Run Hierarchical Bayesian Model with rstan — run

Loads, filters, and formats data, then compiles and runs a hierarchical Bayesian binomial model using Stan. The model estimates species-level proportions within species complexes, accounting for hierarchical structure and covariates such as region and year.

Usage

run_stan(data = data, iter = 3000, chains = 4, thinning = 1)

Arguments

data: clean generated by creation_df()
iter: Integer. Total number of MCMC iterations per chain (including warmup). Default is 2000.
chains: Integer. Number of MCMC chains. Default is 4.
thinning: Integer. Thinning interval for posterior samples. Default is 1.

Value

A named list containing:

`fit`: An `rstan` `stanfit` object with the fitted model and posterior samples.
`species_complex`: Data frame or vector linking species to complexes used in the model.
`nice_varname`: User-friendly variable name for labeling and plotting.
`varname`: The original variable name input.
`iter`: Number of iterations used in sampling.
`warmup`: Number of warmup (burn-in) iterations.
`thinning`: Thinning interval applied during sampling.

Details

The function calls `creation_df()` to filter and preprocess the data, then prepares Stan-compatible data using `prepare_stan_data()`. The Stan model file `"Hierarchical_Model.stan"` is loaded from the package directory, compiled, and sampled with specified MCMC parameters. Recommended control parameters `adapt_delta = 0.99` and `max_treedepth = 15` improve convergence. Warmup is set to 20