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Worst-case risk as a function of sigma_nu

Source: R/risk_utility_figures.R, R/suggest_n.R
pm_plot_risk_max.Rd

Reproduces the middle panel of Figure 3, generalised: the worst case over rho of the disclosure risk against sigma_nu, one curve per n, at a fixed sigma_eps. Panels split by scenario when several are present.

Reduces a calibration table to the worst case over rho and plots it against one of the two mechanism parameters.

Usage

pm_plot_risk_max(
  x,
  x_axis = c("sigma_nu", "n"),
  sigma_nu = NULL,
  n = NULL,
  beta = NULL,
  sigma_eps = NULL,
  scenario = NULL,
  tau = NULL,
  mark_frontier = TRUE,
  marks = NULL,
  thresholds = c(0.5, 0.8)
)

pm_plot_risk_max(
  x,
  x_axis = c("sigma_nu", "n"),
  sigma_nu = NULL,
  n = NULL,
  beta = NULL,
  sigma_eps = NULL,
  scenario = NULL,
  tau = NULL,
  mark_frontier = TRUE,
  marks = NULL,
  thresholds = c(0.5, 0.8)
)

Arguments

x

A calibration table (e.g. from pm_calib_dominance()).

x_axis

Parameter on the x-axis: "sigma_nu" (default) or "n".

sigma_nu, n, beta, sigma_eps, scenario

Optional values to keep.

tau

Risk ceiling, drawn as a dashed line. Defaults to thresholds.

mark_frontier

If TRUE and x_axis = "n", mark the largest admissible n of each sigma_nu with a point. Requires a single tau.

marks

Values of the x-axis parameter highlighted with a point; NULL for none.

thresholds

Risk levels drawn as dashed horizontal lines.

Value

A ggplot object.

A ggplot object.

Details

With x_axis = "sigma_nu" this is the middle panel of Figure 3: the risk decreases with sigma_nu, one curve per n.

With x_axis = "n" it is the view that accompanies pm_suggest_n(): the risk increases monotonically with n, one curve per sigma_nu. Each crossing of the ceiling tau is the n_max of its sigma_nu – the very root that pm_suggest_n() solves for. Set mark_frontier = TRUE to overlay those points, computed by the same function so the plot and the table always agree.

Examples

grid <- pm_calib_dominance(sigma_nu = seq(0.01, 0.5, 0.005), n = c(3, 6, 9, 12))
pm_plot_risk_max(grid, beta = 0.2)
#> Warning: mark_frontier applies to x_axis = 'n' only; ignored.

grid <- pm_calib_dominance(sigma_eps = 0.031, beta = 0.2,
                           sigma_nu = c(0.3, 0.4, 0.5), n = seq(1, 12, 0.2))
pm_plot_risk_max(grid, x_axis = "n", tau = 0.5)