# BRMS pp_check with logistic regression

Hi all,

I ran the following logistic regression in BRMS: Essentially I am measuring the impact of a behavior treatment. This is baseline data prior to the treatment so I expected that none of the parameters would share a reliable relationship with the probability of producing a correct response.

baseline <- brm(formula = Successes | trials(Trials) ~ Probe * AcquiGen +
(1+Probe+AcquiGen|SubjectCode) + (1|obs) + (1|Structure),
data = BLOnly,
family = binomial(“logit”),
cores = 4,
prior = c(prior(normal(0, 10), class = b),
prior(normal(0, 10), class = b, coef = AcquiGen1),
prior(normal(0, 10), class = b, coef = Probe),
prior(normal(0, 10), class = b, coef = Probe:AcquiGen1),
prior(cauchy(0, 3), class = sd, group = “obs”),
prior(cauchy(0, 3), class = sd, coef = Intercept, group = “obs”),
prior(cauchy(0, 3), class = sd, group = “Structure”),
prior(cauchy(0, 3), class = sd, coef = Intercept, group = “Structure”),
prior(cauchy(0, 3), class = sd, group = “SubjectCode”),
prior(cauchy(0, 3), class = sd, coef = AcquiGen1, group = “SubjectCode”),
prior(cauchy(0, 3), class = sd, coef = Intercept, group = “SubjectCode”),
prior(cauchy(0, 3), class = sd, coef = Probe, group = “SubjectCode”)),
iter = 10000,
inits = “random”,
The y-axis is the density of the distribution, which does not make much sense for a response that is actually continuous. I recomend trying out other `pp_check` types or just manually create predictions via `posterior_predict` and then summarize/visualize the results yourself.