A pre-data rubric of the form of each prediction's claim. risk_score
is the base riskiness of the declared type: existence 0.1, directional 0.4,
interval 0.7 and point 0.9. computed_severity discounts a directional
prediction by 25 per cent, adds 0.1 when diagnostic_vs names a
registered alternative and is capped at 1. The order of the types follows
Popper (1959, sections 31-33) for nested claims about one quantity, and the
directional penalty follows Meehl's (1967, 1990b) argument that a sign alone
risks little. The base values, the discount and the bonus are the package's
conventions, and neither source gives them.
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.
Details
How severely a claim is tested depends on the design and the data, which the rubric does not read. A prediction scores the same however it is tested, and a failed test leaves the value where it was.
References
Meehl, P. E. (1967). Theory-testing in psychology and physics: A methodological paradox. Philosophy of Science, 34(2), 103-115. doi:10.1086/288135
Meehl, P. E. (1990b). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66(1), 195-244. doi:10.2466/pr0.1990.66.1.195
Popper, K. R. (1959). The logic of scientific discovery. Hutchinson.
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