Per-prediction risk and computed severity — tf

Computes, for each prediction (in file order), the riskiness of the claim form and the discounted/bonus-adjusted severity.

Usage

tf_severity(theory)

Arguments

theory: A theory object (named list), e.g. from tf_read().

Value

A data.frame with columns prediction_id, type, risk_score, computed_severity, one row per prediction in file order.

References

Mayo, D. G. (2018). Statistical inference as severe testing. Cambridge University Press. doi:10.1017/9781107286184

Meehl, P. E. (1990). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66, 195-244. doi:10.2466/pr0.1990.66.1.195

Examples

theory <- tf_theory("demo-1", "A demonstration theory") |>
  tf_add_prediction("h1", "Effect is exactly 0.30.", "point") |>
  tf_add_prediction("h2", "Effect is positive.", "directional")
tf_severity(theory)
#>   prediction_id        type risk_score computed_severity
#> 1            h1       point        0.9               0.9
#> 2            h2 directional        0.4               0.3