R/rstanMethods.R

In bayesGAM: Fit Multivariate Response Generalized Additive Models using Hamiltonian Monte Carlo

# from rstan
as.matrix.bayesGAMfit <- function(x, ...) {
  x <- as(x, "stanfit")
  if (x@mode != 0) return(numeric(0)) 
  e <- rstan::extract(x, permuted = FALSE, inc_warmup = FALSE, ...) 
  if (is.null(e)) return(e)
  enames <- dimnames(e)
  edim <- dim(e)
  dim(e) <- c(edim[1] * edim[2], edim[3])
  dimnames(e) <- enames[-2]
  e
}

# from rstan
as.array.bayesGAMfit <- function(x, ...) {
  x <- as(x, "stanfit")
  if (x@mode != 0) return(numeric(0)) 
  out <- rstan::extract(x, permuted = FALSE, inc_warmup = FALSE, ...)
  return(out)
}

# from rstan
as.data.frame.bayesGAMfit <- function(x, ...) {
  x <- as(x, "stanfit")
  as.data.frame(as.matrix(x, ...))
}

# from rstan
printbg <- function(x, pars = x@sim$pars_oi, 
                              probs = c(0.025, 0.25, 0.5, 0.75, 0.975), 
                              digits_summary = 2, include = TRUE, ...) { 
  x <- as(x, "stanfit")
  
  if (x@mode == 1L) { 
    cat("Stan model '", x@model_name, "' is of mode 'test_grad';\n",
        "sampling is not conducted.\n", sep = '')
    return(invisible(NULL)) 
  } else if (x@mode == 2L) {
    cat("Stan model '", x@model_name, "' does not contain samples.\n", sep = '') 
    return(invisible(NULL)) 
  } 
  
  if(!include) pars <- setdiff(x@sim$pars_oi, pars)
  s <- rstan::summary(x, pars, probs, ...)
  if (is.null(s)) return(invisible(NULL))
  n_kept <- x@sim$n_save - x@sim$warmup2
  # cat("Inference for Stan model: ", x@model_name, '.\n', sep = '')
  cat(x@sim$chains, " chains, each with iter=", x@sim$iter, 
      "; warmup=", x@sim$warmup, "; thin=", x@sim$thin, "; \n", 
      "post-warmup draws per chain=", n_kept[1], ", ", 
      "total post-warmup draws=", sum(n_kept), ".\n\n", sep = '')
  
  # round n_eff to integers
  s$summary[, 'n_eff'] <- round(s$summary[, 'n_eff'], 0)
  
  print(round(s$summary, digits_summary), ...) 
  
}