I use Windows 7, RStudio, R version 3.5.1, ‘brms’ package (version 2.3.4) from github.

This tutorial gave me working template (fixing labels after hypothesis() is a minor issue).

```
require(tidyverse)
require(brms)
d <- data.frame(s = 9, k = 10)
mOK <- brm(s | trials(k) ~ 0 + intercept, data=d,
prior = set_prior("beta(1, 1)", class = "b", lb = 0, ub = 1),
family = binomial(link="identity"), sample_prior = TRUE, cores = 3,
seed = 20180709 )
(hOK <- hypothesis(mOK, "intercept = 0.5"))
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Star
## 1 (intercept)-(0.5) = 0 0.33 0.11 0.09 0.48 0.14 *
plot(hOK, plot = F, theme = theme_get())[[1]] +
scale_x_continuous(breaks = seq(-.5, .5, by = .25),
labels = seq(-.5, .5, by = .25)+.5)
```

Now I tried to use default prior and got two problems:

```
mNoBF <- brm(s | trials(k) ~ 0 + intercept, data=d,
family = binomial(link="identity"), sample_prior = TRUE, iter = 4e4, cores = 3,
seed = 20180709, control=list(max_treedepth=10, adapt_delta=0.999))
```

It leads to “Rejecting initial value” warning (hidden in RStudio’s Viewer), but at least we get a warning about “2988 divergent transitions after warmup” to alert us. Increasing adapt_delta does not help even for this **embarrassingly simple** model. My real binomial (2-way mixed) model barely gives me ESS=918 with 4e5 iterations over 4 cores and tons of “divergent transitions” using default prior.

**Question #1**: Could the default binomial prior be chosen better?

```
(hNoBF <- hypothesis(mNoBF, "intercept = 0.5"))
# Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Star
# 1 (intercept)-(0.5) = 0 0.33 0.1 0.09 0.48 NA *
plot(hNoBF)
```

**Question #2**: Was “sample_prior = TRUE” simply ignored in mNoBF <- brm() without warning? Why not sample default prior, just like it is done for user-given prior, especially if hypothesis() needs it to compute Evid.Ratio?

hNoBF gives the same results as hOK, so I guess Stan (as usual) is too “trigger happy” to fire these divergent transitions warnings on us…