and don’t understand the behaviour:
modelTEST1<- brm(outcome ~ 1 + predictor , data = dat, warmup = 1000, prior = c( prior(normal(0,1000), class="Intercept"), prior(cauchy(0,100), class = "sigma"), prior(normal(0,10000000), class = "b") ), iter = 3000, chains = 2, sample_prior = "only", seed = 221, control = list(adapt_delta = 0.97), cores = 2)
plot(conditional_effects(modelTEST1)). This yields a graph (see attached)
What I don’t understand is why there is this asymmetry in the graph? Why is there more uncertainty of the estimate of predictor=0 than predictor =1? It appears that the larger the slope, the larger the asymmetry - but why?
Sorry if I am missing something really basic, but am totally stumped!