#' Filter best ML results and compute AIC-based selection scores
#'
#' Returns a table with best maximum likelihood results for each of the 10 DD
#' models, and computes the delta AIC, AIC weights, and the latter's cumulative sum.
#'
#' @param mle_tbl a data frame containing maximum likelihood estimates for each of
#' the DD models (at least one row for each model), with at least cols `dd_model`,
#' and `aic`. E.g. the output of [read_mle_tbl].
#'
#' @export
#'
filter_aic_best <- function(mle_tbl) {
aic_tbl <- mle_tbl %>%
dplyr::group_by(dd_model) %>%
dplyr::slice_min(aic, with_ties = FALSE) %>%
dplyr::ungroup() %>%
dplyr::arrange(aic) %>%
dplyr::mutate(
"delta_aic" = aic - min(aic),
"aicw_num" = exp(-delta_aic / 2),
"aicw_denom" = sum(aicw_num),
"aicw" = round(aicw_num / aicw_denom, 5),
"cumsum_aicw" = cumsum(aicw)
)
return(aic_tbl)
}
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