For each cell, evaluates whether the attack of a given scenario would in fact
have succeeded on the realised perturbation – scenario I:
|Y' - X1| / X1 < beta; scenario II: |(Y' - X2) - X1| / X1 < beta – and
compares the observed success rate with the theoretical probability
mu(rho) averaged over the table. The two should agree: the realised rate is
one draw from the probability the mechanism guarantees.
Usage
assess_risk_empirical(x, scenario = c("I", "II"), beta = NULL, by = NULL)Arguments
- x
A table returned by
pm_perturb().- scenario
"I"(default) or"II".- beta
Accuracy threshold. Defaults to the matching policy of the parameter object.
- by
Optional column name(s) to break the summary down by.
Value
A data.frame with the number of cells, the observed success rate
(in percent), the mean theoretical risk, and the maximum theoretical risk.
Details
Scenario II requires the x2 column to have been declared in pm_perturb(),
which adds the rho2 share the theoretical measure needs.
The differencing scenario is not assessed here: it bears on pairs of cells,
not on single cells, and is controlled a priori by the upper bound of the
metric.
See also
dominoise-package paper's proposition 7
Examples
set.seed(123)
para <- suppressMessages(
pm_commit_dominance(pm_commit_diff(pm_params()), sigma_nu = 0.4, n = 4)
)
tab <- data.frame(turnover = runif(500, 100, 1000), ck = runif(500))
tab$x1 <- tab$turnover * runif(500, 0.2, 1)
tab$x2 <- (tab$turnover - tab$x1) * runif(500)
res <- pm_perturb(tab, "turnover", "x1", "ck", para, x2 = "x2")
assess_risk_empirical(res, scenario = "I")
#> scenario beta n_cells observed_pct theo_mean theo_max
#> 1 I 0.2 500 13.4 0.1360245 0.4822485
assess_risk_empirical(res, scenario = "II")
#> scenario beta n_cells observed_pct theo_mean theo_max
#> 1 II 0.1 500 15.4 0.1615296 0.8464624