Cross-Validation for Stagewise Variable Selection

Selects the optimal penalty parameter (lambda) along the stagewise path using K-fold cross-validation with cross-fitted estimating equations.

Usage

cv_stagewise(
  data,
  model = c("jfm", "jscm"),
  penalty = c("coop", "lasso", "group"),
  K = 5L,
  lambda_seq = NULL,
  eps = NULL,
  max_iter = NULL,
  pp = NULL,
  estimate_frailty = FALSE,
  ncores = 1L
)

Arguments

data

A data frame in recurrent-event format.

model

Character. Either "jfm" or "jscm".

penalty

Character. One of "coop", "lasso", or "group".

K

Integer. Number of cross-validation folds (default 5).

lambda_seq

Numeric vector. The lambda sequence at which to evaluate the cross-validation criterion. If NULL, extracted from a full-data stagewise fit.

eps

Numeric. Step size (passed to stagewise_fit).

max_iter

Integer. Maximum iterations (passed to stagewise_fit).

pp

Integer. Early-stop block size (passed to stagewise_fit).

estimate_frailty

Logical. For JFM only: if TRUE, estimates the frailty variance and uses frailty weights in the estimating equations (passed to stagewise_fit).

ncores

Integer. Number of cores for parallel fold training (default 1, sequential). Uses parallel::parLapply with a PSOCK cluster, which works on all platforms including Windows.

Value

An object of class "swjm_cv", a list with components:

position_CF

Integer, position of best lambda by combined cross-fitted score norm.

position_CF_re

Integer, position of best lambda by recurrence score norm.

position_CF_cen

Integer, position of best lambda by terminal score norm.

lambda_seq

Numeric vector of lambda values evaluated.

Scorenorm_crossfit

Combined cross-fitted score norm path.

Scorenorm_crossfit_re

Recurrence score norm path.

Scorenorm_crossfit_ce

Terminal score norm path.

full_fit

The full-data stagewise fit (class "swjm_path").

model

Character.

penalty

Character.

Examples

# \donttest{
dat <- generate_data(n = 50, p = 10, scenario = 1, model = "jfm")
fit <- stagewise_fit(dat$data, model = "jfm", penalty = "coop",
                     max_iter = 100)
cv_res <- cv_stagewise(dat$data, model = "jfm", penalty = "coop",
                       lambda_seq = fit$lambda, max_iter = 100)
cv_res
#> Cross-validation (jfm/coop)
#> 
#>   Covariates (p):              10
#>   Lambda grid size:            101
#>   Best position (combined):    101  (lambda = 2.008)
#>   Selected variables:          1 readmission, 1 death
#>     Readmission (alpha): 9
#>     Death (beta):        9
# }