Hello everyone,

I’m working on a model to calculate the fertility rate of a population of orcas, which is composed of 3 different pods. The goal is to understand whether there’s a relationship between salmon abundances (and their yearly variation) and the population and at pod-level fertility rates.

I’m using `brms`

to fit the model, with z-scores (denoted with the `.z`

suffix) representing the abundances of various salmon stocks. Here’s the model that runs smoothly:

```
model <- brm(
Birth ~ (1|Pod) + s(Age) + (1|Animal) + Cor.z:Pod + Lcol.z:Pod + Mcol.z:Pod + Nor.z:Pod + Puso.N.z:Pod +
Puso.S.z:Pod + Sfb.z:Pod + Wac.z:Pod,# The next stocks break the model: + Sgeo.S.z, #+ Urb.z:Pod + + Snak.z:Pod + Swvi.z:Pod,
data=data,family=bernoulli(),
iter = 8000,
warmup = 4000,
chains = 3,
cores = 3,control = list(adapt_delta = 0.99)
)
```

This runs quickly, converges well, and all Rhats are close to 1.

However, when I include additional z-scores for the following salmon stocks:

Sgeo.S.z + Urb.z:Pod + Snak.z:Pod + Swvi.z:Pod

The model runs extremely slowly and does not converge, with all Rhats exceeding 1 (as high as 3.62). I tested these additional stocks one by one and in combinations, which is how I confirmed that they are causing the slowdown and convergence issues. Also, I haven’t yet included any specific priors for this model.

The following warnings are generated when I include these additional stocks:

```
Warning messages:
1: There were 12000 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
2: Examine the pairs() plot to diagnose sampling problems
3: The largest R-hat is 3.62, indicating chains have not mixed.
Running the chains for more iterations may help. See
https://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
https://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
https://mc-stan.org/misc/warnings.html#tail-ess
```

I’m attaching the full R code and dataset used in case that helps in troubleshooting. Has anyone encountered similar issues when adding more group-specific slopes for covariates? Any suggestions on how to handle the treedepth or improve convergence would be greatly appreciated. Would including priors help, or should I be looking into adjusting other aspects of the model?

Thanks in advance!

data_brms.csv (308.0 KB)

model_for_Stan_forums.R (534 Bytes)