abess_tram: Optimal subset selection for transformation models

In tramvs: Optimal Subset Selection for Transformation Models

Optimal subset selection for transformation models

Description

Optimal subset selection for transformation models

Usage

abess_tram(
  formula,
  data,
  modFUN,
  supp,
  mandatory = NULL,
  k_max = supp,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  m0 = NULL,
  ...
)

Arguments

formula	object of class "formula".
data	data frame containing the variables in the model.
modFUN	function for fitting a transformation model, e.g., BoxCox().
supp	support size of the coefficient vector
mandatory	formula of mandatory covariates, which will always be included and estimated in the model. Note that this also changes the intialization of the active set. The active set is then computed with regards to the model residuals of modFUN(mandatory, ...) instead of the unconditional model.
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.
m0	Transformation model for initialization
...	additional arguments supplied to modFUN.

Value

List containing the fitted model via modFUN, active set A and inactive set I.

Examples

set.seed(24101968)
library(tramvs)

N <- 1e2
P <- 5
nz <- 3
beta <- rep(c(1, 0), c(nz, P - nz))
X <- matrix(rnorm(N * P), nrow = N, ncol = P)
Y <- 1 + X %*% beta + rnorm(N)

dat <- data.frame(y = Y, x = X)

abess_tram(y ~ ., dat, modFUN = Lm, supp = 3)

tramvs documentation built on April 4, 2025, 6:13 a.m.