Score reserving predictions — Score

Compare predictions with realized claim counts in the lower triangle.

Usage

Score_Reserving(
  models,
  newdata,
  scoring_metrics = c("EI", "R-tot", "R-cell-wise", "R-cal-wise"),
  granularity = c("output", "input"),
  chain_ladder = TRUE,
  clmplus_benchmark = NULL,
  ...
)

Arguments

models: A fitted ReSurvFit or a named list of models. At least one entry must be a ReSurvFit; other entries must support predictReserve().
newdata: Realized claims, either individual data with the accident and reporting columns used for fitting, or an aggregate table with AP, DP, CP, and counts in actual, I, or IBNR. Aggregate periods must be on the requested granularity scale.
scoring_metrics: Character vector selecting "EI", "R-tot", "R-cell-wise", "R-cal-wise", or "CRPS". CRPS is available only for ReSurvFit models.
granularity: Time scale for scoring: "output" (default) or "input".
chain_ladder: Logical; include the aggregate chain-ladder benchmark.
clmplus_benchmark: Optional character vector containing any of `"ac"` or `"apc"`. Requested models are fitted with the `clmplus` package to the same aggregate triangle as the chain-ladder benchmark. `NULL` (the default) disables these benchmarks.
...: Additional arguments passed to predictReserve().

Value

A list of score tables, one per requested metric, with class Score_Reserving and a granularity attribute.