- Operating System: Linux (Debian testing)
- brms Version: brms_2.9.3 (from Github)
I have trouble fitting a (relatively simple) model using
foo.R (5.3 KB)
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
brmsparameters, either in a gaussian model or a Poisson model (which would be reasonable,
Depbeing indeed a count), in the latter case, forcing
adapt_delta=0.95(IIRC) avoids divergent transitions.
Introducing N4 requires to raise
adapt_deltato 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 + (1|F4),
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.
- 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.
glmerreport problems about the corresponding frequentist models.