R/loo.R

In bayesdfa: Bayesian Dynamic Factor Analysis (DFA) with 'Stan'

#' LOO information criteria
#'
#' Extract the LOOIC (leave-one-out information criterion) using
#' [loo::loo()]. Note that we've implemented slightly different variants
#' of loo, based on whether the DFA observation model includes correlation
#' between time series or not (default is no correlation). Importantly,
#' these different versions are not directly comparable to evaluate data support
#' for including correlation or not in a DFA. If time series are not correlated,
#' the point-wise log-likelihood for each observation is calculated and used
#' in the loo calculations. However if time series are correlated, then each
#' time slice is assumed to be a joint observation of
#' all variables, and the point-wise log-likelihood is calculated as the
#' joint likelihood of all variables under the multivariate normal distribution.
#'
#' @param x Output from [fit_dfa()].
#' @param ... Arguments for [loo::relative_eff()] and [loo::loo.array()].
#'
#' @export
#' @examples
#' \donttest{
#' set.seed(1)
#' s <- sim_dfa(num_trends = 1, num_years = 20, num_ts = 3)
#' m <- fit_dfa(y = s$y_sim, iter = 50, chains = 1, num_trends = 1)
#' loo(m)
#' }
#' @rdname loo
loo.bayesdfa <- function(x, ...) {
  log_lik <- loo::extract_log_lik(x$model, merge_chains = FALSE)
  rel_eff <- loo::relative_eff(exp(log_lik), ...)
  loo::loo.array(log_lik,
    r_eff = rel_eff,
    save_psis = FALSE,
    ...
  )
}

#' @name loo
#' @rdname loo
#' @export
#' @importFrom loo loo
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