SelectBoost.gamlss (version 0.2.2)

SelectBoost.gamlss-package: SelectBoost.gamlss: Stability-Selection via Correlated Resampling for 'GAMLSS' Models

Description

Extends the 'SelectBoost' approach to Generalized Additive Models for Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC(). Includes optional standardization of predictors and helper functions for corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) https://hal.science/hal-05352041 that highlights correlation-aware resampling to improve variable selection for GAMLSS and quantile regression when predictors are numerous and highly correlated.

Author

This package was written by Frederic Bertrand. Maintainer: Frederic Bertrand

References

Bertrand, F. and Maumy, M. (2024). An Improvement for Variable Selection for Generalized Additive Models for Location, Shape and Scale and Quantile Regression. Joint Statistics Meetings (JSM) 2024, Portland, OR. Presented the SelectBoost approach for GAMLSS and quantile regression, emphasizing correlation-aware resampling to improve recall and precision when predictors are numerous and highly correlated.

See Also

Useful links:

• https://fbertran.github.io/SelectBoost.gamlss/

• https://github.com/fbertran/SelectBoost.gamlss

• Report bugs at https://github.com/fbertran/SelectBoost.gamlss/issues

Examples

if (FALSE) { # requireNamespace("gamlss.dist", quietly = TRUE)
set.seed(1)
dat <- data.frame(
  y = gamlss.dist::rNO(80, mu = 0),
  x1 = rnorm(80),
  x2 = rnorm(80)
)
fit <- SelectBoost_gamlss(
  y ~ 1,
  data = dat,
  family = gamlss.dist::NO(),
  mu_scope = ~ x1 + x2,
  B = 10,
  pi_thr = 0.6,
  trace = FALSE
)
fit$final_formula
}