Hello,

This is a continuation of a different question. I am trying do some simulations to study dose/response curves with variation on blocks. My model is as follows:

stan_glmer(formula(y~logDose*sampleName+(1|block)+(0+logDose|block)),

family=binomial(link=“probit”),

data=dataSet, adapt_delta=0.99)

The simulated data is a logDose/Response dataset with 5 doses and the responses are 0’s and 1’s, with two samples and 2, 4 or 16 blocks, and 20 replicates at each dose.

Sometimes I might generate situations where there is very little variation on block, and also very few blocks (two blocks total). In this situation (low variance, few blocks) it seems that the sampler is having a hard time estimating the variance parameters and converging, exactly why is this? I understand that perhaps this is due to the small number of blocks and very small variance, but wouldn’t one expect this to be reflected on wider credible intervals? What do people do in this situations? Is estimation not possible?

Thank you,

Ramiro