CoxphVS: Optimal subset selection in a Coxph-type transformation model

In tramvs: Optimal Subset Selection for Transformation Models

Optimal subset selection in a Coxph-type transformation model

Description

Optimal subset selection in a Coxph-type transformation model

Usage

CoxphVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

formula	object of class "formula".
data	data frame containing the variables in the model.
supp_max	maximum support which to call abess_tram with.
k_max	maximum support size to consider during the splicing algorithm. Defaults to supp.
thresh	threshold when to stop splicing. Defaults to 0.01 * supp * 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)).
m_max	maximum number of iterating the splicing algorithm.
parallel	toggle for parallel computing via future_lapply
future_args	arguments passed to plan; defaults to a "multisession" with supp_max workers
...	Additional arguments supplied to Coxph

Value

See tramvs

tramvs documentation built on Sept. 11, 2024, 7:55 p.m.