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Measures the perturbation actually undergone by the table, and compares it with the theoretical expectation of Proposition 3 evaluated at each cell's own dominance. A close match is the natural consistency check: the mechanism is analytical, so the realised loss should track the predicted one.

Usage

assess_utility_empirical(x, by = NULL, thresholds = c(5, 10, 20))

Arguments

x

A table returned by pm_perturb().

by

Optional column name(s) to break the summary down by (e.g. a publication stratum, or a dominance band built beforehand).

thresholds

Relative deviations (in percent) whose exceedance rate is reported.

Value

A data.frame: number of cells, mean and median absolute relative deviation, quantiles, maximum, relative RMSE, exceedance rates, and the theoretical mean absolute loss averaged over the observed dominance.