Please also provide the following information in addition to your question:
I used this autocorrelation cor_ar structure for my time series data. However, I think it is more meant for evenly spaced data, while my data, as well as many other data, are more likely to be unevenly spaced, I believe. Will brms consider implement an autocorrelation structure for unevenly spaced time series data? Although most of my models included autocorrelation (cor_ar) were better than those not included when judged by loo_compare.
Should the random effect and autocorrelation have the same structure? In a model, where I used the same structure for random effect and autocorrelation, and I used re_formula = NA in function fitted to not to include those random effects. However, my predicted lines looked awkward, they were jagged. Can someone tell me what is wrong?
I also found that lines fitted with brms are more wiggly than those fitted with gamm from package mgcv, why is that?
- Operating System: 64-bit
- brms Version:2.9.0