 Operating System: Linux (Debian testing)
 brms Version: brms_2.9.3 (from Github)
I have trouble fitting a (relatively simple) model using brms
.
foo.R (5.3 KB)
Data=dataframe foo
in the uploaded file (the data not being mine, I have obfuscated the variable names and factor levels…).
My problem is to fit the dependent variable Dep
on a linear model depending on
 the factors F2, F3 and F4 (the latter being a random effect) ;
 the boolean variables B1, B2 ;
 the numeric variables N2, N3, N4, N5.
With the following troubling results :

“Small” models containing F2, N2, B1, F3, B2 and N1 fit without difficulty with default
brms
parameters, either in a gaussian model or a Poisson model (which would be reasonable,Dep
being indeed a count), in the latter case, forcingadapt_delta=0.95
(IIRC) avoids divergent transitions. 
Introducing N4 requires to raise
adapt_delta
to 0.99 (resp 0.999) to avoid divergent transitions. 
I managed to fit the “full” gaussian model with the ridiculous and questionable code below (which takes ages to finish) :
system.time(bar < brm(Dep ~ F2 + N2 + B1 + F3 + B2 + N3 + N4 + N5 + (1F4),
family=gaussian, data=foo,
prior=prior(student_t(3,0,10), class=“b”),
sample_prior=“yes”,
save_all_pars=TRUE, seed=1723,
control=list(adapt_delta=11e8)))
I haven’t be able to fit the “full” poisson model : with the seed=1723
value, I get one chain sampling in about 10 seconds, anothe one in about 1 miute, the third one needing about 5 minutes and the last stuck in the “sampling” state at about 1200 iterations for more than 10 minutes.
I suspect that the problem is with my data : I may hit a colinearity, but I have been unable to detect it.
Notes:
 I do not expect to see any credible interval not straddling 0 (except for intercept, of course…). The factor of interest is F3, and establishing that its two contasts are centered around 0 with a small range would be of interest.
 I have but 4 levels for F4, because sampling from it is “expensive”, but it is fundamentally a randiom effect, and my conclusions should revolve around its variance.
 Neither
lmer
norglmer
report problems about the corresponding frequentist models.
Ideas ?