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Flag probabilities with an explicit unclassified state

Source: R/summaries.R
compute_flags.Rd

For each participant, the posterior probability that the classical flag rule fires on each signed factor, with the probability of remaining unclassified alongside. Selected flags come from all signed candidates by the posterior false-discovery rule at level q.

Usage

compute_flags(fit, q = 0.05, floor = 0.5)

Arguments

fit

A bayesqm_fit.

q

Posterior expected false-discovery bound (default 0.05).

floor

Minimum flag probability a candidate needs before it can be selected (default 0.5, so at most one signed flag per participant).

Value

A data frame with one row per participant: modal signed candidate (factor, sign), its probability flag_prob, unclassified_prob, and selected. The full probability matrix is attached as attr(, "phi") and the expected number of false selected flags as attr(, "expected_false").

Examples

compute_flags(demo_fit())
#>   participant factor sign flag_prob unclassified_prob selected
#> 1          P1     f1    1     0.840             0.120    FALSE
#> 2          P2     f2    1     0.795             0.195    FALSE
#> 3          P3     f1    1     0.875             0.120    FALSE
#> 4          P4     f2    1     0.750             0.250    FALSE
#> 5          P5     f1    1     0.895             0.105    FALSE
#> 6          P6     f2    1     0.325             0.520    FALSE
#> 7          P7     f1    1     0.785             0.195    FALSE
#> 8          P8     f2    1     0.330             0.645    FALSE