I’m performing a simple meta-analysis with `brms`

following some suggestions (here and here ). This is my dataset:

```
> meta_data %>%
+ select(study, eff_size, eff_size_variance_pool)
# A tibble: 16 x 3
study eff_size eff_size_variance_pool
<dbl> <dbl> <dbl>
1 1 1.52 0.307
2 1 1.23 0.189
3 1 1.05 0.131
4 1 0.99 0.166
5 2 0.543 0.0478
6 2 0.714 0.0589
7 3 1.74 0.0495
8 4 1.15 0.0737
9 4 1.7 0.129
10 5 0.997 0.0488
11 5 1.10 0.0408
12 6 0.568 0.0774
13 6 0.786 0.0818
14 7 0.369 0.0325
15 7 0.369 0.0325
16 9 0.947 0.0154
```

This is my model with `brms`

:

```
brm_fit <- brm(
eff_size | se(eff_size_variance_pool) ~ 1 + (1 | study),
data = meta_data,
)
```

The model works but diagnostic is bad and strange. In particular Rhat and ESS seems good but the warnings highlight a too low EES and NA Rhat.

```
Warning messages:
1: There were 47 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
2: Examine the pairs() plot to diagnose sampling problems
3: The largest R-hat is NA, indicating chains have not mixed.
Running the chains for more iterations may help. See
http://mc-stan.org/misc/warnings.html#r-hat
4: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
http://mc-stan.org/misc/warnings.html#bulk-ess
5: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
http://mc-stan.org/misc/warnings.html#tail-ess
```

This is the model summary:

```
> summary(brm_fit)
Family: gaussian
Links: mu = identity; sigma = identity
Formula: eff_size | se(eff_size_variance_pool) ~ 1 + (1 | study)
Data: meta_data (Number of observations: 16)
Samples: 4 chains, each with iter = 10000; warmup = 5000; thin = 1;
total post-warmup samples = 20000
Group-Level Effects:
~study (Number of levels: 8)
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept) 0.53 0.18 0.29 1.02 1.00 2319 1502
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 0.96 0.20 0.56 1.34 1.00 1813 1299
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 0.00 0.00 0.00 0.00 1.00 20000 20000
Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
Warning message:
There were 47 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
```

I’ve noticed that the model is trying to fit a `family-specific parameter`

that is absent in linked examples. Maybe the fitting problems are related to this parameter? Does the model is correctly specified?