Signal strength, Error assumptions, and MCMC convergence

Hi,

I'm using State-Space Bayesian Partial Pooling approach for my model and I have a few questions:

1. Does the model provide information of coefficient of determination, R²_det? If it does, how can I access it?

2. When dealing with data with low metabolism signal (low diel change in DO), I found that the process error terms made the DO fluctuation even less. I have tried completely disregarding process error (setting "err_proc_iid = FALSE" in mm_name) or modified "err_proc_iid_sigma_scale" in specs, but it turned out that process errors were either 0 or still large. I'm wondering if there's any way that I can modify the standard deviation of process error (err_proc_iid_sigma?) in my partial pooling model.

3. To see if the chains in MCMC converged, I was looking for Rhat of standard deviation of K600 in the model outputs but failed to find it in get_fit (I did see Rhat for K600 mean and K600 predlog) or other functions. Does the model provide such information?

Thank you so much,
Tzu-Yao

Start by reviewing the partial-pooling model configuration around mm_name and specs, including err_proc_iid, err_proc_iid_sigma_scale, and err_proc_iid_sigma. Then inspect get_fit for the reported K600 outputs and determine whether R2det, process-error controls, and K600 standard-deviation Rhat are available; document the access paths or record the missing capabilities.

Written by the indexing model from the issue text.