Fit a Stagewise Regularization Path

Unified interface for stagewise variable selection in joint models of recurrent events and terminal events. Dispatches to model-specific implementations for the Joint Frailty Model (JFM) or Joint Scale-Change Model (JSCM).

Usage

stagewise_fit(
  data,
  model = c("jfm", "jscm"),
  penalty = c("coop", "lasso", "group"),
  eps = NULL,
  max_iter = NULL,
  pp = NULL,
  estimate_frailty = FALSE
)

Arguments

data

A data frame in recurrent-event format with columns id, t.start, t.stop, event, status, and covariate columns x1, ..., xp.

model

Character. Either "jfm" or "jscm".

penalty

Character. One of "coop" (cooperative lasso), "lasso", or "group" (group lasso).

eps

Numeric. Step size for the stagewise update. If NULL, uses adaptive step size.

max_iter

Integer. Maximum number of stagewise iterations.

pp

Integer. Early-stopping block size: algorithm checks every pp iterations if fewer than 3 unique coordinates were updated.

estimate_frailty

Logical. For JFM only: if TRUE, estimates the frailty variance and uses the Kalbfleisch et al. (2013) frailty weights \(w_i(t)\) in the estimating equations. If FALSE (default), uses unit weights (simplified model without frailty).

Value

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

k

Number of iterations performed.

theta

Matrix of coefficient paths (2p rows by k+1 columns).

lambda

Numeric vector of penalty parameter approximations at each iteration.

norm

Numeric vector of penalty norm values along the path.

model

Character, the model used.

penalty

Character, the penalty used.

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)
fit
#> Stagewise path (jfm/coop)
#> 
#>   Covariates (p):            10
#>   Iterations:                100
#>   Lambda range:              [2, 2.009]
#>   Active at final step:      1 readmission, 1 death
#>     Readmission (alpha): 9
#>     Death (beta):        9
# }