What you’ll learn
- How to compute
confidence_functionals()from asb_gamlss_c0_grid()result. - How to plot ranking summaries (
plot()) and raw stability trajectories (plot_stability_curves()). - How to customise weights, quantiles, and conservative adjustments for different decision rules.
- AUSC (Area Under the Stability Curve): normalized trapezoidal area over the grid.
- Thresholded AUSC: integrate the positive excess \((p - \pi^\star)_+\).
- Coverage: fraction of grid points with \(p \ge \pi^\star\).
- Quantiles: median and high quantiles (e.g.,
80th/90th) of \(p\) across
c0. - Weighted AUSC: integrate \(w(c0)\,p(c0)\) normalized by \(\int w(c0)\,dc0\).
- Conservative AUSC: replace \(p\) by a Wilson lower bound before integrating.
library(gamlss)
library(SelectBoost.gamlss)
set.seed(1)
n <- 400
x1 <- rnorm(n); x2 <- rnorm(n); x3 <- rnorm(n)
y <- gamlss.dist::rNO(n, mu = 1 + 1.3*x1 - 1.0*x3, sigma = 1)
dat <- data.frame(y, x1, x2, x3)
g <- sb_gamlss_c0_grid(
y ~ 1, data = dat, family = gamlss.dist::NO(),
mu_scope = ~ x1 + x2 + x3, sigma_scope = ~ x1 + x2,
c0_grid = seq(0.2, 0.8, by = 0.2), B = 40, pi_thr = 0.6, pre_standardize = TRUE, trace = FALSE
)
#> | | | 0% | |================== | 25% | |=================================== | 50%
#> Warning in RS(): Algorithm RS has not yet converged
#> | |==================================================== | 75% | |======================================================================| 100%
cf <- confidence_functionals(
g, pi_thr = 0.6, q = c(0.5, 0.8, 0.9),
weight_fun = NULL, conservative = FALSE, method = "trapezoid"
)
plot(cf, top = 10, label_top = 6)
