Use of `sample_prior='only'` in brms

Interfaces brms
8

Sorry, I should have put that into more context. The scales of the predictors are so large that the chosen priors lead to very a large prior on the intercept, which is parameterised in a special way. See ?set_prior for details. I see the following output:

Population-Level Effects: 
          Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
Intercept    -0.13     18.23   -36.15    35.96       4000 1.00
technique    -0.02      1.02    -1.99     2.01       4000 1.00
category     -0.01      1.00    -1.96     1.95       4000 1.00
subject       0.01      1.00    -2.01     1.98       4000 1.00

Thus, the intercept is the problem. You may put a prior on the intercept directly via

fit2 <- brm(formula = tp ~ 0 + intercept + technique + category + subject,
           data = dat,
           prior = set_prior("normal(0,1)", class="b"),
           family = poisson(link=log),
           sample_prior = "only" #To ignore the likelihood and check if the model is sane
)

In this case, the normal(0, 1) prior really affects the intercept instead of the transformed intercept.

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