mmltVS: Select optimal subset based on high dimensional BIC in mmlts

In tramvs: Optimal Subset Selection for Transformation Models

Select optimal subset based on high dimensional BIC in mmlts

Description

Select optimal subset based on high dimensional BIC in mmlts

Usage

mmltVS(
  mltargs,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  verbose = TRUE,
  parallel = FALSE,
  m0 = NULL,
  future_args = list(strategy = "multisession", workers = supp_max),
  ic_scale = 1,
  ...
)

Arguments

mltargs	Arguments passed to mmlt
supp_max	maximum support which to call abess_tram with.
k_max	maximum splicing size, i.e., number of covariates to swap between active and inactive set, to consider during the splicing algorithm. Defaults to supp.
thresh	threshold when to stop splicing. Defaults to 0.01 * supp * log(p) * log(log(n)) / n, where p denotes the number of predictors and n the sample size.
init	initialize active set. Defaults to TRUE and initializes the active set with those covariates that are most correlated with score residuals of an unconditional modFUN(update(formula, . ~ 1)). If FALSE, the initial active set is drawn at random.
m_max	maximum number of iterating the splicing algorithm.
verbose	show progress bar (default: TRUE)
parallel	toggle for parallel computing via future_lapply
m0	Transformation model for initialization
future_args	arguments passed to plan; defaults to a "multisession" with supp_max workers
ic_scale	non-negative value multiplying the penalty term in the SIC (default: 1). ic_scale = 0.5 corresponds to the information criterion used in the abess package.
...	Currently ignored

Details

L0-penalized (i.e., best subset selection) multivariate transformation models using the abess algorithm.

Value

object of class "mmltvs", containing the regularization path (information criterion SIC and coefficients coefs), the best fit (best_fit) and all other models (all_fits)

tramvs documentation built on Oct. 17, 2026, 5:07 p.m.