Define a pass/fail cut under an explicit decision rule

The decision rule determines how rater severity can reach the decision:

`raw_total`: Pass if the summed observed ratings reach `value`. Severity passes straight through to the decision.
`measure`: Pass if the severity-adjusted Rasch measure (logits) reaches `value`. Severity is modeled out; only its effect on measurement precision remains.
`fair_average`: Pass if the FACETS-style fair average (expected mean rating per cell for an average rater) reaches `value`. It is monotone in the measure, so it behaves like `measure` with a transformed cut.

Usage

df_cut(value, decision_rule = c("raw_total", "fair_average", "measure"))

Arguments

value: The cut score on the scale implied by `decision_rule`.
decision_rule: One of `"raw_total"`, `"fair_average"`, `"measure"`.

Value

A `df_cut` object (a list with `value` and `decision_rule`).

Examples

df_cut(16, "raw_total")        # pass if the summed ratings reach 16
#> $value
#> [1] 16
#> 
#> $decision_rule
#> [1] "raw_total"
#> 
#> attr(,"class")
#> [1] "df_cut"
df_cut(2, "fair_average")      # pass if the fair average reaches 2
#> $value
#> [1] 2
#> 
#> $decision_rule
#> [1] "fair_average"
#> 
#> attr(,"class")
#> [1] "df_cut"
df_cut(0.25, "measure")        # pass if the Rasch measure reaches 0.25 logits
#> $value
#> [1] 0.25
#> 
#> $decision_rule
#> [1] "measure"
#> 
#> attr(,"class")
#> [1] "df_cut"