bayesianOU (version 0.1.3)

extract_posterior_summary: Extract posterior summary from fitted model

Description

Extracts median point estimates and credible intervals for all model parameters from a fitted Stan model.

Usage

extract_posterior_summary(fit)

Value

List with components:

beta1

Median of global TMG effect

beta0_s

Vector of sector-specific intercepts

kappa_s

Vector of mean reversion speeds

a3_s

Vector of cubic drift coefficients

theta_s

Vector of equilibrium levels

rho_s

Vector of SV persistence parameters

alpha_s

Vector of SV level parameters

sigma_eta_s

Vector of SV volatility parameters

nu

Degrees of freedom for Student-t

gamma

COM effect in mean

rhat

R-hat convergence diagnostics

ess

Effective sample sizes

Arguments

fit

Fitted Stan model object (CmdStanMCMC or stanfit)

Examples

# \donttest{
# 1. Create a mock CmdStanMCMC object
# We simulate a posterior distribution for 2 sectors
S <- 2
n_draws <- 100

# Helper to generate random draws
mock_draws <- function(name, n_cols=1) {
  m <- matrix(rnorm(n_draws * n_cols), nrow = n_draws, ncol = n_cols)
  if (n_cols > 1) {
    colnames(m) <- paste0(name, "[", 1:n_cols, "]")
  } else {
    colnames(m) <- name
  }
  as.data.frame(m)
}

# Combine draws into one data frame
df_draws <- cbind(
  mock_draws("beta1", 1),
  mock_draws("beta0_s", S),
  mock_draws("kappa_tilde", S), # Note: function expects log-scale kappa
  mock_draws("a3_tilde", S),    # Note: function expects log-scale a3
  mock_draws("theta_s", S),
  mock_draws("rho_s", S),
  mock_draws("alpha_s", S),
  mock_draws("sigma_eta_s", S),
  mock_draws("nu_tilde", 1),
  mock_draws("gamma", 1)
)

# Mock fit object
mock_fit <- structure(list(
  draws = function(vars, format="df") {
     # Simple regex matching for the mock
     if (length(vars) == 1) {
       # Check if it's a scalar or vector parameter request
       if (vars %in% names(df_draws)) return(df_draws[vars])
       # Pattern match for vectors like "beta0_s" -> "beta0_s[1]", "beta0_s[2]"
       cols <- grep(paste0("^", vars, "\\["), names(df_draws), value = TRUE)
       if (length(cols) > 0) return(df_draws[cols])
     }
     return(df_draws) 
  },
  summary = function() {
    data.frame(
      variable = names(df_draws),
      rhat = rep(1.0, ncol(df_draws)),
      ess_bulk = rep(400, ncol(df_draws))
    )
  }
), class = "CmdStanMCMC")

# 2. Run extraction
summ <- extract_posterior_summary(mock_fit)
print(summ$kappa_s)
# }