Update the identification with new evaluations

Feeds one or more evaluations, each a vector of objective values for one alternative, to the confidence sequences. The running intersections are updated and the three sets are recomputed.

Usage

update_objectives(state, evaluations)

Arguments

state

A paretoinfer_state.

evaluations

Data frame with a column alternative (name as in the design) and one numeric column per objective; an optional cost column overrides the design cost for those rows.

Value

The updated state.

Examples

design <- pareto_design(alternatives = c("a", "b"), objectives = c("y1", "y2"),
                        bounds = c(0, 1), epsilon = 0.05)
evaluations <- tibble::tibble(alternative = c("a", "b", "a"),
                              y1 = c(0.2, 0.5, 0.25),
                              y2 = c(0.6, 0.3, 0.55))
state <- initialize_pareto(design) |>
  update_objectives(evaluations)
state
#> 
#> ── Pareto identification ───────────────────────────────────────────────────────
#> 2 alternatives, 2 objectives, alpha 0.05, epsilon 0.05, 0.05.
#> 3 evaluations, cost 3.
#> • Estimated frontier: "a" and "b"
#> • Plausible Pareto set: "a" and "b"
#> • Certified dominated: none
#> Running; 2 alternatives still uncertain.