Please also provide the following information in addition to your question:

- Operating System: Win10
- brms Version: 2.8.0

Hello! I have built up a three-level model binary logistic model where the small group is nested within a big group. I have five population-level variables (a1-a5 are category ones and a6 is numerical) and one group-level variable (b1 as a variable at the big group level). For quick coverage, I adopt the prior for “sd” as Cauchy (0, 2.5). However, I met a problem with my model results. As there are very few effective samples for the estimates of “sd” at both group levels, and few effective samples for the estimates of “Intercept” and the group-level variable “b1”. Anyone could help detect the problem with my model? Is there any suggestions to deal with the problem? Many thanks. Diva

The output shows as below:

Family: bernoulli

Links: mu = logit

Formula: Rf ~ a1 + a2 + a3 + a4 + a5 + a6 + b1 + (1 | BigGroup/SmallGroup)

Data: orgin (Number of observations: 5314)

Samples: 2 chains, each with iter = 4000; warmup = 2000; thin = 1;

total post-warmup samples = 4000

Group-Level Effects:

~BigGroup(Number of levels: 14)

Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat

sd(Intercept) 1.00 0.34 0.54 1.79 578 1.00

~BigGroup:SmallGroup (Number of levels: 115)

Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat

sd(Intercept) 1.24 0.11 1.03 1.47 859 1.00

Population-Level Effects:

Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat

Intercept -0.91 0.33 -1.57 -0.27 1272 1.00

a1F -0.09 0.07 -0.23 0.06 3535 1.00

a2W 0.31 0.11 0.10 0.52 2588 1.00

a2G 0.78 0.17 0.46 1.11 2646 1.00

a2S 0.45 0.19 0.09 0.80 2701 1.00

a3dd -0.16 0.10 -0.35 0.04 3081 1.00

a3nn -0.06 0.13 -0.31 0.18 2769 1.00

a3tm -0.13 0.11 -0.36 0.10 2998 1.00

a3ot -0.29 0.16 -0.61 0.01 3326 1.00

a4low -0.04 0.10 -0.23 0.15 3390 1.00

a4mid 0.05 0.12 -0.18 0.29 3150 1.00

a5HH 0.49 0.09 0.32 0.66 3228 1.00

a6 -0.06 0.07 -0.20 0.08 2324 1.00

b1 -0.28 0.34 -0.84 0.41 784 1.00